JP2020077256A - Anonymization system and anonymization method - Google Patents

Abstract

To provide an anonymization system and an anonymization method which can verify correctness of anonymization processing for data even after carrying out anonymization processing such as deletion and substitution.SOLUTION: An anonymization data providing server carries out patient data record expansion processing S01 to store the processing result on an extended patient data table. The server subsequently carries out signature generating processing S02 for generating digital signature by using data of the extended patient data table as an input. The server allows an anonymization data user terminal to acquire S05 the generated signature value together with an original patient data name through a Web server registration S03. The anonymization data providing server which received an anonymization data acquisition request S06 carries out the verifiable anonymization processing S07 with the use of the patient data name and anonymization conditions as an input to generate anonymization data, and allows the anonymization data user terminal to acquire the same S08. The anonymization data user terminal carries out signature verification processing S09 with the use of the anonymization data and the signature value to verify validity of the anonymization data.SELECTED DRAWING: Figure 10

Description

  The present invention relates to an anonymization system and anonymization method, detects tampering when providing personal information such as medical information to be used for anonymizing and utilizing it in medical research, and the validity of the provided information. The present invention relates to an anonymization system and anonymization method suitable for verifying.

  In recent years, with the full enforcement of the revised Personal Information Protection Law in May 2017, the use and utilization of anonymously processed information, which is premised on the proper protection of personal information, is progressing. In addition, in May 2018, the Next-Generation Medical Infrastructure Act was enacted, which established a mechanism that anonymously processes medical information of the people and enables it to be used in research and development of universities and pharmaceutical companies. Due to such regulations, anonymized data can be used for research and development in the medical field.

  In the medical field, the validity of medical data used for research is called “validation”. In the future, it is considered that the verification of the same legitimacy will be an issue for anonymous processing.

  A technique for anonymizing and providing personal information such as clinical information is disclosed in Patent Document 1, for example. According to the information management system described in Patent Document 1, after the subject information (personal information) such as clinical information has been anonymized, the owner of the subject information and the viewing authority owner have anonymized information. It is possible to specify the information accumulated in association with.

International Publication No. 2008/069011

  In Patent Document 1 of the above-mentioned related art, since the target of the validity guarantee is a part of the original data (a combination of identifiers or quasi-identifiers), the validity of the anonymization cannot be guaranteed for the entire data.

  Generally, there is a digital signature technique as a technique for guaranteeing the validity of data. However, the legitimacy cannot be verified by simply applying a digital signature and performing anonymization processing such as deletion or replacement of data. If unauthorized anonymized data is used, there is a risk that research results will be unauthorized.

  An object of the present invention is to provide an anonymization system and an anonymization method capable of verifying the anonymization correctness of data that can be verified even after the anonymization process such as deletion or replacement. Especially.

  The configuration of the anonymization system of the present invention is preferably an anonymization system in which confidential information is anonymized by an information processing device and provided to a user, and the confidential information storage means stores the confidential information and the confidential information is abstracted. Abstraction candidate group information storage means that is a candidate group of information to be converted, extended secret data storage means that stores extended secret data in which a candidate group of information to be abstracted is added to secret information, and some information from the secret information The anonymized data storage means for storing the anonymized data with deleted or replaced, the extended secret data generation means for generating the extended secret data by using the secret information and the abstraction candidate group information, and the extended secret data or the anonymized data. Signature generation means for generating a digital signature using the hash value of secret information as an intermediate value, anonymization means for generating anonymization data using extended secret data, and verification of the validity of given anonymization data And an anonymized data validity verification means, wherein the extended secret data generation means is an information candidate for abstracting the secret information stored by the secret information storage means and the secret information stored by the abstraction candidate group information storage means. By referring to the group, the extended secret data is generated, and the anonymization means performs a process of replacing the extended secret data with the same hash value as the intermediate value of the signature generation of the signature generation means to generate the anonymized data. The signature generation means generates a first signature value from the extended secret data generated by the extended secret data generation means, and a second signature value from the anonymization data generated by the anonymization means. The anonymized data validity verification means is configured to verify the validity of the given anonymized data by comparing the first signature value and the second signature value.

  According to the present invention, there is provided an anonymization system and anonymization method capable of verifying the correctness of anonymization processing that can verify the correctness of anonymization processing for data even after performing anonymization processing such as deletion or replacement. be able to.

It is a whole block diagram of an anonymization system. It is a functional block diagram of an anonymization data provision server. It is a functional block diagram of an anonymization data user terminal. It is a hardware / software block diagram of an anonymization data providing server and an anonymization data user terminal. It is a figure which shows an example of the data structure of a patient data table. It is a figure which shows an example of the data structure of an abstraction pattern group. It is a figure which shows an example of the data structure of an extended patient data table. It is a figure which shows an example of the data structure of a signature data table. It is a figure which shows an example of the data structure of anonymization data. It is a sequence diagram which shows a series of processes until a user acquires anonymized data from an anonymized data providing server and verifies it. It is a flowchart which shows a patient data record expansion process. It is a flow chart which shows signature generation processing (the 1). It is a flow chart which shows signature generation processing (the 2). It is a flowchart which shows a verifiable anonymization process. It is a flow chart which shows record deletion processing. It is a flowchart which shows an attribute deletion process. It is a flowchart which shows an attribute replacement process. It is a flowchart which shows a signature verification process.

An embodiment of the present invention will be described below with reference to FIGS. 1 to 17.
In the present embodiment, an example will be described in which a hospital or the like anonymizes medical information of a patient and provides it for use in medical research, statistical data, and the like.
First, the configuration of the anonymization system will be described with reference to FIGS. 1 to 4.

First, the overall configuration of the anonymization system will be described with reference to FIG.
The anonymized data providing system is a system in which a data owner (data holder) holding information including personal information anonymizes the information and provides it to the data user. As shown in FIG. 1, the anonymized data providing system includes an anonymized data providing server 1 and an anonymized data user terminal 2, which are connected by a network 3.

  The anonymized data providing server 1 is a server that provides a function in which a data holder stores personal information and performs an providing anonymization process. The anonymized data user terminal 2 is a client terminal where a data user downloads anonymized data and verifies the validity. The network 3 may be a global network such as the Internet or a LAN (Local Network) installed in the premises.

Next, the functional configuration of the anonymized data providing server will be described with reference to FIG.
As shown in FIG. 2, the anonymized data providing server 1 has a Web server function unit 101, a record extension function unit 102, an anonymization processing function unit 103, a random number generation unit 104, a hash value generation unit 105, and a signature generation unit 106. , Storage unit 110.

  The Web server function unit 101 is a function unit that performs processing of disclosing the patient data name and the digital signature value to the data user on the Web page. The record expansion function unit 102 is a function unit that performs preprocessing for expanding patient data prior to anonymization processing. The anonymization processing function unit 103 is a function unit that performs verifiable anonymization processing. The random number generation unit 104 is a functional unit that generates a random number that is added to enhance the security of hash values. The hash value generation unit 105 is a functional unit that generates a hash value using a one-way function or the like. The signature generation unit 106 is a functional unit that inputs the extended patient data and generates a digital signature. The storage unit 110 is a functional unit that stores data used in the anonymized data providing server 1.

  The storage unit 110 stores a patient data table 301, an abstract pattern group 302, an extended patient data table 303, a signature data table 304, and anonymized data 305.

The patient data table 301 is a table that stores patient data including personal information. The abstraction pattern group 302 is data of a pattern group of replacement processing in anonymization of patient data. The extended patient data table 303 is a table that stores the patient data that has undergone the extended process as a preprocess of the anonymization process. The signature data table 304 is a table that stores a pair of a patient data name and a digital signature value of extended patient data of the patient data. Anonymization data 305 is data obtained by anonymizing patient data.
The specific structure of the data will be described in detail later.

Next, the functional configuration of the anonymized data user terminal will be described with reference to FIG.
As shown in FIG. 3, the anonymized data user terminal 2 includes a browser function unit 201, a signature verification processing unit 202, a hash value generation unit 203, a signature generation unit 204, and a storage unit 210. The web browser function unit 201 is a function unit that performs a process of referring to a web page. The signature verification processing unit 202 is a functional unit that compares the signature value acquired from the Web page with the signature value of the received anonymization data to verify the validity of the anonymization. The hash value generation unit 203 is a functional unit that generates a hash value using a one-way function or the like. The signature generation unit 204 has a function equivalent to that of the signature generation unit 106 of the anonymized data providing server 1, and is a functional unit that inputs the anonymized data and generates a digital signature. The storage unit 210 is a functional unit that stores data used in the anonymized data user terminal 2.

  Anonymized data 305 and a signature value 311 are stored in the storage unit 210. Anonymized data 305 is data obtained by anonymizing patient data, and is data received from the anonymized data providing server 1 via the network 3. The signature value 311 is a digital signature value for verification acquired from the Web page and a digital signature value generated from the anonymized data 305.

Next, the hardware configuration and software configuration of the anonymized data providing server and the anonymized data user terminal will be described with reference to FIG.
The hardware configuration of the anonymized data providing server 1 is realized by a general information processing device such as the server device shown in FIG. 4, for example. It may also be a virtual machine constructed on a real computer.

  The anonymized data providing server 1 has a form in which a CPU (Central Processing Unit) 401, a main memory 402, a network interface 403, a display device 410, and an input device 420 are connected by a bus.

  The CPU 401 controls each unit of the anonymized data providing server 1, loads a necessary program into the main memory 402, and executes it.

  The main memory 402 is usually composed of a volatile memory such as a RAM, and stores programs executed by the CPU 401 and data to be referred to.

  The network interface 403 is an interface for connecting to the network 3.

  The display device 410 is a device that displays information such as an LCD (Liquid Crystal Display).

  The input device 420 is a device for inputting information such as commands and data and for inputting to control the device, and is, for example, a keyboard or a mouse of a pointing device.

  A hard disk drive (HDD) 430 has a large storage capacity and stores a program for executing the present embodiment. The Web server function program 601, the record expansion function program 602, the anonymization processing function program 603, the random number generation program 604, the hash value generation program 605, and the signature generation program 606 are installed in the hard disk drive 430 of the anonymized data providing server 1. ing. The Web server function program 601, the record expansion function program 602, the anonymization processing function program 603, the random number generation program 604, the hash value generation program 605, and the signature generation program 606 are respectively the Web server function unit 101, the record expansion function unit 102, It is a program that executes the functions of the anonymization processing function unit 103, the random number generation unit 104, the hash value generation unit 105, and the signature generation unit 106.

  Further, the hard disk drive 430 stores a patient data table 301, an abstracted pattern group 302, an extended patient data table 303, a signature data table 304, and anonymized data 305.

  The hardware configuration of the anonymized data user terminal 2 is realized by, for example, a general information processing device such as the personal computer shown in FIG. Further, it may be a smartphone or a dedicated terminal.

  Each unit of the hardware configuration of the anonymized data user terminal 2 is the same as that of the anonymized data providing server 1.

  A browser function program 701, a signature verification processing program 702, a hash value generation program 703, and a signature generation program 704 are installed in the hard disk drive 530 of the anonymized data user terminal 2. The browser function program 701, the signature verification processing program 702, the hash value generation program 703, and the signature generation program 704 execute the functions of the browser function unit 201, the signature verification processing unit 202, the hash value generation unit 203, and the signature generation unit 204, respectively. It is a program to do.

  Further, the hard disk drive 530 stores anonymized data 305 and a signature value 311.

Next, a data structure used in the anonymization system will be described with reference to FIGS.
The patient data table 301 is a table that stores personal information of a patient and related data, and as shown in FIG. 5, is composed of “name”, “address (prefecture name)”, and “sex”. For example, the record 3011 indicates that the name of a certain patient is “Hitachi Taro”, the address is “Tokyo”, and the gender is “male”.

  The abstract pattern group 302 is data used when abstracting the attributes represented by the columns of the patient data table 301, and as shown in FIG. 6A, for example, has a tree structure of a generalized hierarchical tree 302a. Expressed in data.

  In the generalized hierarchical tree 302a, the leaf node of the tree structure has the lowest abstraction value, the root node has the highest abstraction value, and the intermediate nodes start from nodes close to the leaf node. A value having a higher degree of abstraction is arranged as the node gets closer to the root node. For example, in the generalized hierarchical tree 302a of the attribute “address” shown in FIG. 6A, the leaf nodes are “prefecture names” such as “Tokyo” and “Kanagawa prefecture”, and the intermediate nodes are more abstract. This shows a generalized hierarchical tree in which the "region name" is high such as "Kanto region" and "Kinki region", and the root node is the "country name" of "Japan" which has the highest degree of abstraction among these.

  The abstract pattern group 302 may be expressed in the format of the abstract pattern table 302b shown in FIG. For example, the abstract pattern table 302b of the attribute “address” is represented by data having a table structure including columns of “address” and “abstract pattern”. In the abstraction pattern table 302b, for each "prefecture name" that is currently the value of the "address" column, the values of "regional name" and "country name" that are candidates for replacement in anonymization processing with a higher degree of abstraction. Correspond to. For example, in the abstract pattern of the value "Tokyo" of the "address" of the abstract pattern table 302b, "Kanto region" indicating "region name" and "Japan" indicating "country name" are anonymized with a higher degree of abstraction. It indicates that the candidate is a replacement target in the process.

  The extended patient data table 303 is a table in which the patient data of the patient data table 301 has been subjected to preprocessing for realizing verifiable anonymization processing. The extended patient data table 303 is a hash that adds each value of the abstract pattern group 302 that is a replacement candidate when anonymization by replacement is performed to patient data, and that is executed as an alternative process of anonymization process of deletion or replacement. It has a data structure with a random number added to improve the security of computerization. For example, in the example shown in FIG. 7, values of “address 3” and “address 4” which are abstract patterns of the attribute “address” are added to the patient data table 301, and further, for each attribute. Random number values of “name 1”, “address 1”, and “sex 1”, which are random numbers for enhancing the security of hashing, are added.

  By using a data structure that adds a random number for each attribute and an algorithm that generates a hash value step by step for each attribute that will be described later, while reducing the data size compared to when assigning random numbers to all elements, The increased security of hashing can be applied to the entire data.

  The signature data table 304 is a table holding the signature value for the patient data, and as shown in FIG. 8, is composed of “target data” and “signature value”. The “target data” is a column that stores the patient data name, and the “signature value” is a column that stores the signature value generated by the signature generation process for each patient data.

  In the present embodiment, the data of the signature data table 304 is read by the Web server function unit 101 of the anonymized data providing server 1 and is used as a Web page from the anonymized data user terminal 2 via the network 3 by a Web browser or the like. You can browse and download.

Anonymization data 305 is data obtained by executing anonymization processing on the extended patient data table 303. For example, FIG. 9 shows an example of the result of executing the anonymization process on the data of the extended patient data table 303 shown in FIG. 7. For example, the deleted record 3051 indicates a label “Delete_R” indicating that the record is a deleted record, a record hash value “22891F” generated by hashing executed as an alternative process of deletion, and an empty value. It is composed of "-" (Null). In addition, for example, the attribute deleted and replaced record 3052 includes a label “Delete_A”, which is a label indicating that the attribute “name” has been deleted, and an attribute hash generated by hashing executed as an alternative process of attribute deletion. The value “465FC4” is composed of “Replace” indicating that the attribute “address” has been replaced, and the attribute hash value “B0D8C7” generated by hashing executed as an alternative process of the attribute replacement.
Details of each process of record deletion, attribute deletion, and attribute replacement will be described later.

  These hash values are intermediate processing values in the signature generation processing, and this data structure can reduce the data processing amount of the signature generation processing in the signature verification processing of the anonymized data user terminal 2. It can speed up.

  Next, processing of the anonymization system will be described with reference to FIGS. 10 to 17.

First, a series of processes until the user acquires anonymized data from the anonymized data providing server and verifies the anonymized data will be described with reference to FIG. 10.
First, the record expansion function unit 102 of the anonymized data providing server 1 receives the patient data table 301 and the abstract pattern group 302 as input, performs patient data record expansion processing as preprocessing of anonymization processing, and expands the patient data table 303. The processing result is stored in (S01). The patient data record expansion process will be described later in detail with reference to FIG.

  Next, the signature generation unit 106 of the anonymized data providing server 1 receives the data of the extended patient data table 303 as input, performs a signature generation process of generating a digital signature, and uses the generated signature value as the source of the patient data name. It is also stored in the signature data table 304 (S02). The signature generation process will be described later in detail with reference to FIGS. 12A and 12B.

  Next, the Web server function unit 101 of the anonymized data providing server 1 reads the patient data name and the signature value stored in the signature data table 304, generates a Web page accessible from the network 3, and outputs the URL of the Web page. The (Uniform Resource Locator) is notified to the web browser function of the anonymized data user terminal 2 (S03).

  Next, the web browser function unit 201 of the anonymized data user terminal 2 acquires a web page including the patient data name and the signature value, and displays a list of the patient data name and the signature value on a display device such as a display for the data user. It is displayed by 510 (S06).

  Next, the data user of the anonymized data operates the input device 520 such as the mouse of the anonymized data user terminal 2 to download the signature value corresponding to the patient data name of the patient data to be used from the Web page. The signature value 311 for verification is stored in the main memory 502 or the hard disk drive 530 of the anonymized data user terminal 2 (S05).

  Next, the data user inputs the patient data name to be used and the anonymization condition to the Web browser function unit 201 of the anonymized data user terminal 2. In this embodiment, the following three anonymization conditions are input.

Anonymization condition 1: Delete records where attribute "address" is other than "Japan" Anonymization condition 2: Delete attribute "name" Anonymization condition 3: Change attribute "address" from "prefecture name" to "regional name" Replacement

  Then, the Web browser function unit 201 notifies the Web server function unit 101 of the anonymized data providing server 1 of the anonymized data acquisition request including the patient data name and the anonymization condition (S06).

  Next, the Web server function unit 101 of the anonymization data providing server 1 transmits the notified patient data name and anonymization condition to the anonymization processing function unit 103. The anonymization process function unit 103 executes the verifiable anonymization process by inputting the patient data name and the anonymization condition, generates the anonymization data 305, and transmits the anonymization data 305 to the Web server function unit 101. Then, the Web server function unit 101 registers the anonymized data 305 in the download Web page for data users, and notifies the URL to the Web browser function of the anonymized data user terminal 2 (S07). The details of the verifiable anonymization process will be described later with reference to FIG.

  Next, the web browser function unit 201 of the anonymized data user terminal 2 accesses the download web page for the data user by inputting the notified URL, acquires the anonymized data by downloading, and acquires the anonymized data. The anonymized data 305 is stored in the main memory 502 or the hard disk drive 530 of the user terminal 2 (S08).

  Finally, the signature verification processing unit 202 of the anonymized data user terminal 2 receives the anonymized data 305 and the verification signature value 311 and executes a signature verification process for verifying the anonymity of the anonymization to obtain the validity. Is verified, and if it is not valid, “NG” is displayed to the data user by the display device 510 such as the display of the anonymized data user terminal 2 (S09). The details of the signature verification process will be described later with reference to FIG.

  Through the series of processes until the above user acquires anonymized data from the anonymized data providing server and verifies it, the data user determines whether the obtained anonymized data has been anonymized by a valid anonymization process. Can be confirmed. Thereby, for example, in the medical field, since it is possible to verify the anonymity of the anonymization data of the patient, the subject, etc. used in the research, it is possible to avoid the situation where the research result is erroneous due to the illegal anonymization data.

Next, the patient data record expansion process will be described with reference to FIG.
This is a process corresponding to S01 of FIG.

  First, the record expansion function unit 102 of the anonymized data providing server 1 counts the number of records in the patient data table and sets it as the number of records N (S101). The initial value of the variable m (m is a record counter) used in the processing shown in FIG. 11 is 1.

  Next, the record expansion function part 102 reads one record of the patient data table (S102).

  For example, when the read record is the record 3011 shown in FIG. 5, the element having the attribute “name” is “Hitachi Taro”, the element having the attribute “address” is “Tokyo”, and the element having the attribute “gender” is “gender”. Become a man ".

  Next, the record expansion function unit 102 reads the abstract pattern group of the read record from the abstract pattern group 302 (S103). For example, when the abstract pattern of “Tokyo” of the attribute “address” of the record is read from the abstract pattern table 302b of the abstract pattern group of the attribute “address” shown in FIG. 6B, the "Kanto region" and "Japan" of the record 3021 in which the element of the attribute "address" is "Tokyo" is read.

  Next, the record extension function unit 102 adds each element of the acquired abstraction pattern to the record (S104). For example, when the record 3011 in FIG. 5 is the relevant record, the elements “Kanto region” and “Japan” that are read from the record 3021 of the abstraction pattern in S103 are added. As a result, a record including the candidate for the value of the replacement destination at the time of anonymization by replacement is generated.

  Next, the record extension function unit 102 adds one random number acquired from the random number generation unit 104 to each attribute of the record (S105). For example, when the record 3011 in FIG. 5 is the record, three different random numbers are acquired for three attributes and added to the record. By adding one random number to each attribute in this way, the data size of a record can be made smaller than in the case where random numbers are added to all elements (columns).

  Next, the record expansion function unit 102 outputs the record in which the attribute is replaced and the random number is added, as a record of the expanded patient data table 303 (S106). For example, when the record 3011 in FIG. 5 is the record, the record generated in S103 to S105 is the value of the attribute “name 1” indicating the random number added to the attribute “name”, as indicated by 3031 in FIG. Is "5EF4BE", the value of the attribute "Name 2" indicating the original value of the attribute "Name" is "Hitachi Taro", and the value of the attribute "Address 1" indicating the random number added to the attribute "Address" is "A754B9" , The value of the attribute “address 2” indicating the original value of the attribute “address” is “Tokyo”, and the value of the attribute “address 3” indicating the first element of the abstraction pattern added to the attribute “address” is "Kanto region", the attribute "address 4" showing the second element of the abstract pattern added to the attribute "address" has the value "Japan", and the attribute "gender 1" showing a random number added to the attribute "gender 1" "770E67" and the value of the attribute "sex 2" indicating the original value of the attribute "sex" is "male".

  Next, the record extension function unit 102 increments the value of the variable m by 1 (S107).

  Finally, the record expansion function unit 102 compares the value of the variable m with the number N of records of the patient data, and when m is N or less (S108: No), the processes of S102 to S107 are executed, and m is If it is larger than N (S108: Yes), the process ends.

Next, the signature verification process will be described with reference to FIGS. 12A and 12B.
This is a process corresponding to S02 and S09 in FIG. 10, and is a common process performed by the signature generation unit 106 of the anonymized data providing server 1 and the signature generation unit 204 of the anonymized data user terminal 2. is there. In the present embodiment, the signature generation unit 106 of the anonymized data providing server 1 inputs the extended patient data and outputs a digital signature value, while the signature generation unit 204 of the anonymized data user terminal 2 anonymizes. Outputs a digital signature value with data as input.

  In the following, the signature verification process is performed by the signature generation unit 106 of the anonymized data providing server 1, but the signature verification process by the signature generation unit 204 of the anonymized data user terminal 2 is also the same.

  First, the signature generation unit 106 acquires the number of records N and the number of attributes n from the target data (S201). The initial values of the variables i, j, t, q, k, l, m used in this flowchart are all 1.

  Next, the signature generation unit 106 reads one record from the target data (S202). For example, when the target data is the expanded patient data table 303 in FIG. 7, the record to be read is the record 3031. If the target data is the anonymized data 305 in FIG. 9, the target record to be read is the record 3052.

  Next, if the first element of the read record is “Delete_R” indicating that the record is deleted (S203: Yes), the signature generation unit 106 executes the process of S204. Otherwise (S203: No), the process of S205 is executed (S203). For example, when the record is the record 3051 of the anonymized data 305, the first element of the record is “Delete_R”, and thus the process of S204 is executed. On the other hand, when the record is the record 3031 of the expanded patient data table 303, the first element of the record is “5EF4BE”, and therefore the process of S205 is executed next.

  Next, the signature generation unit 106 sets the value of the second element of the record as the hash value Hri of the i-th (i is an attribute counter) record, and then executes the process of S215 (S204). Then, the process of S216 is executed. For example, when the record is the record 3051 of the anonymized data 305, the value of the second element “22891F” is set as the value of Hri.

  Next, the signature generation unit 106 acquires the number of elements of the i-th attribute Ai of the record and sets it as Ein (S205). For example, when the first attribute “name” of the record 3031 of the extended patient data table 303 is A1, it is composed of two extended attributes “name 1” and “name 2”, so the value of Ein is “2”. "It becomes.

  Next, when the first element of the attribute Ai is “Delete_A” (S206: “Delete_A”), the signature generation unit 106 executes the process of S207, and when it is “Replace”. (S206: “Replace”), next executes the process of S208, and if neither (S206: Other), next executes the process of S209 (S206). For example, when the record is the record 3052 of the anonymized data 305 and the attribute Ai is “name”, the first element is “Delete_A” of the attribute “name 1”, and thus the process of S207 is executed next. . Further, for example, when the record is the record 3052 of the anonymized data 305 and the attribute Ai is “address”, the first element is “Replace” of the attribute “address 1”, and therefore the process of S208 is executed next. To do. Further, for example, when the record is 3031 of the extended patient data table 303 and the attribute Ai is “name”, the first element is “5EF4BE” of the attribute “name 1”, so the process of S209 is executed next. To do.

  Next, the signature generation unit 106 sets the value of the second element of the attribute Ai as the value of the hash value Hri (S207), and then executes the process of S216. For example, if the record is a record 3052 of the anonymized data 305 and the attribute Ai is “name”, the value of the attribute “name2” of the second element “465FC4” is set as the value of Hri.

  Next, the signature generation unit 106 sets the value of the second element of the attribute Ai as the hash value Hri, and then executes the processing of S210 (S208). For example, when the record is a record 3052 of the anonymized data 305 and the attribute Ai is “address”, “B0D8C7” which is the value of the attribute “address 2” of the second element is set as the value of Hri.

  Next, the signature generation unit 106 increments the value of the variable j (j is an element counter) by 1 (S210).

  Next, the signature generation unit 106 receives the j-th element Aij and the (j + 1) -th element Ai (j + 1) of the attribute Ai as input, acquires the hash value from the hash value generation unit 105, and obtains the hash value of the record. Hri (S209). For example, if the record is a record 3031 of the extended patient data table 303, and the attribute Ai is “name”, i = 1, j = 1, the j-th element of the attribute Ai is the value of the attribute “name 1” “ 5EF4BE ", the (j + 1) th element becomes the value" Hitachi Taro "of the attribute" name 2 ", and the hash value" A8E0C2 "obtained by inputting these two values to the hash value generation unit 105 is the value of Hri. ..

  Next, if the value of the j-th element of the attribute Ai is "-" (Null) (S212: Yes), the signature generation unit 106 next executes the process of S213, and otherwise. If (S212: No), then the process of S212 is executed. For example, if the record is the record 3051 of the anonymized data 305, the attribute Ai is “address”, and the jth element is the value “−” of the attribute “address 1”, then the process of S213 is executed.

  Next, the signature generation unit 106 increments the value of the variable j by 1, and then executes the process of S213 (S212).

  Next, the signature generation unit 106 compares the number of elements Ein of the attribute Ai with the value of the variable j, and if Ein is larger than j (S213: Yes), next executes the process of S216, and Ein is If j or less (S213: No), the process of S214 is executed next.

  Next, the signature generation unit 106 receives the j-th element Aij of the attribute Ai and the hash value Hri of the record at that time as an input, acquires a new hash value from the hash value generation unit 105, and acquires a new Hri. (S214). For example, the record is a record 3031 of the extended patient data table 303, the attribute Ai is “address”, j = 3, and the third element of the attribute Ai is the value of the attribute “address 3”, “Kanto region”, When the Hri value at that time is “B7EF14”, “7C648B” obtained by inputting “Kanto region” and “B7EF14” into the hash value generation unit 105 is set as a new Hri value.

  Next, the signature generation unit 106 increments the value of the variable j by 1, and then executes the process of S211 (S215).

  Next, the signature generation unit 106 increments the value of the variable i by 1 in S215, and then executes the process of S217 (S216).

  Next, the signature generation unit 106 compares the number of attributes n with the variable i. When i is n or less (S217: Yes), the process of S203 is executed next, and when i is larger than n (S217). : No), the process of S218 is executed next.

  Next, n is compared with q (q is an attribute counter), and when n is larger than q (S218: Yes), S219 is executed next, and when n is equal to or smaller than q (S218: No). Then, S221 is executed next.

  Next, the signature generation unit 106 inputs the hash value Hrq and the hash value Hr (q + 1), generates a hash value, and sets it as a new Hr (q + 1) (S219).

  Next, q is incremented by 1 (S220), and the process returns to S218.

  Next, the signature generation unit 106 sets the hash value HRt (t is a record counter) to the hash value Hrn (note that this is equal to the value of Hr (q + 1) when n ≧ 2). Substitute (S221).

  Next, the signature generation unit 106 increments the value of the variable t by 1, sets the values of the variable i and the variable j to 1, and then executes the process of S218 (S217).

  Next, the signature generation unit 106 compares the number N of records of the target data with the value of the variable t, and when t is N or less (S223: No), next executes the processes of S202 to S222, and t If is larger than N (S223: Yes), then the process of S251 of FIG. 12B is executed.

  The subsequent process is a process of generating a root hash value from a hash tree having a binary tree structure composed of record hash values.

  Next, the signature generation unit 106 sets the value of m (m is a counter of the number of remaining items of the record processing) as the number of remaining items of the record hash value HRi as the number of records N of the target data, and then performs the processing of S252. It is executed (S251 in FIG. 12B).

  Next, the signature generation unit 106 compares the number of remaining items m with the value of the variable l, and when they are not equal (S252: No), the process of S253 is executed next, and when they are equal (S252: Yes). Next executes the processing of S260.

  Next, in step S <b> 253, the signature generation unit 106 receives the hash value HRk and the (k + 1) th hash value HR (k + 1) of the kth (k is a record counter) record from the hash value generation unit 105. A new hash value is acquired and set as a new value of the 1st (l is a record counter) record hash value HRl (S253). For example, when k = 1 and l = 1, the hash value newly acquired from the hash value generation unit 105 with HR1 and HR2 as input is set as the new value of HR1.

  Next, if the value of the remaining item number m is other than 2 (S254: No), the signature generation unit 106 next executes the process of S255, and if the value of the remaining item number m is 2 (S254). : Yes), the process of S260 is executed next.

  Next, the signature generation unit 106 sets 2k + 1 as a new value of k and increments the value of l by 1 (S255).

  Next, the signature generation unit 106 compares the number of remaining items m with the value of the variable k, and when m is larger than k (S256: Yes), next executes the process of S253, where m is less than or equal to k. In the case of (S256: No), the process of S257 is then executed.

  Next, the signature generation unit 106 compares the number of remaining items m with the value of the variable k, and when m and k are different (S257: No), next executes the process of S258, and m and k are equal. In the case (S257: Yes), the process of S259 is executed next.

  The signature generation unit 106 sets the value of (m / 2 + m% 2) as the value of the new remaining item number m, sets the values of k and l to 1, and then executes the processing of S253 (S258). Here, m% 2 represents a remainder obtained by dividing m by 2.

  Next, the signature generation unit 106 substitutes the value of Hrk as a new value of Hrl (S259).

  Next, the signature generation unit 106 sets the value of Hrl at that time as the value of the entire hash value Hd of the target data (S260).

  Finally, the signature generation unit 106 generates and outputs the signature value δ from Hd by a digital signature algorithm such as RSA (S261), and ends the processing. An existing algorithm can be used as the digital signature algorithm.

  With respect to the anonymized data that has been subjected to the verifiable anonymization process described below with reference to FIGS. 13 to 16 by the signature generation process described above, even after the anonymization process, the extended patient before anonymization When the data is the same, the signature value generated for the extended patient data and the signature value generated for the anonymized data are the same, so the validity of the anonymization can be verified. Becomes

Next, the verifiable anonymization process will be described with reference to FIG.
This is a process corresponding to S07 of FIG.

  First, the anonymization processing function unit 103 of the anonymized data providing server 1 reads out the extended patient data corresponding to the patient data name transmitted from the Web server function unit 101 from the extended patient data table 303 (S301). For example, in S06 of FIG. 10, the data of the extended patient data table 303 of FIG. 7 is acquired as the corresponding extended patient data.

  Next, the anonymization processing function unit 103 reads the anonymization condition transmitted from the Web server function unit 101 (S302). For example, in this embodiment, the following three anonymization conditions are read.

Anonymization condition 1: Delete records where attribute "address" is other than "Japan" Anonymization condition 2: Delete attribute "name" Anonymization condition 3: Change attribute "address" from "prefecture name" to "regional name" Replacement

  Next, if there is an anonymization condition for deleting a record, the anonymization processing function unit 103 does not simply delete the record, but performs hashing of the entire target record for the extended patient data. Is executed (S303). For example, in the case of the present embodiment, since the anonymization condition 1 is an anonymization condition for deleting a record, the record deletion process is executed on the extended patient data read from the extended patient data table 303. The details of the record deletion process will be described later with reference to FIG.

  Next, if there is an anonymization condition for deleting an attribute, the anonymization processing function unit 103 executes an attribute deletion process of performing hashing of the entire target attribute on the extended patient data instead of simply deleting the attribute. Yes (S304). For example, in the case of the present embodiment, since the anonymization condition 2 is the anonymization condition for deleting the attribute “name”, the attribute deletion process is executed on the extended patient data. The details of the attribute deletion process will be described later with reference to FIG.

  Next, if there is an anonymization condition that replaces the attribute element, the anonymization processing function unit 103 does not simply replace the attribute element, but rather hashes some elements of the target attribute to the extended patient data. The attribute replacement process to be executed is executed (S305). For example, in the case of the present embodiment, the anonymization condition 3 is an anonymization condition for replacing the element of the attribute “address” from “prefecture name” to “regional name”, and therefore attribute replacement processing is performed on each article patient data. To execute. The details of the attribute replacement process will be described later with reference to FIG.

  Finally, the anonymization processing function unit 103 outputs the extended patient data after executing the processing of S303 to S305 to the file as the anonymization data 305 (S306), transmits it to the Web server function unit 101, and ends the processing. ..

Next, the record deletion process will be described with reference to FIG.
This is a process corresponding to S303 of FIG.

  First, the anonymization processing function unit 103 of the anonymized data providing server 1 reads the anonymization condition for record deletion. For example, in the case of the present embodiment, the anonymization condition 1 is read (S401). The initial values of the variables i, j, and t are set to 1 (anonymization condition 1: records in which the attribute "address" is other than "Japan" are deleted).

  Next, the anonymization processing function unit 103 identifies the record group Rd to be deleted from the extended patient data, and sets the number of records of Rd to N and the number of attributes of the attribute of Rd to n. For example, in the present embodiment, from the extended patient data read from the extended patient data table 303 of FIG. 7, the record 3032 whose attribute “address” is “America” other than “Japan” is Rd, and N = 1, n = 3 (attribute “name” “address” “sex”) (S402).

  Next, the anonymization processing function unit 103 reads one t-th (t is a record counter) record from the record group Rd to be deleted. For example, in the present embodiment, the record 3032 identified as Rd in S402 is read (S403).

  Next, the anonymization processing function unit 103 acquires the number of elements Ein of the i-th (i is an attribute counter) attribute Ai of the read record. For example, when the record is the record 3032 and i = 1, the number of elements 2 of the first attribute “name” is set as the value of Ein (S404).

  Next, the anonymization processing function unit 103 inputs the jth element Aij and the (j + 1) th element Ai (j + 1) of the attribute Ai to the hash value generation unit 105 to acquire the hash value, and then the record hash value Hri. Value of. For example, when the record is the record 3032, the attribute Ai is the attribute “name” and j = 1, the first element “741DC3” and the second element “Tom” of the attribute “name” are input to the hash value generation unit 105. Then, the hash value is acquired and used as the value of Hri (S405).

  Next, the anonymization processing function unit 103 compares Ein with the value of the variable j, and when Ein is larger than j (S406: Yes), next executes the processing of S407 so that Ein is equal to or less than j. In that case (S406: No), the process of S410 is then executed (S409).

  Next, the anonymization processing function unit 103 increments the value of j by 1 (S407).

  Next, the anonymization processing function unit 103 inputs the th element Aij of the attribute Ai and the Hri value at that time to the hash value generation unit 105 to acquire a new hash value, and sets it as a new Hri value. (S408).

  Next, the anonymization processing function unit 103 compares Ein with the value of the variable j, and when Ein is larger than j (S409: Yes), next executes the processing of S407, and Ein is equal to or less than j. In this case (S09: No), the process of S410 is executed next.

  Next, the anonymization processing function unit 103 increments the value of the variable i by 1, and then executes the processing of S411 (S410).

  Next, in S411, the anonymization processing function unit 103 compares the number of attributes n with the value of the variable i, and when n is smaller than i (S411: No), next executes the processing of S404, When n is i or more (S411: Yes), the process of S412 is performed next.

  Next, n is compared with k (k is an attribute counter), and when n is larger than k (S412: Yes), S413 is executed next, and when n is not larger than k (S412: No). ), Then S415 is executed.

  Next, the anonymization processing function unit 103 receives the hash value Hrk and the hash value Hr (k + 1), generates a hash value, and sets it as a new Hr (k + 1) (S413).

  Next, k is incremented by 1 (S414), and the process returns to S412.

  Next, the anonymization processing function unit 103 increments the value of the variable t (t is a record counter) by 1 and sets the values of the variable i and the variable j to 1 (S415).

  Next, the anonymization processing function unit 103 sets the value of the first element of the record to be deleted to “Delete_R”, which is a label indicating that the record has been deleted, and sets the value of the second element of the record to the value. The record hash value Hrn at that point (note that this is equal to the value of Hr (k + 1) when n ≧ 2), and all the values of the third and subsequent elements of the record are “-” indicating no value. Then, as (Null), the process of S414 is executed (S416). For example, when the record is the record 3032 of FIG. 7, the result of this processing is that the first element shown in the record 3051 of FIG. 9 is “Delete_R”, the second element is the record hash value “22891f”, and the third All the following elements are "-".

  Finally, the anonymization processing function unit 103 compares the number of records N of Rd with the value of the variable t, and when N is t or more (S417: No), next executes the processing of S403 and thereafter, N If is less than t (S417: Yes), the process ends.

Next, the attribute deletion process will be described with reference to FIG.
This is the process corresponding to S304 of FIG.

  First, the anonymization processing function unit 103 of the anonymized data providing server 1 reads out anonymization conditions for attribute deletion (S501). For example, in the case of the present embodiment, the anonymization condition 2 is read (anonymization condition 2: the attribute “name” is deleted). The initial values of the variables i, j, and t are 1.

  Next, the anonymization processing function unit 103 sets the number of records of the extended patient data to N, the number of attributes to n, the attribute group to be deleted to Ad, and the number of attributes of Ad to an. For example, when the extended patient data is the data of the extended patient data table 303 shown in FIG. 7, the elements of N = 4, n = 3, and Ad have the attribute “name” and the number of attributes of Ad an = 1. (S502).

  Next, the anonymization processing function unit 103 reads the t-th (t is a record counter) record of the extended patient data (S503).

  Next, the anonymization processing function unit 103 reads one attribute from Ad and sets it as Adi (i is an attribute counter) (S504).

  Next, the anonymization processing function unit 103 sets the number of elements of the attribute Adi as the value of Ein (S505).

  Next, the anonymization processing function unit 103 inputs the j-th element Adj and the (j + 1) -th element Adi (j + 1) of the attribute Adi to the hash value generation unit 105 to acquire a new hash value, The hash value Ha is set as the value (S506). For example, when the record is the record 3031 of FIG. 7, the attribute Adi is the attribute “name”, and j = 1, the first element “5EF4BE” and the second element “Hitachi Taro” are input to the hash value generation unit 105. Then, a new hash value “465FC4” is acquired and set as the value of Ha.

  Next, the anonymization processing function unit 103 compares Ein with the value of the variable j, and when Ein is larger than j (S507: Yes), next executes the processing of S508, and when Ein is j or less. In (S507: No), the process of S510 is executed next.

  Next, the anonymization processing function unit 103 increments the value of the variable j by 1 (S508).

  Next, the anonymization processing function unit 103 inputs the j-th element Adj and Ha of the attribute Adi to the hash value generation unit 105, acquires a new hash value, and sets it as a new Ha value ( S509).

  Next, the anonymization processing function unit 103 compares Ein with the value of the variable j, and when Ein is larger than j (S510: Yes), next executes the processing of S508, and when Ein is j or less. In (S510: No), the process of S511 is executed next.

  Next, in step S511, the anonymization processing function unit 103 sets the value of the first element of the attribute Adi of the record as “Delete_A”, which is a label indicating that the attribute has been deleted, and sets the value of the second element to the value of the second element. The attribute hash value Ha at that time is set, and the values of the third and subsequent elements are set to "-" (Null) (S511). For example, if the record is record 3031 in FIG. 7 and the attribute to be deleted is “name”, as a result of this processing, as shown in record 3052 in FIG. 9, the first element of the attribute “name” is “ The value of "Name 1" is "Delete_A", the value of "Name 2" which is the second element of the attribute "Name" is "465FC4" which is the value of the attribute hash value Ha at that time, and is the attribute "Name". Does not use the value of "-" because there is no element after 3.

  Next, the anonymization processing function unit 103 increments the value of the variable i by 1 (S512).

  Next, the anonymization processing function unit 103 compares the attribute number an of Ad with the value of the variable i, and when an is greater than or equal to i (S513: No), next executes the processing of S504, and an is If it is smaller than i (S513: Yes), then the process of S514 is executed.

  Next, the anonymization processing function unit 103 increments the value of the variable t by 1 and sets the values of the variable i and the variable j to 1 (S514).

  Finally, the anonymization processing function unit 103 compares the record number N of the extended patient data with the value of the variable t, and when N is t or more (S515: No), next executes the processing of S503 and thereafter. , N is less than t (S515: Yes), the attribute deletion process ends.

Next, the attribute replacement process will be described with reference to FIG.
This is a process corresponding to S305 in FIG.

  First, the anonymization processing function unit 103 of the anonymized data providing server 1 reads out anonymization conditions for attribute replacement (S601). For example, in the case of this embodiment, the anonymization condition 3 is read out (the anonymization condition 3 attribute “address” is replaced from “prefecture name” (address 2) with “regional name” (address 3)). The initial values of the variables i, j, and t are 1.

  Next, the anonymization processing function unit 103 sets the number of records of the extended patient data to N, the number of attributes to n, the attribute group to be replaced to Ar, and the number of attributes of Ar to rn. For example, when the extended patient data is the data of the extended patient data table 303, N = 4, n = 3, the element of Ar has the attribute “address”, and the number of attributes of Ar rn = 1 (S602).

  Next, the anonymization processing function unit 103 reads the t-th (t is a record counter) record of the extended patient data (S603).

  Next, the anonymization processing function unit 103 reads out one attribute from Ar and sets it as Ari (i is an attribute counter) (S604).

  Next, the anonymization processing function unit 103 sets the number of elements to be replaced for the attribute Ari as the value of Eir (S605). For example, when the attribute Ar is “address”, “Prefecture name” (address 2) is replaced, and thus Eir = 2.

  Next, the anonymization processing function unit 103 inputs the jth (j is an element counter) element Arij and the (j + 1) th element Ari (j + 1) of the attribute Ari to the hash value generation unit 105, and A unique hash value is acquired and used as the value of the attribute hash value Ha (S606). For example, when the record is the record 3031 of FIG. 7, the attribute Ari is the attribute “address”, and j = 1, the value “A754B9” of the first element “address 1” and the value of the second element “address 2” The new hash value “B0D8C7” obtained by inputting “Tokyo” into the hash value generation unit 105 is set as the value of Ha.

  Next, the anonymization processing function unit 103 compares Eir with the value of the variable j + 1, and when Eir is larger than j + 1 (Yes: S607), next executes the processing of S608, and when Eir is j or less. In (No: S607), the process of S611 is executed next.

  Next, the anonymization processing function unit 103 increments the value of the variable j by 1 (S608).

  Next, the anonymization processing function unit 103 inputs the j-th element Arij and Ha of the attribute Ari to the hash value generation unit 105 to acquire a new hash value, and sets it as a new Ha value (S609). ).

  Next, the anonymization processing function unit 103 compares the value of Eir with the value of the variable j, and when Eir is larger than j + 1 (S610: Yes), next executes the processing of S608, and when Eir is j + 1 or less. In (S610: No), the process of S611 is executed next.

  Next, the anonymization processing function unit 103 sets the value of the first element of the attribute Ari of the record as “Replace”, which is a label indicating that the attribute has been replaced, and sets the value of the second element at that time. If the attribute hash value Ha is set and Eir is larger than 3, the values of the third to Eirth elements of the attribute Ari are set to "-" (Null), and then the processing of S612 is executed (S611). For example, if the record is record 3031 in FIG. 7 and the attribute to be replaced is “address”, as a result of this processing, as shown in record 3052 in FIG. 9, it is the first element of the attribute “address”. The value of "address 1" is "Replace", and the value of the second element "address 2" is "B0D8C7" which is the value of the attribute hash value Ha at that time.

  Next, the anonymization processing function unit 103 increments the value of the variable i by 1 (S612).

  Next, the anonymization processing function unit 103 compares the number of attributes rn of Ar and the value of the variable i, and when rn is i or more (S613: No), next executes the processing of S604, where rn is If it is smaller than i (S613: Yes), then the process of S614 is executed (S613).

  Next, the anonymization processing function unit 103 increments the value of the variable t (t is a record counter) by 1 and sets the values of the variable i and the variable j to 1 (S614).

  Finally, the anonymization processing function unit 103 compares the record number N of the extended data with the value of the variable t, and when N is t or more (S615: No), next executes the processing of S603 and thereafter, When N is less than t (S615: Yes), the attribute replacement process ends.

Next, the signature verification process will be described with reference to FIG.
This is a process corresponding to S09 in FIG.

  First, in S108 of FIG. 10, the signature verification processing unit 202 of the anonymized data user terminal 2 uses the anonymized data 305 and the verification data 305 stored in the main memory 502 or the hard disk drive 530 of the anonymized data user terminal 2 for verification. The signature value 311 which is the signature value is read (S701, S702).

  Next, the signature verification processing unit 202 inputs the anonymized data 305 to the signature generation unit 204 of the anonymized data user terminal 2 and acquires the generated signature value δ (S703).

  Finally, the signature verification processing unit compares the signature value 311 which is the verification signature value with δ, and when the two values are the same (S704: Yes), the anonymized data is valid. The display device 510 of the anonymized data user terminal 2 displays “OK” for the data user (S705). On the other hand, when the two values are different (S704: No), the anonymized data is not authorized due to tampering or a mistake, and the data is displayed by the display device 510 of the anonymized data user terminal 2 or the like. "NG" is displayed to the user (S706), and the process ends.

  As described above, in the anonymized data providing system of the exemplary embodiment, the value of the replacement candidate of the target data is added to the target data in advance, and the intermediate process of the signature generation process is performed instead of simply deleting or replacing. Since the hash value is replaced with the hash value, it is possible to verify the anonymity of the signature value even after the anonymization process such as deletion or replacement.

  Also in the hashing process, after adding a random number to the original data and performing hashing with the value of each attribute and the random number as input values, the calculation necessary to identify the original data from the hash value Since the amount is enormous, the risk of restoring the original data from the anonymized data can be reduced.

  In addition, when adding a random number to the original data, one random number is added to each attribute, and then hashing including random numbers is performed stepwise within each attribute in the algorithm shown in FIGS. 12A to 16. Therefore, the data size of the anonymized data can be reduced as compared with the case where random numbers are added to all the elements of the original data.

  Further, since the hashing process is the same as the intermediate process of the signature generation process, the signature generation process in the signature verification process of the anonymized data can be reduced and the signature verification process can be speeded up.

  As described above, according to the present embodiment, when utilizing anonymized data for research and development in the medical field, even if anonymization processing such as data deletion or replacement is performed, the validity of anonymization can be confirmed. Since the verification is possible, it is possible to avoid the situation where the research result is illegal due to the use of illegal anonymized data.

  DESCRIPTION OF SYMBOLS 1 ... Anonymized data providing server, 2 ... Anonymized data user terminal, 3 ... Network, 102 ... Record expansion function part, 103 ... Anonymization processing function part, 105 ... Hash value generation part, 106 ... Signature generation part, 110 Storage unit 301 Patient data table 302 Abstract pattern group 303 Extended patient data table 304 Signature data table 305 Anonymous data 201 Browser function unit 202 Signature verification processing unit 203 ... hash value generation unit, 204 ... signature generation unit, 210 ... storage unit, 311 ... signature value

Claims (6)

An anonymization system in which confidential information is anonymized by an information processing device and provided to a user.
Secret information storage means for storing secret information,
An abstraction candidate group information storage unit which is a candidate group of information for abstracting the secret information,
Extended secret data storage means for storing extended secret data in which a candidate group of information to be abstracted is added to the secret information,
Anonymized data storage means for storing anonymized data obtained by deleting or replacing some information from the secret information,
Extended secret data generation means for generating the extended secret data using the secret information and the abstraction candidate group information;
Signature generation means for generating a digital signature using the extended secret data or anonymized data as an intermediate value of the hash value of the secret information,
Anonymizing means for generating anonymized data using the extended secret data,
Anonymized data validity verification means for verifying the validity of given anonymized data,
The expanded secret data generation unit refers to a secret information stored in the secret information storage unit and a candidate group of information for abstracting the secret information stored in the abstraction candidate group information storage unit, and refers to the expansion. Generate secret data,
The anonymization means performs a process of replacing the extended secret data with the same hash value as the intermediate value of the signature generation of the signature generation means to generate anonymized data,
The signature generation unit generates a first signature value from the extended secret data generated by the extended secret data generation unit and a second signature value from the anonymization data generated by the anonymization unit. ,
The anonymization data validity verification means verifies the validity of given anonymization data by comparing the first signature value and the second signature value. Furthermore, a random number generating means for adding a random number to the extended secret data is provided,
The anonymization system according to claim 1, wherein the signature generation unit generates a hash value as an intermediate value from the extended secret data to which the random number generated by the random number generation unit is added. The random number generation means adds one random number to each attribute of the extended secret data,
The anonymization system according to claim 2, wherein the signature generation unit generates an intermediate value of hash values including random numbers in the input for each attribute. A method of anonymizing confidential information by an information processing device and providing it to a user,
A secret information storing step of storing secret information,
An abstraction candidate group information storage step, which is a candidate group of information for abstracting the secret information,
An extended secret data storing step of storing extended secret data in which a candidate group of information to be abstracted is added to the secret information,
Anonymized data storage step of storing anonymized data obtained by deleting or replacing some information from the secret information,
An extended secret data generating step of generating the extended secret data using the secret information and the abstraction candidate group information;
A signature generation step of generating a digital signature using the extended secret data or anonymized data as an intermediate value of the hash value of the secret information,
Anonymizing step of generating anonymized data using the extended secret data,
Anonymized data validity verification step for verifying the validity of given anonymized data,
In the extended secret data generation step, the extension is performed by referring to the secret information stored in the secret information storage step and the candidate group of information for abstracting the secret information stored in the abstraction candidate group information storage step. Generate secret data,
In the anonymization, with respect to the extended secret data, a process of replacing the intermediate value of the signature generation of the signature generation means with the same hash value is executed to generate anonymized data,
In the signature generation step, a first signature value is generated from the extended secret data generated by the extended secret data generation means, and a second signature value is generated from the anonymization data generated by the anonymization means. ,
In the anonymization data validity verification step, the validity of the given anonymization data is verified by comparing the first signature value and the second signature value. Further, a random number generation step of adding a random number to the extended secret data,
5. The anonymization method according to claim 4, wherein in the signature generation step, a hash value as an intermediate value is generated from the extended secret data to which the random number generated by the random number generation means is added. In the random number generating step, one random number is added to each attribute of the extended secret data,
The anonymization method according to claim 5, wherein the signature generation means generates an intermediate value of hash values including random numbers in the input for each attribute.

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