CN116680484A - User information query protection method, device, equipment, medium and program product - Google Patents

User information query protection method, device, equipment, medium and program product Download PDF

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Publication number
CN116680484A
CN116680484A CN202310659768.9A CN202310659768A CN116680484A CN 116680484 A CN116680484 A CN 116680484A CN 202310659768 A CN202310659768 A CN 202310659768A CN 116680484 A CN116680484 A CN 116680484A
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Prior art keywords
user
social network
query request
similarity
noise
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魏博言
郭相林
刘微
张春雨
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Industrial and Commercial Bank of China Ltd ICBC
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Industrial and Commercial Bank of China Ltd ICBC
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Priority to CN202310659768.9A priority Critical patent/CN116680484A/en
Publication of CN116680484A publication Critical patent/CN116680484A/en
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9536—Search customisation based on social or collaborative filtering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/02—Knowledge representation; Symbolic representation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/04—Inference or reasoning models
    • G06N5/041—Abduction
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

Abstract

The disclosure provides a user information query protection method, device, electronic equipment, medium and computer program product based on a social network graph. The method and the device can be used in the technical fields of artificial intelligence and information security. The user information query protection method based on the social network graph comprises the following steps: acquiring a query request, wherein the query request comprises personal information of a queried user and/or release content of the queried user; calculating the overlapping similarity between the user initiating the query request and the queried user based on a pre-constructed social network graph; determining noise according to the set privacy budget and the overlapped similarity; and adding the noise to the reply information made for the query request to obtain private data.

Description

User information query protection method, device, equipment, medium and program product
Technical Field
The present disclosure relates to the field of artificial intelligence and information security technologies, and more particularly, to a social network graph-based user information query protection method, apparatus, electronic device, medium, and computer program product.
Background
In the existing social network, when a certain user inquires other strange user information, if the inquired user does not do any authority setting, all the issued information including personal information, resident places, login IP addresses, constellation hobbies, personal habits and the like can be seen, and if the inquirer has certain background knowledge, such as friend circle intersection with the inquired user and the like, the privacy of the inquired user can be easily positioned through inference. In the prior art, the privacy protection method controls the authority of the personal information and the release content by the inquired user, such as the disclosure range of the personal information and the release content.
Disclosure of Invention
In view of this, the present disclosure provides a social network graph-based user information query protection method, apparatus, electronic device, computer-readable storage medium, and computer program product that enable information to be protected against background knowledge attacks on the premise of being readable, usable, and transmissible, and that protect user privacy.
One aspect of the present disclosure provides a user information query protection method based on a social network graph, including: acquiring a query request, wherein the query request comprises personal information of a queried user and/or release content of the queried user; calculating the overlapping similarity between the user initiating the query request and the queried user based on a pre-constructed social network graph; determining noise according to the set privacy budget and the overlapped similarity; and adding the noise to the reply information made for the query request to obtain private data.
According to the user information query protection method based on the social network graph, the overlapping similarity between the user initiating the query request and the queried user can be calculated through the pre-constructed social network graph; noise can be determined according to the set privacy budget and the overlapping similarity; adding noise to the reply information made to the query request results in protected private data. According to the method, the association degree of two users is constructed through the overlapped similarity, the higher the association degree is, the higher the possibility of background knowledge attack is, the smaller privacy budget is further allocated, the larger disturbance noise is added to the personal information and the release content of the queried user, and the probability that the privacy is positioned is reduced. In addition, the privacy budget is dynamically allocated according to the overlapping similarity, so that the information can be prevented from being attacked by background knowledge on the premise of readability, availability and transmissibility, and the privacy of the user is protected.
In some embodiments, the pre-building a social network graph includes: acquiring social data, wherein the social data comprises user information and association relation information between users; and constructing a social network graph according to the social data, wherein nodes of the social network graph are constructed according to the user information, and edges of the social network graph are constructed according to the association relation information.
In some embodiments, when the social data is updated, the social network graph is updated according to the updated social data.
In some embodiments, the calculating, based on the pre-constructed social network graph, the overlapping similarity between the user who initiated the query request and the queried user includes: determining the number of users related to the user initiating the query request according to the social network map to obtain a first related number; determining the number of users related to the queried user according to the social network graph to obtain a second related number; determining the number of users commonly related to the user initiating the query request and the queried user according to the social network map to obtain a third related number; and calculating the overlapping similarity between the user initiating the query request and the queried user according to the first correlation quantity, the second correlation quantity and the third correlation quantity.
In some embodiments, the determining noise according to the set privacy budget and the overlapping similarity comprises: determining noise distribution according to the set privacy budget and the overlapped similarity; and the noise obeys the noise distribution.
In some embodiments, the determining the noise distribution according to the set privacy budget and the overlapping similarity comprises: mapping the overlapped similarity to obtain a mapping value; and determining a noise distribution from the set privacy budget and the mapping value.
Another aspect of the present disclosure provides a user information query protection device based on a social network graph, including: the acquisition module is used for executing an acquisition inquiry request, wherein the inquiry request comprises personal information of the inquired user and/or release content of the inquired user; the computing module is used for executing the calculation of the overlapping similarity between the user initiating the query request and the queried user based on the pre-constructed social network graph; a determining module for performing a determination of noise based on the set privacy budget and the overlapping similarity; and the adding module is used for adding the noise to the reply information made for the query request to obtain the privacy data.
Another aspect of the present disclosure provides an electronic device comprising one or more processors and one or more memories, wherein the memories are configured to store executable instructions that, when executed by the processors, implement the method as described above.
Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions that, when executed, are configured to implement a method as described above.
Another aspect of the present disclosure provides a computer program product comprising a computer program comprising computer executable instructions which, when executed, are for implementing a method as described above.
Drawings
The above and other objects, features and advantages of the present disclosure will become more apparent from the following description of embodiments thereof with reference to the accompanying drawings in which:
FIG. 1 schematically illustrates an exemplary system architecture to which methods, apparatuses may be applied according to embodiments of the present disclosure;
FIG. 2 schematically illustrates a flow chart of a user information query protection method based on a social network graph, in accordance with an embodiment of the present disclosure;
FIG. 3 schematically illustrates a flow diagram of pre-building a social network graph, according to an embodiment of the disclosure;
FIG. 4 schematically illustrates a flow chart of a social network graph, according to an embodiment of the present disclosure;
FIG. 5 schematically illustrates a flow chart for computing overlapping similarities between a user initiating a query request and a queried user based on a pre-constructed social network graph, in accordance with an embodiment of the present disclosure;
FIG. 6 schematically illustrates a flow chart of determining noise according to a set privacy budget and overlap similarity according to an embodiment of the present disclosure;
FIG. 7 schematically illustrates a flow chart of determining a noise profile based on a set privacy budget and overlap similarity according to an embodiment of the present disclosure;
FIG. 8 schematically illustrates a block diagram of a social network graph based user information query protection device, in accordance with an embodiment of the present disclosure;
fig. 9 schematically illustrates a block diagram of an electronic device according to an embodiment of the disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is only exemplary and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the concepts of the present disclosure.
In the technical scheme of the disclosure, the acquisition, storage, application and the like of the related personal information of the user all conform to the regulations of related laws and regulations, necessary security measures are taken, and the public order harmony is not violated. In the technical scheme of the disclosure, the processes of acquiring, collecting, storing, using, processing, transmitting, providing, disclosing, applying and the like of the data all conform to the regulations of related laws and regulations, necessary security measures are adopted, and the public order harmony is not violated.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," "includes," and/or "including," when used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
Where a formulation similar to at least one of "A, B or C, etc." is used, in general such a formulation should be interpreted in accordance with the ordinary understanding of one skilled in the art (e.g. "a system with at least one of A, B or C" would include but not be limited to systems with a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.). The terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more of the described features.
In the existing social network, when a certain user inquires other strange user information, if the inquired user does not do any authority setting, all the issued information including personal information, resident places, login IP addresses, constellation hobbies, personal habits and the like can be seen, and if the inquirer has certain background knowledge, such as friend circle intersection with the inquired user and the like, the privacy of the inquired user can be easily positioned through inference. In the prior art, the privacy protection method controls the authority of the personal information and the release content by the inquired user, such as the disclosure range of the personal information and the release content. The prior art scheme is mainly based on an authority management and control mechanism, controls the spreading range of user information and release content, and has the defect that the exposure degree and the spreading degree of the content are reduced, so that the sharing desire of users is reduced. In terms of data security, for information which is not subjected to authority management and control, a querying user can attack by combining background knowledge, and the privacy of the queried user is positioned.
Embodiments of the present disclosure provide a social network graph-based user information query protection method, apparatus, electronic device, computer-readable storage medium, and computer program product. The user information query protection method based on the social network graph comprises the following steps: acquiring a query request, wherein the query request comprises personal information of a queried user and/or release content of the queried user; based on a pre-constructed social network graph, calculating the overlapping similarity between a user initiating a query request and a queried user; determining noise according to the set privacy budget and the overlapping similarity; and adding noise to the reply information made for the query request to obtain the private data.
It should be noted that the method, apparatus, electronic device, computer readable storage medium and computer program product for protecting user information query based on social network graph of the present disclosure may be used in the fields of artificial intelligence and information security technology, and may also be used in any fields other than the fields of artificial intelligence and information security technology, such as financial fields, where the fields of the present disclosure are not limited.
FIG. 1 schematically illustrates an exemplary system architecture 100 in which social network graph-based user information query protection methods, apparatus, electronic devices, computer-readable storage media, and computer program products may be applied, in accordance with embodiments of the present disclosure. It should be noted that fig. 1 is only an example of a system architecture to which embodiments of the present disclosure may be applied to assist those skilled in the art in understanding the technical content of the present disclosure, but does not mean that embodiments of the present disclosure may not be used in other devices, systems, environments, or scenarios.
As shown in fig. 1, a system architecture 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as shopping class applications, web browser applications, search class applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only) may be installed on the terminal devices 101, 102, 103.
The terminal devices 101, 102, 103 may be a variety of electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.
The server 105 may be a server providing various services, such as a background management server (by way of example only) providing support for websites browsed by users using the terminal devices 101, 102, 103. The background management server may analyze and process the received data such as the user request, and feed back the processing result (e.g., the web page, information, or data obtained or generated according to the user request) to the terminal device.
It should be noted that the user information query protection method based on the social network graph provided in the embodiments of the present disclosure may be generally executed by the server 105. Accordingly, the user information query protection device based on social network graph provided by the embodiments of the present disclosure may be generally disposed in the server 105. The user information query protection method based on the social network profile provided by the embodiments of the present disclosure may also be performed by a server or a server cluster that is different from the server 105 and is capable of communicating with the terminal devices 101, 102, 103 and/or the server 105. Accordingly, the user information query protection device based on social network profile provided by the embodiments of the present disclosure may also be provided in a server or a server cluster different from the server 105 and capable of communicating with the terminal devices 101, 102, 103 and/or the server 105.
It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
The user information query protection method based on the social network map according to the embodiment of the present disclosure will be described in detail below based on the scenario described in fig. 1 through fig. 2 to 7.
Fig. 2 schematically illustrates a flowchart of a user information query protection method based on a social network graph according to an embodiment of the disclosure.
As shown in fig. 2, the social network graph-based user information query protection method of this embodiment includes operations S210 to S240.
In operation S210, a query request is acquired, wherein the query request includes personal information of the queried user and/or distribution content of the queried user.
In operation S220, an overlapping similarity between the user who initiated the query request and the queried user is calculated based on the social network graph constructed in advance.
Note that, the overlap similarity (Overlap Similarity) is an extension of the jaccard similarity, and represents the degree of similarity of two sets by dividing the intersection size of the two sets by the smaller set size of the two sets. The value range of the overlapping similarity is [0,1], and the larger the value is, the more similar is. In the disclosure, a user initiating a query request and a queried user are respectively regarded as two sets, neighbor users connected with the users are regarded as elements in the sets, intersection elements are associated users which are common to the user initiating the query request and the queried user, and the similarity degree of two target users can be known based on overlapping similarity by combining a user relationship network topological graph.
As one implementation, as shown in fig. 3, pre-building a social network graph may include operations S310 and S320.
In operation S310, social data is acquired, wherein the social data includes user information and association relationship information between users. Wherein, the user information can include name, gender, address, location, position, constellation hobbies, personal habits and the like; the association relationship information between users may include relatives, colleagues, friends, trade relationships, and the like.
In operation S320, a social network graph is constructed according to social data, wherein nodes of the social network graph are constructed according to user information, edges of the social network graph are constructed according to association relationship information, and the social network graph may be as shown in fig. 4, which is merely an exemplary illustration and is not to be construed as limiting the present disclosure. The pre-construction of the social network profile may be facilitated through operations S310 and S320.
In some specific examples, when social data is updated, the social network graph is updated according to the updated social data. Therefore, the effectiveness of the data in the social network map can be ensured, so that the accuracy of the data obtained from the social network map is good, and the effectiveness is high.
As an implementation manner, as shown in fig. 5, operation S220 calculates the overlapping similarity between the user who initiates the query request and the queried user based on the pre-constructed social network graph, including operations S221 to S224.
In operation S221, a number of users related to the user who initiated the query request is determined according to the social network graph, resulting in a first related number.
In operation S222, the number of users related to the queried user is determined according to the social network graph, and a second related number is obtained.
In operation S223, the number of users commonly related to the user who initiated the query request and the queried user is determined according to the social network graph, and a third related number is obtained.
In operation S224, the overlapping similarity between the user who initiated the query request and the queried user is calculated according to the first, second, and third correlation numbers.
Continuing to take the social network graph example in fig. 4, assuming that the user a is the user initiating the query request, and the user B is the queried user, thus obtaining that the users having the side relationship with the user a have the user C, the user D and the user F, and the total three users, namely, the first related number is 3; the users with side relation with the user B can be obtained to have a user C, a user D and a user E, wherein the total number of the three users is 3; and then, the users commonly related to the user A and the user B are the user C and the user D, and the total number of the two users, namely the third related number is 2.
For example, overlapping similarity can be used with S 0 (A, B), the first correlation number may be represented by |A|, the second correlation number may be represented by |B|, the third correlation number may be represented by |A n B|, and the overlapping similarity may be obtained by the formula (1).
Thus, through operations S221 to S224, it is possible to facilitate calculation of the overlapping similarity between the user who initiates the query request and the queried user based on the social network graph constructed in advance.
In operation S230, noise is determined according to the set privacy budget and the overlapping similarity.
As one possible implementation, as shown in fig. 6, operation S230 determines noise according to the set privacy budget and the overlapping similarity, including operation S231 and operation S232.
In operation S231, a noise distribution is determined according to the set privacy budget and the overlap similarity.
In some specific examples, as shown in fig. 7, operation S231 determines a noise distribution according to the set privacy budget and the overlap similarity, including operations S2311 and S2312.
In operation S2311, the overlapping similarity is subjected to a mapping process to obtain a mapped value. Wherein the mapping value may be expressed as S' 0 The mapped value can be obtained by the formula (2).
S′ 0 ＝sigmoid(S 0 ) (2)
Wherein S is 0 Overlapping similarities.
In operation S2312, a noise profile is determined according to the set privacy budget and the mapping value, e.g., the noise profile may beEpsilon is the privacy budget set.
Since the initial privacy budget value is generally smaller, it is usually in the range of [0,1]Within the interval, the similarity S will be overlapped 0 The mapping process is carried out, and the noise distribution is determined according to the mapping value, so that the display effect and the rationality of the noise distribution are better. Determining the noise profile according to the set privacy budget and the overlap similarity may be facilitated through operations S2311 and S2312.
In operation S232, the noise follows the noise distribution. Thus, determining noise according to the set privacy budget and the overlap similarity can be facilitated through operation S231 and operation S232.
In operation S240, noise is added to the reply information made for the query request, resulting in privacy data.
According to the user information query protection method based on the social network graph, the overlapping similarity between the user initiating the query request and the queried user can be calculated through the pre-constructed social network graph; noise can be determined according to the set privacy budget and the overlapping similarity; adding noise to the reply information made to the query request results in protected private data. According to the method, the association degree of two users is constructed through the overlapped similarity, the higher the association degree is, the higher the possibility of background knowledge attack is, the smaller privacy budget is further allocated, the larger disturbance noise is added to the personal information and the release content of the queried user, and the probability that the privacy is positioned is reduced. In addition, the privacy budget is dynamically allocated according to the overlapping similarity, so that the information can be prevented from being attacked by background knowledge on the premise of readability, availability and transmissibility, and the privacy of the user is protected.
Based on the user information query protection method based on the social network graph, the disclosure also provides a user information query protection device based on the social network graph. The user information query protection device 10 based on the social network profile will be described in detail below with reference to fig. 8.
Fig. 8 schematically illustrates a block diagram of a user information query protection device 10 based on a social network profile, according to an embodiment of the present disclosure.
The user information query protection device 10 based on the social network map comprises an acquisition module 1, a calculation module 2, a determination module 3 and an addition module 4.
Acquisition module 1, acquisition module 1 is configured to perform operation S210: a query request is obtained, wherein the query request comprises personal information of the queried user and/or release content of the queried user.
Calculation module 2, calculation module 2 is configured to perform operation S220: based on the pre-constructed social network graph, the overlapping similarity between the user initiating the query request and the queried user is calculated.
A determining module 3, where the determining module 3 is configured to perform operation S230: and determining noise according to the set privacy budget and the overlapping similarity.
The adding module 4, the adding module 4 is configured to perform operation S240: noise is added to the reply information made for the query request, resulting in private data.
According to some embodiments of the present disclosure, the user information query protection device based on a social network graph further includes a construction module, where the construction module is configured to construct the social network graph in advance, and the construction module includes an acquisition unit and a construction unit.
The acquisition unit is used for acquiring social data, wherein the social data comprises user information and association relation information between users.
The construction unit is used for constructing a social network graph according to the social data, wherein nodes of the social network graph are constructed according to the user information, and edges of the social network graph are constructed according to the association relation information.
According to some embodiments of the present disclosure, the building module further includes an updating unit, where the updating unit is configured to update the social network graph according to the updated social data when the social data is updated.
According to some embodiments of the present disclosure, the computing module includes a first determining unit, a second determining unit, a third determining unit, and a computing unit.
The first determining unit is used for determining the number of users related to the user initiating the query request according to the social network map, and obtaining a first related number.
And the second determining unit is used for determining the number of the users related to the queried user according to the social network map to obtain a second related number.
And the third determining unit is used for determining the number of the users commonly related to the user initiating the query request and the queried user according to the social network map to obtain a third related number.
The calculation unit is used for calculating the overlapping similarity between the user initiating the query request and the queried user according to the first correlation quantity, the second correlation quantity and the third correlation quantity.
According to some embodiments of the present disclosure, the determining module includes a fourth determining unit and a slave unit.
And a fourth determining unit for determining a noise distribution according to the set privacy budget and the overlapping similarity.
And the compliance unit is used for noise compliance with noise distribution.
According to some embodiments of the present disclosure, the fourth determination unit comprises a mapping element and a determination element.
And the mapping element is used for mapping the overlapped similarity to obtain a mapping value.
And the determining element is used for determining noise distribution according to the set privacy budget and the mapping value.
According to the user information query protection device 10 based on the social network graph, the overlapping similarity between the user who initiates the query request and the queried user can be calculated through the pre-constructed social network graph; noise can be determined according to the set privacy budget and the overlapping similarity; adding noise to the reply information made to the query request results in protected private data. According to the method, the association degree of two users is constructed through the overlapped similarity, the higher the association degree is, the higher the possibility of background knowledge attack is, the smaller privacy budget is further allocated, the larger disturbance noise is added to the personal information and the release content of the queried user, and the probability that the privacy is positioned is reduced. In addition, the privacy budget is dynamically allocated according to the overlapping similarity, so that the information can be prevented from being attacked by background knowledge on the premise of readability, availability and transmissibility, and the privacy of the user is protected.
In addition, according to the embodiment of the present disclosure, any of the plurality of modules of the acquisition module 1, the calculation module 2, the determination module 3, and the addition module 4 may be incorporated in one module to be implemented, or any of the plurality of modules may be split into a plurality of modules. Alternatively, at least some of the functionality of one or more of the modules may be combined with at least some of the functionality of other modules and implemented in one module.
According to embodiments of the present disclosure, at least one of the acquisition module 1, the calculation module 2, the determination module 3 and the addition module 4 may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in hardware or firmware in any other reasonable way of integrating or packaging the circuits, or in any one of or a suitable combination of any of the three implementations of software, hardware and firmware.
Alternatively, at least one of the acquisition module 1, the calculation module 2, the determination module 3 and the addition module 4 may be at least partially implemented as computer program modules which, when executed, may perform the respective functions.
The user information query protection method based on the social network profile according to the embodiment of the present disclosure is described in detail as follows. It is to be understood that the following description is exemplary only and is not intended to limit the disclosure in any way.
The terms involved in the following examples are explained below.
Differential privacy: the concept of differential privacy comes from the concept of semantic security in cryptography, and the technology ensures that a disclosed output result cannot generate obvious change due to whether an individual is in a data set or not by adding random noise, and gives a quantitative model for the degree of privacy disclosure. It is specifically defined as follows.
For any two adjacent data sets X and X', a randomization algorithm D can be considered to satisfy the following condition.
Pr[D(X)∈S]≤e ε ·Pr[D(X′)∈S]，
Privacy budget: in the above equation, ε is referred to as the privacy budget. When epsilon is small enough, meaning that the availability of data is very low, in practice the parameter will usually take a very small value. The parameters should be set reasonably according to the specific business scenario and the desired requirements of privacy protection.
Overlapping similarity: the set is composed of a plurality of elements, and the elements in the set are unordered and mutually different; the number of elements in set A is the size of set A, denoted as |A|, and the intersection of set A and set B is A n B. The overlapping similarity of sets a and B can be expressed as,
for example, the elements of set A are { B, c, e, c, g }, the elements of set B are { a, B, d, g }, the intersection is A n B is { B, g }, their overlapping similarities can be expressed as,
the patent provides a scheme for desensitizing personal information and release content of users in a social network through a differential privacy technology.
The overlap similarity (Overlap Similarity) is an extension of the Jacquard similarity by dividing the intersection size of two sets by the smaller of the two sets, thus representing the degree of similarity of the two sets. The value range of the overlapping similarity is [0,1], and the larger the value is, the more similar is.
In the embodiment of the disclosure, a query user and a queried user are respectively regarded as two sets, neighbor users connected with the users are regarded as elements in the sets, intersection elements are associated users common to the two users, and the similarity degree of the two target users is calculated based on overlapping similarity by combining a user relationship network topological graph. The method is used for constructing the association degree of two users, the probability of background knowledge attack is higher as the association degree is higher, and further, smaller privacy budget is allocated, and larger disturbance is added to the information and the content of the queried user, so that the probability of locating the privacy is reduced.
The method and the device introduce network topology influence factors of adjacent nodes, and more accurately define the association degree of two individual users in the social network. And the privacy budget is dynamically allocated by combining the overlapped similarity, so that the privacy budget is flexibly scrambled, and the possibility of privacy disclosure of the individual user is reduced. And the privacy budget is dynamically allocated based on the overlapping similarity of the nodes, so that the privacy of the individual user and the propagation degree of the content of the individual user in the social network are flexibly balanced and protected.
In order to achieve the above purpose, the present disclosure provides the following technical solutions.
S101, a user information analysis module.
S102, a user network topological graph construction module.
S103, overlapping the similarity calculation module.
S104, a noise calculation module.
S105, a noise adding module.
S106, returning a result to the module.
S101, analyzing the relevance among users based on the historical activities of the users in the social network, such as whether the users pay attention to each other, whether the users have private chat records, whether the users have transaction records and the like.
S102, constructing a user network topological graph based on the user relevance of S101.
S103, calculating the overlapping similarity between the users, namely, the user association degree. Taking the example of user a and user B, the overlapping similarity is,
wherein A is the querying user, B is the queried user, A and B represent the number of the common related users, A and B represent the number of all the related users of the querying user, B represents the number of all the related users of the queried node, and min (A and B) represent the minimum value of A and B).
S104, according to the overlapping similarity S of S103 0 (A, B) calculating an allocable privacy budget and noise to be added.
Since the privacy budget is generally smaller, usually in the [0,1] interval, the calculated overlapping similarity needs to be mapped first.
S′ 0 ＝sigmoid(S 0 )
Finally, the noise r is calculated, i.e.,
D′(X)＝D(X)+r
wherein r is subject to distributionEpsilon is a given initial privacy budget and can be set to a value of less than 1, such as 0.01.
S105, a noise adding module. And adding the noise generated in the step S104 into the related information and the release content of the queried user.
S106, returning the information generated in S105 to the inquiring user.
Fig. 9 schematically shows a block diagram of an electronic device adapted to implement the above-described method according to an embodiment of the present disclosure.
As shown in fig. 9, an electronic device 900 according to an embodiment of the present disclosure includes a processor 901 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 902 or a program loaded from a storage portion 908 into a Random Access Memory (RAM) 903. The processor 901 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or an associated chipset and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), or the like. Processor 901 may also include on-board memory for caching purposes. Processor 901 may include a single processing unit or multiple processing units for performing the different actions of the method flows according to embodiments of the present disclosure.
In the RAM 903, various programs and data necessary for the operation of the electronic device 900 are stored. The processor 901, the ROM 902, and the RAM 903 are connected to each other by a bus 904. The processor 901 performs various operations of the method flow according to the embodiments of the present disclosure by executing programs in the ROM 902 and/or the RAM 903. Note that the program may be stored in one or more memories other than the ROM 902 and the RAM 903. The processor 901 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in the one or more memories.
According to an embodiment of the disclosure, the electronic device 900 may also include an input/output (I/O) interface 905, the input/output (I/O) interface 905 also being connected to the bus 904. The electronic device 900 may also include one or more of the following components connected to the I/O interface 905: an input section 906 including a keyboard, a mouse, and the like; an output portion 907 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and a speaker; a storage portion 908 including a hard disk or the like; and a communication section 909 including a network interface card such as a LAN card, a modem, or the like. The communication section 909 performs communication processing via a network such as the internet. The drive 910 is also connected to an input/output (I/O) interface 905 as needed. A removable medium 911 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed as needed on the drive 910 so that a computer program read out therefrom is installed into the storage section 908 as needed.
The present disclosure also provides a computer-readable storage medium that may be embodied in the apparatus/device/system described in the above embodiments; or may exist alone without being assembled into the apparatus/device/system. The computer-readable storage medium carries one or more programs which, when executed, implement methods in accordance with embodiments of the present disclosure.
According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example, but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, according to embodiments of the present disclosure, the computer-readable storage medium may include ROM 902 and/or RAM 903 and/or one or more memories other than ROM 902 and RAM 903 described above.
Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowcharts. The program code, when executed in a computer system, causes the computer system to perform the methods of embodiments of the present disclosure.
The above-described functions defined in the system/apparatus of the embodiments of the present disclosure are performed when the computer program is executed by the processor 901. The systems, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.
In one embodiment, the computer program may be based on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted, distributed, and downloaded and installed in the form of a signal on a network medium, via communication portion 909, and/or installed from removable medium 911. The computer program may include program code that may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
In such an embodiment, the computer program may be downloaded and installed from the network via the communication portion 909 and/or installed from the removable medium 911. The above-described functions defined in the system of the embodiments of the present disclosure are performed when the computer program is executed by the processor 901. The systems, devices, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.
According to embodiments of the present disclosure, program code for performing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, such computer programs may be implemented in high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. Programming languages include, but are not limited to, such as Java, c++, python, "C" or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Those skilled in the art will appreciate that the features recited in the various embodiments of the disclosure and/or in the claims may be combined in various combinations and/or combinations, even if such combinations or combinations are not explicitly recited in the disclosure. In particular, the features recited in the various embodiments of the present disclosure and/or the claims may be variously combined and/or combined without departing from the spirit and teachings of the present disclosure. All such combinations and/or combinations fall within the scope of the present disclosure.
The embodiments of the present disclosure are described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the embodiments cannot be used advantageously in combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be made by those skilled in the art without departing from the scope of the disclosure, and such alternatives and modifications are intended to fall within the scope of the disclosure.

Claims (10)

1. The user information query protection method based on the social network graph is characterized by comprising the following steps of:
acquiring a query request, wherein the query request comprises personal information of a queried user and/or release content of the queried user;
calculating the overlapping similarity between the user initiating the query request and the queried user based on a pre-constructed social network graph;
determining noise according to the set privacy budget and the overlapped similarity; and
and adding the noise to the reply information which is made for the query request to obtain the privacy data.
2. The method of claim 1, wherein the pre-building a social network graph comprises:
acquiring social data, wherein the social data comprises user information and association relation information between users; and
and constructing a social network graph according to the social data, wherein nodes of the social network graph are constructed according to the user information, and edges of the social network graph are constructed according to the association relation information.
3. The method of claim 2, wherein when the social data is updated, a social network graph is updated according to the updated social data.
4. The method of claim 1, wherein the computing overlapping similarities between the user initiating the query request and the queried user based on pre-constructed social network graphs comprises:
determining the number of users related to the user initiating the query request according to the social network map to obtain a first related number;
determining the number of users related to the queried user according to the social network graph to obtain a second related number;
determining the number of users commonly related to the user initiating the query request and the queried user according to the social network map to obtain a third related number; and
and calculating the overlapping similarity between the user initiating the query request and the queried user according to the first correlation quantity, the second correlation quantity and the third correlation quantity.
5. The method of claim 1, wherein said determining noise based on the set privacy budget and the overlapping similarity comprises:
determining noise distribution according to the set privacy budget and the overlapped similarity; and
noise follows the noise distribution.
6. The method of claim 5, wherein said determining a noise profile based on the set privacy budget and the overlapping similarities comprises:
mapping the overlapped similarity to obtain a mapping value; and
and determining noise distribution according to the set privacy budget and the mapping value.
7. The utility model provides a user information inquiry protection device based on social network diagram which characterized in that includes:
the acquisition module is used for executing an acquisition inquiry request, wherein the inquiry request comprises personal information of the inquired user and/or release content of the inquired user;
the computing module is used for executing the calculation of the overlapping similarity between the user initiating the query request and the queried user based on the pre-constructed social network graph;
a determining module for performing a determination of noise based on the set privacy budget and the overlapping similarity; and
and the adding module is used for adding the noise to the reply information made for the query request to obtain the privacy data.
8. An electronic device, comprising:
one or more processors;
one or more memories for storing executable instructions which, when executed by the processor, implement the method of any of claims 1-6.
9. A computer readable storage medium, characterized in that the storage medium has stored thereon executable instructions which, when executed by a processor, implement the method according to any of claims 1-6.
10. A computer program product comprising a computer program comprising one or more executable instructions which when executed by a processor implement the method according to any one of claims 1 to 6.
CN202310659768.9A 2023-06-05 2023-06-05 User information query protection method, device, equipment, medium and program product Pending CN116680484A (en)

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