CN115114315A - Lawyer sensitive data privacy chaining certificate storing method, device, equipment and storage medium - Google Patents
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Abstract
This specification provides lawyer sensitive data privacy chain deposit certificate method, apparatus, device and storage medium, the method including: forming an open data set with a lawyer ID as a main key based on lawyer open data and storing the open data set in a block chain; acquiring random parameters and local random numbers; taking the random parameter, the local random number and the quantized lawyer sensitive data as input, generating a commitment data set taking the lawyer ID as a main key based on a homomorphic commitment algorithm, and storing the commitment data set in a block chain; receiving a credit report generation request carrying a target attorney ID and an evaluation dimension; extracting relevant data of the target lawyer ID corresponding to the evaluation dimension from the open data set and the commitment data set; a credit report for the target attorney ID corresponding to the evaluation dimension is generated from the relevant data and stored in a blockchain for access by the user. Embodiments of the present description can address attributes and security issues of lawyer sensitive data link credit.
Description
Technical Field
The present disclosure relates to the technical field of private chain credit for sensitive data, and in particular, to a method, an apparatus, a device and a storage medium for private chain credit for lawyers.
Background
The blockchain technology has the property of being non-tamper-able, and due to the special property of blockchains, many applications desire to chain-up data. For example, a link credit scheme for lawyer sensitive data (such as penalty information such as lawyer, contract authorization information, contract content, and order content) generally employs storing lawyer sensitive data in a private database or a sensitive data hosting platform, and then hashing the link credit; or, after asymmetric encryption is carried out on lawyer sensitive data, the certificate is uploaded, and only a user with a secret key can unlock the data.
However, when lawyer sensitive data is subjected to hash chain saving, the attribute problem of the lawyer sensitive data exists; if the lawyer sensitive data is subjected to asymmetric encryption and then the chain deposit certificate is uploaded, decryption needs to be performed in a shared key mode during later use, and the problems that lawyer sensitive data is illegally leaked and cannot be traced are easily caused.
Disclosure of Invention
An object of the embodiments of the present disclosure is to provide a lawyer sensitive data privacy link credit method, apparatus, device and storage medium, so as to solve or at least partially solve the attribution and security problems of lawyer sensitive data link credit.
To achieve the above object, in one aspect, an embodiment of the present specification provides a lawyer sensitive data privacy chain deposit certificate method, including:
forming an open data set with a lawyer ID as a main key based on lawyer open data and storing the open data set in a block chain;
acquiring random parameters and local random numbers;
taking the random parameter, the local random number and the quantized lawyer sensitive data as input, generating a commitment data set taking a lawyer ID as a main key based on a homomorphic commitment algorithm, and storing the commitment data set in the block chain;
receiving a credit report generation request carrying a target lawyer ID and an evaluation dimension;
extracting relevant data of the target lawyer ID corresponding to the evaluation dimension from the open dataset and the commitment dataset;
generating a credit report for the target lawyer ID corresponding to the evaluation dimension from the relevant data and storing it in the blockchain for user access.
In the lawyer sensitive data privacy upload-link evidence method according to the embodiment of the present specification, the method for generating a commitment data set using a lawyer ID as a main key based on a homomorphic commitment algorithm by using the random parameter, the local random number and quantized lawyer sensitive data as inputs includes:
and generating a commitment data set with the lawyer ID as a main key based on the Pedson commitment by taking the random parameter, the local random number and the quantized lawyer sensitive data as input.
In the lawyer sensitive data privacy upload method according to the embodiment of the present specification, the generating a commitment data set with a lawyer ID as a primary key based on the pearson commitment includes:
according to the formula
Generating a commitment data set with the lawyer ID as a main key;
wherein the content of the first and second substances,
is the firstiThe first of an individual lawyerjThe commitment value of the individual lawyer sensitive data,
is the firstiThe first of an individual lawyerjThe data that is sensitive to the individual lawyer,
is as followsiThe first of an individual lawyerjThe local random number assigned by the individual lawyer sensitive data,GandHtwo selected from a designated elliptic curve are taken as base points of the random parameter.
In the lawyer sensitive data privacy upload certificate method according to the embodiment of the present specification, generating a credit report of the target lawyer ID corresponding to the evaluation dimension according to the relevant data includes:
inputting the relevant data of the target lawyer ID into a formula
Generating a credit assessment index value for the target attorney ID corresponding to the assessment dimension; wherein the content of the first and second substances,
is the firstiEach attorney corresponding to a credit evaluation index value for the evaluation dimension,
is the firstiThe individual lawyers correspond to the first of the evaluation dimensionsjThe commitment value of the individual lawyer sensitive data,
is that
The weight of (a) is calculated,Mis a firstiEach attorney corresponds to the number of attorney sensitive data for the evaluation dimension,
is the firstiThe individual lawyers correspond to the first of the evaluation dimensionskThe data was opened by the individual lawyer,
is that
The weight of (a) is determined,Nis as followsiEach attorney corresponds to the attorney opening data quantity of the evaluation dimension;
and filling the credit evaluation index value into a credit report template corresponding to the evaluation dimension, and generating a credit report of which the target attorney ID corresponds to the evaluation dimension.
In the lawyer sensitive data privacy uplink credit method according to the embodiment of the present specification, the credit report further includes:
credit report input data;
lawyer credit evaluation model.
In the lawyer sensitive data privacy uplink credit method according to the embodiment of the present specification, after generating a credit report according to the relevant data and storing the credit report in the blockchain, the method further includes:
performing commitment disclosure based on a homomorphic commitment algorithm when a commitment validation request for the credit report is received.
In another aspect, an embodiment of the present specification further provides an lawyer sensitive data privacy upload and receipt apparatus, including:
the open data forming module is used for forming an open data set with a lawyer ID as a main key based on lawyer open data and storing the open data set in a block chain;
the random data acquisition module is used for acquiring random parameters and local random numbers;
a commitment certification generation module, configured to generate a commitment data set with a lawyer ID as a main key based on a homomorphic commitment algorithm with the random parameter, the local random number, and quantized lawyer sensitive data as inputs, and store the commitment data set in the blockchain;
the report request receiving module is used for receiving a credit report generation request carrying the ID and the evaluation dimension of the target lawyer;
a relevant data extraction module for extracting relevant data of the target lawyer ID corresponding to the evaluation dimension from the open data set and the committed data set;
and the credit report generation module is used for generating a credit report of the target lawyer ID corresponding to the evaluation dimension according to the related data, and storing the credit report in the block chain for the user to access.
In another aspect, the embodiments of the present specification further provide a computer device, which includes a memory, a processor, and a computer program stored on the memory, and when the computer program is executed by the processor, the computer program executes the instructions of the above method.
In another aspect, the present specification further provides a computer storage medium, on which a computer program is stored, and the computer program is executed by a processor of a computer device to execute the instructions of the method.
In another aspect, the present specification further provides a computer program product, which includes a computer program that, when executed by a processor of a computer device, executes the instructions of the method described above.
As can be seen from the technical solutions provided in the embodiments of the present specification, the commitment certification based on lawyer sensitive data in the embodiments of the present specification, that is, ciphertext certification of lawyer sensitive data is stored in all public blockchains of the blockchain; compared with the HashUpLink, the homomorphic committee UpLink for lawyer sensitive data can prove the attribution of lawyer sensitive data; meanwhile, as the homomorphic promise algorithm is homomorphic encryption for numerical calculation, after lawyer sensitive data is homomorphic encrypted, the nodes or other users on the chain can correctly verify the lawyer sensitive numerical calculation result; therefore, based on the verification of the commitment, the privacy of lawyer sensitive data can be protected, and the safety of chaining of the lawyer sensitive data is improved.
Drawings
In order to more clearly illustrate the embodiments of the present specification or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present specification, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative effort. In the drawings:
FIG. 1 illustrates a block diagram of a lawyer sensitive data privacy chaining token system in some embodiments of the present description;
FIG. 2 illustrates a flow diagram of a lawyer sensitive data privacy upload credentialing method in some embodiments of the present description;
FIG. 3 is a flow chart illustrating a lawyer sensitive data privacy upload credentialing method in further embodiments of the present disclosure;
FIG. 4 shows a block diagram of a lawyer sensitive data privacy chain credentialing apparatus in some embodiments of the present disclosure;
FIG. 5 shows a block diagram of a computer device in some embodiments of the present description.
[ description of reference ]
10. A block chain;
20. a server side;
30. a client;
40. a data source;
41. an open data forming module;
42. a random data acquisition module;
43. a commitment certification generating module;
44. a report request receiving module;
45. a related data extraction module;
46. a credit report generation module;
502. a computer device;
504. a processor;
506. a memory;
508. a drive mechanism;
510. an input/output interface;
512. an input device;
514. an output device;
516. a presentation device;
518. a graphical user interface;
520. a network interface;
522. a communication link;
524. a communication bus.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present specification, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only a part of the embodiments of the present specification, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present specification without making any creative effort shall fall within the protection scope of the present specification.
An attorney sensitive data privacy upload certificate system of some embodiments of the present description is illustrated in fig. 1, which may include a blockchain 10, a server 20, a client 30, and a data source 40. The server 20 may obtain lawyer open data and lawyer sensitive data from the data source 40, form the lawyer open data into an open data set with a lawyer ID as a main key, send the open data set to the blockchain 10 for storage, and generate a commitment data set with the lawyer ID as a main key based on a homomorphic commitment algorithm from the sensitive data (i.e., perform homomorphic encryption on the sensitive data to generate a commitment certificate), and send the commitment data set to the blockchain 10 for storage; when receiving a credit report generation request carrying a target attorney ID and an evaluation dimension sent by a client 30 through a blockchain 10, the server 20 may generate a credit report corresponding to the evaluation dimension of the target attorney ID according to an open data set and a committed data set, and send the credit report to the blockchain 10 for storage; and the server 20 may also commit and disclose the credit report according to the commit validation request of the client 30.
Based on the proof of promise of lawyer sensitive data, namely, ciphertext proof of lawyer sensitive data is stored in all public blockchains of the blockchain; compared with the Hash chain, the homomorphic committed chain for lawyer sensitive data can prove the attribution of the lawyer sensitive data; meanwhile, as the homomorphic promise algorithm is homomorphic encryption for numerical calculation, after lawyer sensitive data is homomorphic encrypted, the nodes or other users on the chain can correctly verify the lawyer sensitive numerical calculation result; therefore, based on the verification of the commitment, the privacy of lawyer sensitive data can be protected, and the safety of chaining of the lawyer sensitive data is improved.
The blockchain is a blockchain network, and the blockchain may also be referred to as a consensus network or a Distributed Ledger (DLS), and in some embodiments, the blockchain may be a private chain or a federation chain, and so on.
In some embodiments, the client may be a self-service terminal device, a mobile terminal (i.e., a smartphone), a display, a desktop computer, a tablet computer, a laptop computer, a digital assistant, or a smart wearable device, etc. Wherein, wearable equipment of intelligence can include intelligent bracelet, intelligent wrist-watch, intelligent glasses or intelligent helmet etc.. Of course, the client is not limited to the electronic device with a certain entity, and may also be software (e.g. APP) running in the electronic device.
In some embodiments, the server may be an electronic device with computing and network interaction functions; software that runs in the electronic device and provides business logic for data processing and network interaction is also possible. The server side can perform data interaction with the client side.
The data source may typically be multiple and collectively may provide both the attorney's open data and sensitive data.
In some embodiments, the data sources may include, for example, but are not limited to, judicial examination systems, administrative approval systems, bar administration systems, law association member systems, bar on-line entrusting systems, and the like.
An embodiment of the present specification provides a lawyer sensitive data privacy upload method, which may be applied to the above-mentioned service end, and referring to fig. 2, in some embodiments, the lawyer sensitive data privacy upload method may include the following steps:
Both lawyer open data and lawyer sensitive data may be used for surname courts in order to facilitate the application of lawyer data (e.g., generating lawyer credit reports, or performing analysis or mining of lawyer data, etc.).
Lawyer patency data refers to lawyer data that can be disclosed in the clear, i.e., no sensitive data or information is included in the lawyer patency data. For example, in some embodiments, attorney patency data may include, for example, judicial exam performances, practice achievement certificates, attorney qualifications, attorney practice certificates, annual assessment information, and the like.
The attorney ID may be used to uniquely identify the attorney, and in some embodiments, the attorney ID may be a attorney license number.
In some embodiments, forming an open data set with a lawyer ID as a primary key based on lawyer open data may include:
acquiring lawyer openness data from a data source, and extracting key information from the lawyer openness data (the key information can represent main content or characteristics of the lawyer openness data in a simplified mode); the key information is quantized (e.g., data normalization, numerical conversion, etc.), and then the quantized key information is ul-stored with the attorney ID as the primary key. In an exemplary embodiment, an open data set (partial fields) with attorney ID as the primary key may be as shown in table 1 below.
TABLE 1
Both the random parameter and the local random number are used to obfuscate or homomorphically encrypt lawyer sensitive data. In some embodiments, the random parameter may be two base points (i.e., fixed points) with a larger prime number of order randomly selected from a given elliptical curve. In some embodiments, the random number may be a point other than G and H randomly selected by the server from the designated elliptic curve, so as to avoid leakage of lawyer sensitive data plaintext.
And 203, generating a commitment data set with a lawyer ID as a main key based on a homomorphic commitment algorithm by taking the random parameter, the local random number and the quantized lawyer sensitive data as input, and storing the commitment data set in the blockchain.
Lawyer sensitive data refers to lawyer data that is not suitable for disclosure in the clear text outside; and the quantified lawyer sensitive data is the lawyer sensitive data after the quantification processing. In some embodiments, attorney sensitive data may include, for example, but not limited to, attorney penalty information, authorized commitment case quantity, case victory rate, and the like.
The commitment algorithm is a commitment scheme in the field of cryptography or a commitment agreement commitment algorithm, and is a two-stage interaction agreement involving two parties, namely a commitment party and a receiving party. The first stage is the commitment stage, where the commitment party selects a message m and sends it to the receiving party in the form of ciphertext, meaning that the commitment party does not change m. The second phase is a verification phase (also called an open phase, a disclosure phase), in which the committer publishes the message m and a blinding factor (corresponding to a key) to verify whether the message m is consistent with the message received in the commit phase. The commitment algorithm has two basic properties: hiding (Hiding) and Binding (Binding). The concealment is that the commitment value does not reveal any information about the message m; binding means that no malicious committee can open a commitment as a non-m message and the verification is passed, i.e. the receiver can be sure that m is the message corresponding to the commitment. The homomorphic committing algorithm is a committing scheme or committing protocol with homomorphic properties (e.g., homomorphic addition properties, etc.).
Compared with the hash uplink, the homomorphic acceptance uplink for the sensitive data can not only prove the attribution of the sensitive data, but also protect the privacy of the sensitive data, namely, when acceptance verification is carried out subsequently, the acceptance can be proved (namely, an authentication requester is led to believe the acceptance of the sensitive data) under the condition that a lawyer is not informed of the clear text of the sensitive data based on a homomorphic acceptance algorithm.
The petersen commitment (Pedersen commitment) is a homomorphic commitment protocol that satisfies perfect hiding, which does not rely on any difficult assumptions, and computational binding which relies on Discrete Logarithm Assumptions (DLA). Thus, in some embodiments, applying the peadson commitment to lawyer sensitive data privacy upload is a preferred option, i.e., a commitment data set keyed primarily by lawyer ID can be generated based on the peadson commitment. However, it should be understood that the peterson commitment is merely an exemplary illustration, and in other embodiments, any other suitable homomorphic commitment algorithm may be selected as desired, such as a quadratic residue based bit commitment, a bilinear group based fully hidden trapdoor commitment, a bilinear group based unconditional binding commitment, and so on. Therefore, in this specification, the selection of which homomorphic commitment algorithm generates the commitment data set with the lawyer ID as the main key is not limited uniquely.
In some embodiments, generating a commitment data set keyed primarily by lawyer ID based on the pearson commitment may include:
according to the formula
Generating a commitment data set with the lawyer ID as a main key;
wherein the content of the first and second substances,
is the firstiThe first of an individual lawyerjThe commitment value of the individual lawyer sensitive data,
is the firstiThe first of an individual lawyerjThe data that is sensitive to the individual attorneys,
is as followsiThe first of an individual lawyerjThe local random number assigned by the individual lawyer sensitive data,GandHtwo selected from a designated elliptic curve are taken as base points of the random parameter.
For each sensitive data of each lawyer, after homomorphic encryption calculation is carried out according to the formula, the output commitment values are all ciphertext, and the ciphertext can form a commitment data set with the lawyer ID as a main key.
In an exemplary embodiment, the commitment data set (partial fields) with attorney ID as the primary key may be as shown in table 2 below.
TABLE 2
Obviously, the commitment value in table 2 is obtained by homomorphically encrypting the real data, so that the user can not deduce the real data from the commitment value and see the real data after uplink, thereby improving the security of lawyer sensitive data uplink.
And step 204, receiving a credit report generation request carrying the target attorney ID and the evaluation dimension.
When a user wants to know the credit condition of a lawyer, a credit report generation request carrying a target lawyer ID can be sent to the server side through the block chain network. Accordingly, the server may obtain a credit report generation request with a target attorney ID from the blockchain network.
Further, litigation cases may be divided into different types in different dimensions. For example, litigation cases may be divided into civil cases, criminal cases, administrative cases, and the like, by nature and by legal dimensions. Since the same attorney may be good at handling a particular type of case, when a user wishes to know the credit status of a attorney in an evaluation dimension, a credit report generation request carrying the target attorney ID and the evaluation dimension may be sent to the server via the blockchain network.
For example, taking table 1 and table 2 as examples, if a credit report generation request carries a target attorney ID of 1230000001 and carries an evaluation dimension of a civil case, the field values of row 2 in table 1 and the field values of row 2 in table 1 corresponding to "penalty times", "civil complaint rate" and "civil committee amount" are all relevant data corresponding to a civil case with attorney ID of 1230000001.
It should be noted that in the embodiment of the present specification, the service end is configured with a lawyer credit evaluation model in advance. Therefore, the relevant data of the target attorney ID extracted from the open data set and the committed data set, which corresponds to the evaluation dimension, should be the input data required by the attorney credit evaluation model.
In some embodiments, the attorney credit evaluation model may be represented as
(ii) a Wherein the content of the first and second substances,
is the firstiEach attorney corresponding to a credit evaluation index value for the evaluation dimension,
is the firstiThe individual lawyers correspond to the first of the evaluation dimensionsjThe commitment value of the individual lawyer sensitive data,
is that
The weight of (a) is determined,Mis as followsiEach attorney corresponds to the attorney sensitive data quantity of the evaluation dimension,
is the firstiThe individual lawyers correspond to the first of the evaluation dimensionskThe data was opened by the individual lawyer,
is that
The weight of (a) is determined,Nis as followsiEach attorney corresponds to the attorney patency data quantity of the evaluation dimension. According to the lawyer credit evaluation model, lawyer open data and lawyer sensitive data are comprehensively considered, different weights are set according to different influence degrees of each lawyer data on lawyer credit, and accordingly more objective and accurate lawyer credit evaluation can be obtained. Of course, in other embodiments, any other suitable attorney credit evaluation model may be used, as desired.
Taking the lawyer credit evaluation model as an example, generating a credit report of the target lawyer ID corresponding to the evaluation dimension according to the relevant data may include:
inputting the relevant data of the target lawyer ID into a formula
Generating a credit assessment index value for the target attorney ID corresponding to the assessment dimension; the credit assessment metric values are then automatically populated into a credit report template corresponding to the assessment dimension, thereby generating a credit report for which the target attorney ID corresponds to the assessment dimension.
Referring to fig. 3, another lawyer sensitive data privacy upload method provided in the present specification may be applied to the above-mentioned service end, and the lawyer sensitive data privacy upload method may include the following steps:
Compared with the embodiment shown in fig. 2, the embodiment shown in fig. 3 has an additional step 207, i.e., a step of verifying the commitment is added. In some embodiments, in addition to the credit rating indicator value, the credit report may include: credit report input data and lawyer credit evaluation model. Because the credit report input data contains the commitment value obtained by homomorphically encrypting the real sensitive data (namely, the real sensitive data is invisible to the user), if the user suspects the authenticity of the commitment value, the service side can facilitate the user to carry out commitment verification by adding the credit report input data and a lawyer credit evaluation model into the credit report. Thus, when a commitment validation request for a credit report is received, the server performs commitment disclosure (i.e., sending the user the sum of lawyer-sensitive data and the sum of random numbers for each commitment value in the report input data) based on a homomorphic commitment algorithm.
For example, in an exemplary embodiment, if the lawyer sensitive data upon which a credit report is based is data v1, v 2; the random parameters corresponding to v1 and v2 are G, H; the local random number corresponding to v1 is r1, and the local random number corresponding to v2 is r 2; c1(r1, v1) is the commitment value of v1 using the local random number r1, C2(r2, v2) is the commitment value of v2 using the local random number r 2; then, according to the peterson commitment, when receiving a commitment verification request for a credit report, the server may send the sum of v1 and v2 (i.e., the sum result of v1+ v2) and the sum of r1 and r2 (i.e., the sum result of r1+ r2) to the client, and since G and H are disclosed as a tuple, the client may calculate C3(r1+ r2, v1+ v2) = (r1+ r2) G + (v1+ v2) H = r 1G + v 1H + r 2G + v 2H;
if C3(r1+ r2, v1+ v2) = (r1+ r2) G + (v1+ v2) H = r1 × G + v1 × H + r2 × G + v2 × H = C1(r1, v1) + C2(r2, v2) holds, the commitment is verified; otherwise, the commitment is not verified.
While the process flows described above include operations that occur in a particular order, it should be appreciated that the processes may include more or less operations that are performed sequentially or in parallel (e.g., using parallel processors or a multi-threaded environment).
In accordance with the lawyer sensitive data privacy upload method, an embodiment of the present disclosure further provides a lawyer sensitive data privacy upload device, which may be configured on the service end, as shown in fig. 4, in some embodiments, the lawyer sensitive data privacy upload device may include:
an open data forming module 41, configured to form an open data set with a lawyer ID as a main key based on lawyer open data, and store the open data set in the blockchain;
a random data obtaining module 42, configured to obtain a random parameter and a local random number;
a commitment certification generating module 43, configured to generate a commitment data set with a lawyer ID as a main key based on a homomorphic commitment algorithm and store the commitment data set in the blockchain, with the random parameter, the local random number, and the quantized lawyer sensitive data as inputs;
a report request receiving module 44, configured to receive a credit report generation request carrying a target lawyer ID and an evaluation dimension;
a relevant data extraction module 45, configured to extract relevant data of the target lawyer ID corresponding to the evaluation dimension from the open data set and the committed data set;
a credit report generation module 46, configured to generate a credit report of the target attorney ID corresponding to the evaluation dimension according to the relevant data, and store the credit report in the block chain for user access.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functions of the various elements may be implemented in the same one or more software and/or hardware implementations of the present description.
It should be noted that, in the embodiments of the present specification, the user information (including, but not limited to, user device information, user personal information, etc.) and the data (including, but not limited to, data for analysis, stored data, presented data, etc.) referred to are information and data that are authorized by the user and are sufficiently authorized by the parties.
Embodiments of the present description also provide a computer device. As shown in FIG. 5, in some embodiments of the present description, the computer device 502 may include one or more processors 504, such as one or more Central Processing Units (CPUs) or Graphics Processors (GPUs), each of which may implement one or more hardware threads. The computer device 502 may also include any memory 506 for storing any kind of information such as code, settings, data, etc., and in a specific embodiment, a computer program running on the memory 506 and on the processor 504, which when executed by the processor 504, may perform the instructions of the lawyer sensitive data privacy chain credentialing method as described in any of the above embodiments. For example, and without limitation, memory 506 may include any one or more of the following in combination: any type of RAM, any type of ROM, flash memory devices, hard disks, optical disks, etc. More generally, any memory may use any technology to store information. Further, any memory may provide volatile or non-volatile retention of information. Further, any memory may represent fixed or removable components of computer device 502. In one case, when the processor 504 executes the associated instructions, which are stored in any memory or combination of memories, the computer device 502 can perform any of the operations of the associated instructions. The computer device 502 also includes one or more drive mechanisms 508, such as a hard disk drive mechanism, an optical disk drive mechanism, etc., for interacting with any memory.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), computer-readable storage media, and computer program products of some embodiments of the specification. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processor to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processor, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processor to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processor to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computer device includes one or more processors (CPUs), input/output interfaces, a network interface, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both permanent and non-permanent, removable and non-removable media, may implement the information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium which can be used to store information that can be accessed by a computer device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
The embodiments of this specification may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The described embodiments may also be practiced in distributed computing environments where tasks are performed by remote processors that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
It should also be understood that, in the embodiment of the present specification, the term "and/or" is only one kind of association relation describing an associated object, and means that three kinds of relations may exist. For example, a and/or B, may represent: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of an embodiment of the specification. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A lawyer sensitive data privacy chain credentialing method, comprising:
forming an open data set with a lawyer ID as a main key based on lawyer open data and storing the open data set in a block chain;
acquiring random parameters and local random numbers;
taking the random parameter, the local random number and the quantized lawyer sensitive data as input, generating a commitment data set with a lawyer ID as a main key based on a homomorphic commitment algorithm, and storing the commitment data set in the block chain;
receiving a credit report generation request carrying a target attorney ID and an evaluation dimension;
extracting relevant data of the target attorney ID corresponding to the evaluation dimension from the open data set and the commitment data set;
generating a credit report for the target lawyer ID corresponding to the evaluation dimension from the relevant data and storing it in the blockchain for user access.
2. The lawyer sensitive data privacy chaining verification method of claim 1 wherein the random parameter, the local random number and the quantified lawyer sensitive data are used as input to generate a commitment data set with a lawyer ID as a primary key based on a homomorphic commitment algorithm, comprising:
and generating a commitment data set with the lawyer ID as a main key based on the Pedson commitment by taking the random parameter, the local random number and the quantized lawyer sensitive data as input.
3. The lawyer sensitive data privacy chaining validation method of claim 2 wherein said generating a commitment data set keyed primarily by lawyer ID based on a pearson commitment comprises:
according to the formula
Generating a commitment data set with the lawyer ID as a main key;
wherein the content of the first and second substances,
is the firstiThe first of an individual lawyerjThe commitment value of the individual lawyer sensitive data,
is the firstiFirst of each lawyerjThe data that is sensitive to the individual attorneys,
is as aiThe first of an individual lawyerjThe local random number assigned by the individual lawyer sensitive data,GandHtwo selected from a designated elliptic curve are taken as base points of the random parameter.
4. The lawyer sensitive data privacy upload certificate method of claim 1, wherein generating a credit report for the target lawyer ID corresponding to the evaluation dimension based on the correlation data comprises:
inputting the relevant data of the target lawyer ID into a formula
Generating a credit assessment index value for the target attorney ID corresponding to the assessment dimension; wherein the content of the first and second substances,
is the firstiEach attorney corresponding to a credit evaluation index value for the evaluation dimension,
is the firstiThe individual lawyers correspond to the first of the evaluation dimensionsjThe commitment value of the individual lawyer sensitive data,
is that
The weight of (a) is determined,Mis as followsiEach attorney corresponds to the attorney sensitive data quantity of the evaluation dimension,
is the firstiThe individual lawyers correspond to the first of the evaluation dimensionskThe data was opened by the individual lawyer,
is that
The weight of (a) is determined,Nis as followsiEach attorney corresponds to an attorney openness data quantity of the evaluation dimension;
and filling the credit evaluation index value into a credit report template corresponding to the evaluation dimension, and generating a credit report of which the target attorney ID corresponds to the evaluation dimension.
5. The lawyer sensitive data privacy crediting method of claim 1, wherein the credit report further comprises:
credit report input data;
lawyer credit evaluation model.
6. The lawyer sensitive data privacy upload credentialing method of claim 5, wherein after generating a credit report based on said associated data and storing it in said blockchain, further comprising:
performing commitment disclosure based on a homomorphic commitment algorithm when a commitment validation request for the credit report is received.
7. A lawyer sensitive data privacy chain credentialing apparatus, comprising:
the open data forming module is used for forming an open data set with a lawyer ID as a main key based on lawyer open data and storing the open data set in a block chain;
the random data acquisition module is used for acquiring random parameters and local random numbers;
a commitment certification generation module, configured to generate a commitment data set with a lawyer ID as a main key based on a homomorphic commitment algorithm with the random parameter, the local random number, and quantized lawyer sensitive data as inputs, and store the commitment data set in the blockchain;
the report request receiving module is used for receiving a credit report generation request carrying the ID and the evaluation dimension of the target lawyer;
a relevant data extraction module for extracting relevant data of the target lawyer ID corresponding to the evaluation dimension from the open data set and the committed data set;
and the credit report generation module is used for generating a credit report of the target lawyer ID corresponding to the evaluation dimension according to the related data, and storing the credit report in the block chain for the user to access.
8. A computer device comprising a memory, a processor, and a computer program stored on the memory, wherein the computer program, when executed by the processor, performs the instructions of the method of any one of claims 1-6.
9. A computer storage medium on which a computer program is stored, characterized in that the computer program, when being executed by a processor of a computer device, executes instructions of a method according to any one of claims 1-6.
10. A computer program product, characterized in that the computer program product comprises a computer program which, when executed by a processor, executes instructions for a method according to any one of claims 1-6.
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