CN109033873B - Data desensitization method for preventing privacy leakage - Google Patents
Data desensitization method for preventing privacy leakage Download PDFInfo
- Publication number
- CN109033873B CN109033873B CN201810796961.6A CN201810796961A CN109033873B CN 109033873 B CN109033873 B CN 109033873B CN 201810796961 A CN201810796961 A CN 201810796961A CN 109033873 B CN109033873 B CN 109033873B
- Authority
- CN
- China
- Prior art keywords
- data
- association
- database
- tables
- index
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
- G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
- G06F21/6245—Protecting personal data, e.g. for financial or medical purposes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
- G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
- G06F21/6227—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database where protection concerns the structure of data, e.g. records, types, queries
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Bioethics (AREA)
- Physics & Mathematics (AREA)
- Computer Security & Cryptography (AREA)
- Software Systems (AREA)
- Computer Hardware Design (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Databases & Information Systems (AREA)
- Medical Informatics (AREA)
- Epidemiology (AREA)
- Primary Health Care (AREA)
- Public Health (AREA)
- Storage Device Security (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention relates to the field of big data and discloses a data desensitization method for preventing privacy leakage. The method specifically comprises the following steps: removing the explicit association according to the same index field among different data tables of the database; defining a cryptography function aiming at index fields among the data tables, and processing the associated ID; and calculating the associated ID value according to the cryptography function, and performing data access after writing the associated ID value. The technical scheme of the invention mainly adopts the cryptology thought to perform algorithm processing on the associated fields among the data tables and remove the strong associated coupling between different tables and different data of the database and the user information, so that even under the condition of acquiring the super authority of the user database, the association among the data and the information cannot be known, and the obtained data and the user cannot confirm the relationship, thereby realizing the privacy protection of the data. The method can effectively prevent privacy leakage caused by direct access of the database due to platform attack, ghost and the like.
Description
Technical Field
The invention relates to the field of big data, in particular to a data desensitization method for preventing privacy leakage.
Background
With the development of intellectualization and networking, the information era is the data acquisition era. The purposeful collection, arrangement, processing, analysis and utilization of data are obvious characteristics of the big data era.
The data acquisition mode can be automatically acquired by a sensor, can be automatically acquired by intelligent equipment, and can also be performed by mode backgrounds such as app and user browsing webpages. The content of data collection at present relates to the aspects of individuals. Data has become the cornerstone of all our information applications. The data acquisition brings great convenience for individuals on the one hand, and brings potential privacy leakage risks at the same time.
In the field of medical health, the digital hospital age based on electronic medical records is basically realized at present. The informatization of hospitals forms a highly integrated hospital information management system based on electronic medical records and centered on patient information. The electronic medical record is an important clinical information resource which is necessary for modern medical institutions to carry out efficient and high-quality clinical diagnosis, scientific research and medical management work, and is also a main information source of resident health files. The standardized electronic medical record and the construction of a new generation hospital information system taking the standardized electronic medical record as a core are the premise basis for realizing the clinical information sharing and the interconnection and intercommunication and the cooperative service of medical institutions in the regional range by taking resident individuals as mainlines, can ensure that the health files of residents are 'output active and data', can also help to implement and standardize clinical paths, realize the supervision of medical processes and improve the medical treatment level and the emergency command capability. A standard electronic medical record system or similar medical informatization system includes a very large amount of user information, including:
(1) patient basic information. Such as demographic information, socioeconomic information, relatives information, social security information, and biological information
(2) Basic health information. Including the current medical history, the past medical history, the immunization history, the allergy history, the menstruation history, the family history, the disability condition and the like
(3) A summary of the hygiene events. Including the servicing activities that occur over the patient's past medical facility visits.
(4) Expense record
(5) And (5) recording the outpatient and emergency diagnosis and treatment. Comprises the hospitalization records of the outpatient medical record, the outpatient prescription, the examination and examination record and the like (6). Including medical record, medical advice, treatment record, nursing record, etc
(7) And recording the health physical examination. A routine physical examination record which mainly aims at health monitoring and preventive health care.
The data are generally collected by related mechanisms and stored in related data centers in a database mode and the like, so that powerful support is provided for future treatment, prevention, health care and the like of users, and data support is provided for scientific research, decision support and the like of hospitals. Data provides convenience to patients and medical institutions, and also poses the risk of privacy leakage for patients or users. For example, the medical information database includes privacy information such as user contact information, identification cards, addresses, family members, etc., and also includes some sensitive privacy information of personal health of the user, such as HIV, hepatitis, etc., which will have a great influence on the user.
Data or privacy disclosure involves three main approaches: 1. personal devices, passwords, etc. are lost, resulting in personal information leakage. For example, if a personal mobile phone is lost, or a password of an app is lost, a third party enters the app after acquiring the password, so that personal information is leaked; 2. platform, data, etc. intrusions result in large volumes of data leakage. The existing data center, information system, etc. are all communicated with the network, once an external invader enters a platform and a system, the external invader can easily export, even copy and remove database files in batches, and steal user information; 3. and (4) carrying out ghost stealing. Internal network managers, operation and maintenance personnel, database managers, third-party system developers and the like can easily contact the database, and become an important threat of leakage.
In the existing database, the information of general users is stored in a sub-table mode, and different tables are associated through a certain field. To ensure that the database is copied and accessed without data leakage, the best method is to encrypt the database, but after the database is encrypted, new problems are caused, such as incapability of quick retrieval, data statistical analysis, and data mining, which further cause a great reduction in the access speed of the database and an increase in additional deployment cost.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: in response to the above-identified problems, a data desensitization method for preventing privacy disclosure is provided.
The technical scheme adopted by the invention is as follows: a data desensitization method for preventing privacy leakage specifically comprises the following processes: step 1, removing explicit association according to the same index field among different data tables of a database; step 2, defining a cryptography function aiming at the index fields among the data tables, and processing the associated ID; and 3, calculating the association ID value according to the cryptography function, and performing data access after writing the association ID value.
Further, the specific process of step 1 is as follows: step 11, index association is carried out through the same index fields of different data tables of the database; and step 12, respectively defining the same index fields among different databases to ensure that the index field values among different tables are completely different.
Further, the specific process of step 2 is as follows: step 21, defining a cryptographic function ID ═ f (M1, M2 … Mn, R, … Key) for the index field between the data tables; the ID is an index association ID of the table, M1 and M2, Mn is data characteristics related to a user, R is a random number, Key is a Key selected for the operation, and f is an encryption function or a one-way hash algorithm of a finite field; and step 22, calculating a cryptographic function to ensure that the index field of each data table is completely different from the index field value of the data table.
Further, the specific process of step 3 is as follows: step 31, during forward query, calculating a correlation ID value according to a cryptographic function ID ═ f (M1, M2 … Mn, R, … Key); step 32, writing the calculated associated ID value as the index field value of the data table; and step 33, performing data access and reversely inquiring the required data characteristics.
Compared with the prior art, the beneficial effects of adopting the technical scheme are as follows:
(1) the technical scheme of the invention mainly adopts the cryptology thought to perform algorithm processing on the associated fields among the data tables and remove the strong associated coupling between different tables and different data of the database and the user information, so that even under the condition of acquiring the super authority of the user database, the association among the data and the information cannot be known, and the obtained data and the user cannot confirm the relationship, thereby realizing the privacy protection of the data. The method can effectively prevent privacy leakage caused by direct access of the database due to platform attack, ghost and the like.
(2) The method can prevent private data from being leaked, and meanwhile, the method has no influence on data mining and use, realizes balance between safety and performance and data utilization, and simultaneously meets the requirements of data mining, modeling, statistical analysis, artificial intelligence, decision support and other big data applications. The balance of safety protection and data use is achieved.
(3) The method is suitable for all fields related to user data acquisition, including medical health, electronic commerce, mobile application, internet service and the like.
Detailed Description
The present invention will be further described with reference to the following examples.
The structure of the database has a great influence on the performance and efficiency of the database, especially in the case of a very large amount of data. An application database, or data center, typically includes several data tables, each of which is made up of several different fields, with associations between tables, typically being made through certain fields or foreign keys. For example, a hospital health record database, which includes 4 data tables (a real database has several tables, which are only illustrated by simple contents), data table 1 is a basic information table including ID numbers, nicknames, names, identification numbers, etc. of persons, and data table 2 is a contact list recording mobile phone numbers, e-mails, home addresses, and other data related to home privacy. The data table 3 is a personal profile file table, and records the medical card number, blood type, allergy history, chronic disease, infectious disease and other history conditions of the user. The data table 4 is a physical examination table, and data which needs to be emphasized and kept secret, such as HIV screening, hepatitis B screening and the like of a user, are stored in the physical examination table. The data table 1 can be associated with the contact information, address, etc. of the user's individual and family in table 2 by the person ID (defined as RY _ ID), can be associated with the health record in table 3 of the user by the ID to acquire the history medical history, family history, etc. of the user, and can be acquired by the record ID (defined as DA _ ID) in table 3 to acquire the physical examination table of the user in table 4. If a person normally or abnormally obtains the access authority of the database, all privacy information of the user in the data table can be obtained through the method, and potential harm is caused to the leakage of the user privacy. However, if we remove the associated ID (personnel ID, file ID, etc.) in the data table, the data becomes irrelevant, and only pure data irrelevant to a certain person is used, so that even if the data is leaked, privacy can not be leaked, and meanwhile, the data can still be utilized normally.
A data desensitization method for preventing privacy leakage specifically comprises the following processes:
step 1, removing explicit association according to the same index field among different data tables of a database;
wherein, the specific process of the step 1 is as follows: step 11, index association is carried out through the same index fields of different data tables of the database; the data table 1 and the data table 2 are indexed and associated through RY _ ID, namely RY _ ID of the data table 1 and RY _ ID of the data table 2 are the same, and association between basic information and contact information is performed through the same ID to form complete information. Similarly, the same mechanism is used for index association between the data tables 1 and 2, between the data tables 2 and 3, and between the data tables 3 and 4; and step 12, respectively defining the same index fields among different databases, so that the index field values among different tables are completely different and irregularly circulated. RY _ ID of Table 2 is defined as RY _ ID2, RY _ ID of Table 3 is RY _ ID3, DA _ ID of Table 4 is DA _ ID4 (even defined as field names whose names are totally unrelated), and their corresponding values are redefined, i.e., RY _ ID ≠ RY _ ID2 ≠ RY _ ID3, DA _ ID ≠ DA _ ID 4.
Step 2, defining a cryptography function aiming at the index fields among the data tables, and processing the associated ID;
the specific process of the step 2 is as follows: step 21, defining a cryptographic function ID ═ f (M1, M2 … Mn, R, … Key) for the index field between the data tables. The ID is an index association ID of the table, and M1 and M2, the Mn is data characteristics related to the user and can be used as information of identity characteristics, or data characteristics related to the user information of the index table, such as information of name, identity card, social security card and the like, or the ID, the number and the content of M of the user, and can be flexibly selected according to needs; r is a random number, is optional, and can ensure that the ID numbers of all records of the same user are different after the use; key is a Key selected for the operation, and is selectable; f is an encryption function or a one-way hash algorithm of a finite field, and if the forward connection of the data table is considered, and the reverse direction is not supported, the hash function can be used; if forward and reverse connection indices are to be considered, symmetric or asymmetric encryption algorithms may be chosen, such as AES, SM4, RSA, ECC, etc. And step 22, calculating a cryptographic function to ensure that the index field of each data table is completely different from the index field value of the data table. Under the condition of increasing random numbers, different data records of the user also have no relation at all, and under the condition of not mastering keys and algorithms, anyone can not directly analyze and obtain the related information of the user through the data of the database.
And 3, calculating the association ID value according to the cryptography function, and performing data access after writing the association ID value.
Wherein, the specific process of the step 3 is as follows: step 31, during forward query, according to the cryptographic function
ID＝f(M1、M2…Mn、R、…Key)，
That is, the associate ID value is calculated according to the known conditions M1, M2 … Mn, R, … Key. And step 32, writing the calculated association ID value as the index field value of the data table. In step 33, if a record is known and data access is to be performed, and whose data is to be checked, then (M1, M2 … Mn, R) ═ f can be used-1(ID, Key), calculate the Key word M, use M to confirm and inquire.
Taking data table 2 as an example, taking the data in table 2 as an example for access: let us define an index function of
RY_ID2＝f(XM\\SFZHM、Key)
If f is AES algorithm and \ \ is character combination operation, we want to search a person of Zhang III, 51013019560704341, and his ID is
RY _ ID2 ═ AES (zhangsan 51013019560704341, Key)
The obtained value RY _ ID2 is written in the data table 2. In the forward query, the data RY _ ID2 of the user can be obtained by the same calculation. When the user inquires the data of whom reversely, only AES (RY _ ID2, Key) needs to be calculated, and the user information is zhang san 5101301956070434.
By using the technical scheme of the embodiment, the associated information of the database is removed, so that the irrelevance between the user information and the user data can be ensured, and the purpose of protecting the privacy of the user can be achieved under the condition that the data of the extreme end is leaked. Meanwhile, through the selection of the algorithm and the selection of the parameters, the decoupling between the data can be realized according to the requirement, and the recovery of the data information can also be realized. Under the condition of protecting the privacy of the user, technical conditions are provided for the utilization of the data. The method achieves a balance between security and data utilization.
The invention is not limited to the foregoing embodiments. The invention extends to any novel feature or any novel combination of features disclosed in this specification and any novel method or process steps or any novel combination of features disclosed. Those skilled in the art to which the invention pertains will appreciate that insubstantial changes or modifications can be made without departing from the spirit of the invention as defined by the appended claims.
Claims (2)
1. A data desensitization method for preventing privacy disclosure is characterized by specifically comprising the following processes: step 1, removing explicit association according to the same index field among different data tables of a database; step 2, defining a cryptographic function aiming at the index field between the data tables after the explicit association is removed, and processing the association ID; step 3, calculating an association ID value according to a cryptography function, and performing data access after the association ID value is written in;
the specific process of the step 1 is as follows: step 11, index association is carried out through the same index fields of different data tables of the database; step 12, defining the same index fields among different databases respectively, so that the index field values among different tables are completely different;
the specific process of the step 2 is as follows: step 21, defining a cryptographic function ID ═ f (M1, M2 … Mn, R, … Key) for the index field between the data tables; the ID is an index association ID of the table, M1 and M2, Mn is data characteristics related to a user, R is a random number, Key is a Key selected for the operation, and f is an encryption function or a one-way hash algorithm of a finite field; and step 22, calculating a cryptographic function to ensure that the index field of each data table is completely different from the index field value of the data table.
2. The data desensitization method according to claim 1, wherein, at the time of forward query, the association ID value is calculated according to a cryptographic function ID ═ f (M1, M2 … Mn, R, … Key), step 31; step 32, writing the calculated associated ID value as the index field value of the data table; and step 33, performing data access and reversely inquiring the required data characteristics.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810796961.6A CN109033873B (en) | 2018-07-19 | 2018-07-19 | Data desensitization method for preventing privacy leakage |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810796961.6A CN109033873B (en) | 2018-07-19 | 2018-07-19 | Data desensitization method for preventing privacy leakage |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109033873A CN109033873A (en) | 2018-12-18 |
| CN109033873B true CN109033873B (en) | 2020-11-17 |
Family
ID=64643590
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810796961.6A Active CN109033873B (en) | 2018-07-19 | 2018-07-19 | Data desensitization method for preventing privacy leakage |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109033873B (en) |
Families Citing this family (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111382459B (en) * | 2018-12-27 | 2022-05-17 | 财团法人工业技术研究院 | Private data integration method and server |
| CN109918430B (en) * | 2019-01-22 | 2022-09-23 | 中国人民解放军战略支援部队信息工程大学 | 5G user data disassociation storage system and access method |
| CN110138792B (en) * | 2019-05-21 | 2020-01-14 | 上海市疾病预防控制中心 | Public health geographic data privacy removal processing method and system |
| CN111711674B (en) * | 2020-06-05 | 2023-03-14 | 华南师范大学 | Cloud computing method based on Internet of things |
| CN111859438A (en) * | 2020-07-31 | 2020-10-30 | 上海观安信息技术股份有限公司 | Reversible desensitization encryption algorithm with specified length |
| CN112541193B (en) * | 2020-12-10 | 2024-05-24 | 支付宝(杭州)信息技术有限公司 | Protection method and device for private data |
| CN113257375A (en) * | 2021-05-12 | 2021-08-13 | 中国疾病预防控制中心病毒病预防控制所 | Method for desensitizing sudden acute infectious disease data |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101504668A (en) * | 2009-03-24 | 2009-08-12 | 北京理工大学 | Cryptograph index supported database transparent encryption method |
| CN102243630A (en) * | 2010-05-12 | 2011-11-16 | 北京安华金和科技有限公司 | Method for establishing database cipher text indexes without partial ordering relation |
| CN106874401A (en) * | 2016-12-30 | 2017-06-20 | 中安威士（北京）科技有限公司 | A kind of ciphertext index method of data base-oriented encrypted fields fuzzy search |
| CN106934030A (en) * | 2016-12-08 | 2017-07-07 | 戴林 | The ciphertext index method and Ku Nei encryption systems of a kind of data base-oriented encryption |
Family Cites Families (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7266699B2 (en) * | 2001-08-30 | 2007-09-04 | Application Security, Inc. | Cryptographic infrastructure for encrypting a database |
| US10339336B2 (en) * | 2003-06-11 | 2019-07-02 | Oracle International Corporation | Method and apparatus for encrypting database columns |
| US7519835B2 (en) * | 2004-05-20 | 2009-04-14 | Safenet, Inc. | Encrypted table indexes and searching encrypted tables |
| TW200802029A (en) * | 2005-09-26 | 2008-01-01 | Ingrian Networks Inc | System and method for protecting sensitive data in a database |
| CN101859306B (en) * | 2009-04-07 | 2013-01-23 | 日电（中国）有限公司 | Method and equipment for generating blind index table, and united keyword search method and equipment |
| CN102594779B (en) * | 2011-01-05 | 2016-03-02 | 中国移动通信集团公司 | A kind of user data processing method and equipment thereof |
| CN105824825B (en) * | 2015-01-06 | 2019-10-18 | 中国移动通信集团湖南有限公司 | A kind of sensitive data recognition methods and device |
| CN107145799A (en) * | 2017-05-04 | 2017-09-08 | 山东浪潮云服务信息科技有限公司 | A kind of data desensitization method and device |
| CN107798253B (en) * | 2017-10-31 | 2020-04-03 | 新华三大数据技术有限公司 | Data desensitization method and device |
| CN108021822A (en) * | 2017-11-30 | 2018-05-11 | 广州天鹏计算机科技有限公司 | The desensitization method and system of data |
-
2018
- 2018-07-19 CN CN201810796961.6A patent/CN109033873B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101504668A (en) * | 2009-03-24 | 2009-08-12 | 北京理工大学 | Cryptograph index supported database transparent encryption method |
| CN102243630A (en) * | 2010-05-12 | 2011-11-16 | 北京安华金和科技有限公司 | Method for establishing database cipher text indexes without partial ordering relation |
| CN106934030A (en) * | 2016-12-08 | 2017-07-07 | 戴林 | The ciphertext index method and Ku Nei encryption systems of a kind of data base-oriented encryption |
| CN106874401A (en) * | 2016-12-30 | 2017-06-20 | 中安威士（北京）科技有限公司 | A kind of ciphertext index method of data base-oriented encrypted fields fuzzy search |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109033873A (en) | 2018-12-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109033873B (en) | Data desensitization method for preventing privacy leakage | |
| EP3531656B1 (en) | Block chain-based data processing method and device | |
| Yüksel et al. | Research issues for privacy and security of electronic health services | |
| US8566113B2 (en) | Methods, systems and computer program products for providing a level of anonymity to patient records/information | |
| Lubarsky | Re-identification of “anonymized data” | |
| US20030074564A1 (en) | Encryption system for allowing immediate universal access to medical records while maintaining complete patient control over privacy | |
| MX2014014368A (en) | System for anonymizing and aggregating protected health information. | |
| Kanwal et al. | Privacy preservation of electronic health records with adversarial attacks identification in hybrid cloud | |
| JP2015187828A (en) | information management system | |
| Khan et al. | Development of national health data warehouse Bangladesh: Privacy issues and a practical solution | |
| RU2664406C2 (en) | Method and system for making multisite performance indicator anonymous and for controlling actions and re- identification of anonymous data | |
| Huda et al. | A privacy management architecture for patient-controlled personal health record system | |
| WO2021062310A1 (en) | Utilizing a user's health data stored over a health care network for disease prevention | |
| US20240119174A1 (en) | Personal Data Anonymization System (PDAS) with Customized Token | |
| CN115831302A (en) | Method and apparatus for managing disease control data | |
| Afzal et al. | Trusted framework for health information exchange | |
| Lynda et al. | Data security and privacy in e-health cloud: Comparative study | |
| Asija et al. | A survey on security and privacy of healthcare data | |
| Clark et al. | Computer vision for locating buried objects | |
| Dhyani | E-Health data risks & protection for public cloud: An elderly healthcare usecase for Swedish municipality | |
| Bose et al. | A Survey on Privacy of Health Data Lifecycle: A Taxonomy, Review, and Future Directions | |
| Siegenthaler et al. | Sharing private information across distributed databases | |
| Siegenthaler et al. | Privacy enforcement for distributed healthcare queries | |
| Hu et al. | The design and implementation of the privacy protection system of a Regional Health Information Platform | |
| RU2748052C1 (en) | Method and system for medical data exchange |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |