Data processing method and data processing system applied to cloud

Abstract

The embodiment of the present specification provides a data processing method and a data processing system applied to the cloud, wherein the data processing method is applied to the data processing system, the data processing system comprises a first data provider, a bridging data provider and a second data provider; the bridging data provider determines first bridging data and performs pseudo-random processing on the first bridging data to obtain first pseudo-bridging data; the second data provider determines second bridging data and performs pseudo-random processing on the second bridging data to obtain second pseudo-bridging data; and the first data provider obtains target query data corresponding to a first target object set based on the received first pseudo-bridging data in the bridging data provider and the second pseudo-bridging data in the second data provider; so as to solve the problem of privacy leakage in the data query process, improve the data security, and also will not affect the use efficiency of the target query data by the user subsequently.

Description

Technical Field

[0001] The embodiments in this specification relate to the field of computer technology, and in particular to a data processing method. Background Technology

[0002] With the further development of mobile internet and IoT technologies, people's lives are becoming increasingly convenient. They can access information from various sources through their mobile devices, achieving data sharing. Data sharing involves fully utilizing public data to mine and obtain useful information that users want, which inevitably raises the issue of data privacy. For example, in scenarios where two parties search for tagged data, both parties will inevitably obtain the other party's tag data. If the tag data is deemed private, then the data retrieval process is considered a major cause of data leakage, failing to guarantee the security of data queries and potentially impacting user efficiency. Summary of the Invention

[0003] In view of the above, embodiments of this specification provide a data processing method. One or more embodiments of this specification also relate to a data processing system applied in the cloud, a computing device, a computer-readable storage medium, and a computer program, to address the technical deficiencies existing in the prior art.

[0004] According to a first aspect of the embodiments of this specification, a data processing method is provided, applied to a data processing system, the data processing system including a first data provider, a bridging data provider, and a second data provider;

[0005] The bridging data provider determines first bridging data and performs pseudo-random processing on the first bridging data to obtain first pseudo bridging data, wherein the first bridging data is information associated with the query data information of the first target object set;

[0006] The second data provider determines the second bridging data and performs pseudo-random processing on the second bridging data to obtain the second pseudo bridging data, wherein the data type of the second bridging data is consistent with that of the first bridging data;

[0007] The first data provider obtains target query data corresponding to the first target object set based on the first pseudo-bridging data received from the bridging data provider and the second pseudo-bridging data received from the second data provider, wherein the target query data is data of the same type as the data information to be queried.

[0008] According to a second aspect of the embodiments of this specification, a data processing system applied in the cloud is provided, comprising: a first data provider, a bridging data provider, and a second data provider;

[0009] The bridging data provider is configured to determine first bridging data and perform pseudo-random processing on the first bridging data to obtain first pseudo bridging data, wherein the first bridging data is information associated with the query data information of the first target object set;

[0010] The second data provider is configured to determine the second bridging data and perform pseudo-random processing on the second bridging data to obtain the second pseudo bridging data, wherein the data type of the second bridging data is consistent with that of the first bridging data;

[0011] The first data provider is configured to obtain target query data corresponding to the first target object set based on the first pseudo-bridging data received from the bridging data provider and the second pseudo-bridging data received from the second data provider, wherein the target query data is data of the same type as the data information to be queried.

[0012] According to a third aspect of the embodiments of this specification, a computing device is provided, comprising:

[0013] Memory and processor;

[0014] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the above-described data processing method.

[0015] According to a fourth aspect of the embodiments of this specification, a computer-readable storage medium is provided that stores computer-executable instructions, which, when executed by a processor, implement the steps of the data processing method described above.

[0016] According to a fifth aspect of the embodiments of this specification, a computer program is provided, wherein when the computer program is executed in a computer, it causes the computer to perform the steps of the above-described data processing method.

[0017] This specification provides a data processing method according to one embodiment, applied to a data processing system. The data processing system includes a first data provider, a bridging data provider, and a second data provider. The bridging data provider determines first bridging data and performs pseudo-random processing on the first bridging data to obtain first pseudo-bridging data, wherein the first bridging data is information associated with query data information of a first target object set. The second data provider determines second bridging data and performs pseudo-random processing on the second bridging data to obtain second pseudo-bridging data, wherein the second bridging data has the same data type as the first bridging data. The first data provider, based on the first pseudo-bridging data received from the bridging data provider and the second pseudo-bridging data received from the second data provider, obtains target query data corresponding to the first target object set, wherein the target query data is data of the same type as the query data information.

[0018] Specifically, the data processing method provided in the embodiments of this specification can be applied to application scenarios of third-party data query. It can use the bridging data provider and the second data provider to perform pseudo-random processing on the bridging data to obtain pseudo-bridging data, thereby ensuring the privacy protection of the bridging data during the three-party interaction. At the same time, the first data provider uses the pseudo-bridging data as an intermediate "bridge" to realize the query processing of the target query data corresponding to the first target object set. This method can not only support bridging data query between the three parties, but also ensure that each party is unaware of the data held by the other party, so as to solve the privacy leakage problem in the data query process, improve data security, and will not affect the user's subsequent use efficiency of the target query data. Attached Figure Description

[0019] Figure 1 This is a system architecture diagram of a data processing system applied in the cloud, provided by one embodiment of this specification;

[0020] Figure 2 This is a flowchart illustrating a data processing method provided in one embodiment of this specification;

[0021] Figure 3 This is a flowchart of a data processing method provided in another embodiment of this specification;

[0022] Figure 4 This is a schematic diagram of the structure of a data processing system provided in one embodiment of this specification;

[0023] Figure 5 This is a structural block diagram of a computing device provided in one embodiment of this specification. Detailed Implementation

[0024] Many specific details are set forth in the following description to provide a full understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar extensions without departing from the spirit of this specification. Therefore, this specification is not limited to the specific implementations disclosed below.

[0025] The terminology used in one or more embodiments of this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the one or more embodiments of this specification. The singular forms “a,” “described,” and “the” as used in one or more embodiments of this specification and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items.

[0026] It should be understood that although the terms first, second, etc., may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first may also be referred to as second without departing from the scope of one or more embodiments of this specification, and similarly, second may also be referred to as first. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0027] First, the terms and concepts used in one or more embodiments of this specification will be explained.

[0028] Unintentional pseudorandom function (PRF): It is a keyed deterministic function. When the key k is uniformly randomly selected and kept secret, an attacker cannot distinguish PRF(k, ·) from the input-output query. arrive The true random function; it should be noted that the unintentional pseudo-random function is a two-party protocol. One party holds a uniformly randomly generated key k, and the other party holds... By executing an unintentional pseudo-random function, holding Participants receive the pseudo-random function value PRF(k, x) but not other information; participants holding the key do not receive information about... Any information.

[0029] Privacy Set Intersection (PSI): PSI allows multiple parties holding their own sets to jointly compute the intersection of their sets. At the end of the computation, each participating party only obtains the correct intersection and does not obtain any information from the other party's set outside the intersection.

[0030] Mapping table: This can be understood as a data structure that includes two types of data: labels and values. For example, the label is... Value is The mapping table can then be represented as Furthermore, the values ​​corresponding to the same label are the same; that is, if (l, v1) ∈ M and (l, v2) ∈ M, then v1 = v2.

[0031] Bridging data: Data that has intermediate bridging function and is mapped to the data set that the user needs to query. In this specification, it can refer to data of any type, without any limitation on the specific data type.

[0032] Tag-based bridging retrieval: This can be understood as a three-party protocol that uses bridging data to enable cross-platform data retrieval.

[0033] Currently, data retrieval schemes mainly involve two entities: users and servers. The server stores a mapping table from tag L to value V, and users can obtain the corresponding value by submitting a tag. In this regard, privacy-preserving tag retrieval schemes have the following privacy guarantees: (1) users can only obtain the value corresponding to the queried tag and cannot obtain other information; (2) the server cannot know the tags submitted by the user.

[0034] However, in actual projects, there will be more complex tag retrieval requirements involving three entities: two servers and one user. For example, server A stores a mapping table from tag L2 to value V, server B stores a mapping table from tag L1 to L2, and the user holds tag L1. At this time, the user needs to interact with servers A and B to obtain the V value corresponding to tag L1. Based on this, the data processing method provided in the embodiments of this specification can realize the three-way interaction function in the above application scenario, and can be understood as a tag bridging retrieval scheme. Therefore, the privacy protection that can be achieved is as follows: (1) the user can only obtain the V value corresponding to the queried tag; (2) servers A and B cannot obtain any other information other than the data scale. At the same time, this scheme can support batch queries by users and supports the flow of user information across institutions.

[0035] To facilitate understanding, the following application scenario illustrates the specific application of the tag-bridging retrieval scheme: Participant A collects the MAC value of end users through a specific SDK (Software Development Kit) and calculates a user score based on the end users' behavior and attributes, forming a mapping table M_A from MAC value to score; Participant B possesses the MAC value and mobile phone number of end users, forming a mapping table M_B from mobile phone number to MAC value; while Participant C possesses (a portion of) the mobile phone numbers of end users and hopes to query the user's score, and then carry out further corresponding activities based on the score.

[0036] Therefore, in the above application scenario, without considering privacy compliance, participant C could directly send their phone number to participant B, who would then perform the query. In this case, the queried phone number would be directly leaked to both participant B and participant A, compromising user privacy and posing compliance risks. To address this, this specification provides a data processing method that uses a privacy-protected tag-based retrieval scheme. This method allows participant C to obtain the corresponding score without disclosing the queried phone number, while ensuring that participants A and B do not obtain any information other than the input data from other participants during the query process. Thus, this scheme effectively protects data query operations involving user privacy.

[0037] This specification provides a data processing method, and also relates to a data processing system applied to the cloud, a computing device, and a computer-readable storage medium, which are described in detail in the following embodiments.

[0038] See Figure 1 , Figure 1 A schematic diagram of a system architecture for a cloud-based data processing system according to an embodiment of this specification is shown, specifically including the following steps.

[0039] It should be noted that the data processing system 100 applied to the cloud provided in the embodiments of this specification includes a first data provider 102, a bridging data provider 104, and a second data provider 106.

[0040] The cloud-based data processing system 100 supports a tag-bridging retrieval scheme between three parties. The first data provider 102 can be understood as a data querying party, such as a querying platform that only holds the mobile phone number of a user's terminal on the platform and wants to query the user behavior score corresponding to that mobile phone number. This includes, but is not limited to, merchants who need to conduct marketing (merchants want to obtain the user's behavior score based on the user's mobile phone number in order to conduct data analysis on user behavior and determine corresponding marketing strategies). The bridging data provider 104 can be understood as a bridging platform that stores data. It can also be understood as the data that the first data provider 102 needs to query is not directly stored in the bridging data provider 104, but can be used to query other platforms, including but not limited to e-commerce platforms. The second data provider 106 can be understood as the platform that targets the query data, that is, the platform that stores the user behavior score corresponding to the mobile phone number that the first data provider 102 wants to query. This includes, but is not limited to, developers of data service SDKs.

[0041] In practical applications, the first data provider 102 wants to obtain the user behavior score corresponding to the user's mobile phone number based on the user's mobile phone number. During the data query process for the user behavior score, it can determine that the bridging data in the cloud-based data processing system 100 is a MAC value. This MAC value information is then sent to the bridging data provider 104 and the second data provider 106. The bridging data provider 104 can perform pseudo-random processing on all locally held MAC values ​​to obtain a first pseudo MAC value. The second data provider 106 can also perform pseudo-random processing on all locally held MAC values ​​to obtain a second pseudo MAC value. Finally, the first data provider 102 can determine the user behavior score corresponding to the user's mobile phone number based on the first pseudo MAC value in the bridging data provider 104 and the second pseudo MAC value in the second data provider 106.

[0042] It should be noted that the bridging provider 104 has a mapping table between local users' mobile phone numbers and MAC values, and the second data provider 106 has a mapping table between the MAC values ​​held and the user's behavior score values.

[0043] The cloud-based data processing system provided in this specification can utilize a bridging data provider as a "bridge" to allow the first data provider to query the required data from the second data provider. Furthermore, the bridging data in this scheme undergoes pseudo-random processing in both the bridging data provider and the second data provider, effectively processing the actual bridging data. This ensures that during the three-way interaction, the bridging data provider is unaware of the data from the other two parties, the second data provider is also unaware of the data from the other two parties, and the first data provider, besides querying the required data from the second data provider, is unaware of any other additional data. This guarantees that the privacy of all parties' data is not leaked, effectively ensuring the security of data querying.

[0044] See Figure 2 , Figure 2 A flowchart of a data processing method according to an embodiment of this specification is shown, which specifically includes the following steps.

[0045] To better understand the solution, this specification describes a two-party privacy-preserving data query method for finding the intersection of labeled privacy sets. For example, participant A holds a mapping table. Participant B holds a set of tags Participant A and Participant B run a labeled privacy set intersection protocol. Participant B obtains the intersection L∩L′, and for each l∈L∩L′, obtains the corresponding... By executing the protocol, which ensures (l, v) ∈ M, participant A and participant B complete a privacy-preserving tag query scheme. However, the application scenarios for the aforementioned two-party interaction scheme are relatively limited, namely, participant A obtains the tag space held by participant B. To retrieve value space The mapping table. Furthermore, in the embodiments described below in this specification, this mapping table is split into... arrive Mapping table and from arrive The two mapping tables will be held by two independent and different participants to enable the data query process among the three parties.

[0046] In response, this specification also provides a data processing method applicable to data query scenarios between three parties. First, the three-party tag bridging retrieval problem is broken down into two two-party tag retrieval processes. The first two-party interaction involves participant C (holding a set of tags). ) and participant B (holding a set of tags) To the tag set The mapping table is used to perform a tag retrieval for both parties. Participant C can then obtain... arrive The mapping table (corresponding set denoted as) Secondly, the second interaction between the two parties is as follows: Participant C uses... And participant A (holding a set of tags) to set The mapping table) performs a tag retrieval between the two parties, and participant C obtains L. C ∩L A arrive The mapping table. Finally, participant C can combine the above two mapping tables to obtain... arrive The mapping table allows participant C to obtain the target query data they need to query.

[0047] However, in the aforementioned three-party tag retrieval process, when the tag retrieval schemes used by two parties meet privacy protection requirements, the above scheme can ensure that participants A and B cannot obtain information other than the input size of participant C. However, participant C not only obtains... corresponding The value also yielded the corresponding set. Taking the example of querying the user behavior score corresponding to a mobile phone number in the above embodiment, participant C not only obtains the score corresponding to the mobile phone number, but also the MAC value corresponding to the mobile phone number. Therefore, this data processing scheme leaks additional user privacy and poses compliance risks.

[0048] Therefore, it is important to emphasize in this embodiment that the "additional information" (L) obtained by participant C in the above scheme is... C In reality, it acts as a "bridge" to prevent participant C from obtaining set L. C In the following embodiments, the information can be obtained by utilizing the technique of an unintentional pseudo-random function to map the mapping table M held by participant A. A All of the l A ∈L A and the M held by participant B B All elements in l B ∈L B Convert them to pseudo-random values ​​S respectively A and S B , making s A =s B If and only if l A =l B At this point, participant B holds a set of tags. To the pseudo-random label set S B The mapping table is then used, and participant C can interact with participant B to obtain the pseudo-random label set S. C This set does not reveal set L.C While providing information, it can also achieve the purpose of bridging, thereby supporting a privacy-preserving tag-based retrieval method.

[0049] Furthermore, in the following embodiments, an unintentional pseudo-random function and a labeled privacy set intersection protocol can be used to realize the process of bridging data query in a three-party scenario.

[0050] It should be noted that the data processing method provided in the embodiments of this specification is applied to a data processing system, which includes a first data provider, a bridging data provider, and a second data provider. The interpretation of the above three parties can be referred to the description in the above embodiments, and no specific limitation is made here. At the same time, the data processing system can be configured in a cloud system or in a local server, and no specific limitation is made in this embodiment.

[0051] For ease of understanding, the description of the data processing method in this embodiment also refers to the example above of querying the user behavior score corresponding to the user terminal mobile phone number to illustrate the specific data processing method, but no limitations are made on the specific application scenario and the data queried.

[0052] Step 202: The bridging data provider determines the first bridging data and performs pseudo-random processing on the first bridging data to obtain the first pseudo bridging data.

[0053] The first bridging data is information associated with the query data information of the first target object set.

[0054] The first target object set can be understood as the data set held by the first data provider, such as a set of user terminal mobile phone numbers, and querying a set of user behavior scores that have a mapping relationship with the set of mobile phone numbers based on the held set of user terminal mobile phone numbers; the data information to be queried can be understood as the data type information of the set that has a mapping relationship with the first target object set in the target query data. For example, when querying a set of user behavior scores, if the corresponding target query data can be user behavior scores, the data information to be queried is the data type of MAC value that has a mapping relationship with user behavior scores; the first bridging data can be understood as the data that has a mapping relationship with the target query data held locally by the bridging data provider, and this data can act as a bridge to query user behavior scores from user terminal mobile phone numbers.

[0055] In practical applications, taking MAC value data as an example of bridging data information, the bridging data provider can receive MAC value data sent by the first data provider and determine that the MAC value held locally is the first bridging data. Then, it can perform pseudo-random processing on all MAC values ​​held locally to obtain the pseudo MAC value corresponding to the MAC value.

[0056] Furthermore, before determining the first bridging data, the bridging data provider also includes:

[0057] The first data provider receives a data query request for the first target object set;

[0058] Based on the data information to be queried carried in the data query request, determine the bridging data information associated with the data information to be queried;

[0059] The bridging data information is sent to the bridging data provider;

[0060] Accordingly, the bridging data provider determines the first bridging data, including:

[0061] The bridging data provider determines the first bridging data locally based on the received bridging data information.

[0062] In practical applications, the first data provider, as the initiator of the data query, can receive a data query request corresponding to a set of user terminal mobile phone numbers. Based on the data information to be queried carried in the data query request, it can determine the bridging data information associated with the data information to be queried. For example, if the data set to be queried is a set of user behavior scores based on the set of user terminal mobile phone numbers, then it is necessary to further determine the data information that has a mapping relationship with the set of user behavior scores, and use this data information as bridging data information, and then send the bridging data information to the bridging data provider.

[0063] The data processing method provided in the embodiments of this specification involves a first data provider determining the data type that has a mapping relationship with the target query data based on the data query requirements, thereby accurately determining the bridging data information and thus determining the bridging data.

[0064] The data processing method provided in the embodiments of this specification performs pseudo-random processing on bridging data through an unintentional pseudo-random function protocol to obtain pseudo-bridging data after random processing; specifically, the bridging data provider performs pseudo-random processing on the first bridging data to obtain the first pseudo-bridging data, including:

[0065] The bridging data provider determines an unintentional pseudo-random function protocol with the second data provider, selects a random key based on the unintentional pseudo-random function protocol, and performs pseudo-random processing on the first bridging data based on the random key to obtain the first pseudo-bridging data.

[0066] The Unintentional Pseudorandom Function Protocol is a two-party protocol. It can be understood that the two parties that sign the protocol can use the same key to process pseudorandom numbers, or it can be understood as processing data randomly. In this embodiment, the Unintentional Pseudorandom Function Protocol is used as an example to illustrate the processing of bridged data, but it is not limited to the protocol performing random processing.

[0067] In practical applications, the bridging data provider first determines an unintentional pseudo-random function protocol with the second data provider. The bridging data provider can randomly select a random key according to the protocol, and the random key is kept confidential. Then, the bridging data provider can perform pseudo-random processing on the first bridging data based on the random key to obtain the first pseudo-bridging data.

[0068] For example, participant A uniformly and randomly generates a pseudo-random function key k, and applies it to all tags l. A ∈L A Calculate the corresponding pseudo-random label S A =PRF(k, l) A Participant A obtains a set of pseudo-random labels S. A arrive The mapping table can be found in Equation 1 below:

[0069] M′ A ={(s A ,v):s A =PRF(k, lA)∈S A , (l A ,v)∈M A Equation 1

[0070] Step 204: The second data provider determines the second bridging data and performs pseudo-random processing on the second bridging data to obtain the second pseudo-bridging data.

[0071] The second bridging data has the same data type as the first bridging data, and can be referred to the description of the first bridging data above, so it will not be elaborated on here.

[0072] Furthermore, before determining the second bridging data, the second data provider also includes:

[0073] The first data provider receives a data query request for the first target object set;

[0074] Based on the data information to be queried carried in the data query request, determine the bridging data information associated with the data information to be queried;

[0075] The bridging data information is sent to the second data provider;

[0076] Accordingly, the second data provider determines the second bridging data, including:

[0077] The second data provider determines the second bridging data locally based on the received bridging data information.

[0078] In practical applications, the second data provider can also receive the bridging data information sent by the first data provider, and determine the second bridging data from the data it holds locally based on the bridging data information. For example, it can determine the MAC value data it holds locally, and then similarly, it can obtain the second pseudo bridging data by performing pseudo-random processing on the MAC value data held by the second data provider.

[0079] Furthermore, since the bridging data provider and the second data provider execute an unintentional pseudo-random function protocol, the second data provider can use the random key in the bridging data provider to obtain pseudo-bridging data corresponding to the second bridging data; specifically, the second data provider performs pseudo-random processing on the second bridging data to obtain the second pseudo-bridging data, including:

[0080] The second data provider uses the random key in the bridging data provider to perform pseudo-random processing on the second bridging data to obtain the second pseudo-bridging data.

[0081] In practical applications, the second data provider also performs pseudo-random processing on the second bridging data. In order to ensure that the pseudo-MAC values ​​after pseudo-random processing of the same MAC value are the same, the second data provider can use the random key in the bridging data provider to perform pseudo-random processing on the second bridging data to obtain the second pseudo-bridging data. It should be noted that the first bridging data and the second bridging data may have the same value, such as having the same MAC value. In this case, the pseudo-bridging data processed with the same random key should also be the same.

[0082] Continuing with the previous example, participant B and participant A execute an unintentional pseudo-random function protocol, and participant B obtains all the labels. B ∈L B The corresponding pseudo-random label s B =PRF(k, l) B Thus, the corresponding mapping table is obtained, which can be referred to in Equation 2 below:

[0083]

[0084] The data processing method provided in the embodiments of this specification uses a random key in the bridging data provider to perform pseudo-random processing on the second bridging data in the second data provider, so as to facilitate the subsequent intersection processing between the first pseudo bridging data and the second pseudo bridging data, thereby converting the bridging tag into a pseudo bridging data tag. This ensures that the tag information is not leaked, while also serving the purpose of data bridging.

[0085] Furthermore, in the process of obtaining the second pseudo-bridging data, the second data provider faces the risk of data leakage due to the use of the random key from the bridging data provider. Therefore, the second data provider can first perform random processing on the second bridging data to avoid data leakage. Specifically, the second data provider uses the random key from the bridging data provider to perform pseudo-random processing on the second bridging data to obtain the second pseudo-bridging data, including:

[0086] The second data provider performs random processing on the second bridging data and sends the randomly processed second bridging data to the bridging data provider.

[0087] The second data provider receives the second pseudo-bridging data returned by the bridging data provider, wherein the second pseudo-bridging data is obtained by the bridging data provider through pseudo-random processing of the randomly processed second bridging data based on the random key.

[0088] In practical applications, the second data provider can first randomly process the second bridging data, and then send the randomly processed data to the bridging data provider. The bridging data provider can then perform pseudo-random processing on the randomly processed second bridging data to obtain the second pseudo-bridging data. It should be noted that the second data provider's random processing method for the second bridging data includes, but is not limited to, blinding processing, and this embodiment does not impose any limitations on this. Furthermore, the operation performed by the second data provider is to first randomly process the bridging data, then send the randomly processed second bridging data to the bridging data provider, and then receive the second pseudo-bridging data corresponding to the second bridging data from the bridging data provider.

[0089] The data processing method provided in the embodiments of this specification can quickly obtain the second pseudo-bridge data by using a random key selected by the bridging data provider through an unintentional pseudo-random function protocol.

[0090] Step 206: The first data provider obtains the target query data corresponding to the first target object set based on the first pseudo-bridging data received from the bridging data provider and the second pseudo-bridging data received from the second data provider.

[0091] The target query data is data of the same type as the data information to be queried.

[0092] In practical applications, the first data provider can perform two intersection operations on labeled privacy sets, using the first pseudo-bridging data from the bridging data provider and the second pseudo-bridging data from the second data provider to obtain the target query data corresponding to the first target object set.

[0093] Further, the first data provider, based on the first pseudo-bridging data received from the bridging data provider and the second pseudo-bridging data received from the second data provider, obtains the target query data corresponding to the first target object set, including:

[0094] The first data provider determines, based on the received first pseudo-bridging data, first target pseudo-bridging data for the first target object set; and

[0095] Based on the received second pseudo-bridging data and the first target pseudo-bridging data, the target query data corresponding to the first target object set is determined.

[0096] The first target pseudo-bridging data can be understood as the pseudo-bridging data corresponding to the first target object set in the first pseudo-bridging data.

[0097] In practical applications, the first data provider and the bridging data provider perform a labeled privacy set intersection once. This can be understood as the first data provider performing a privacy set intersection with the set of user terminal mobile phone numbers it holds and the set of mobile phone numbers and pseudo MAC values ​​mapped by the bridging data provider. Furthermore, the first data provider then performs another labeled intersection based on the set of pseudo MAC values ​​corresponding to the user terminal mobile phone number and the second pseudo bridging data determined by the second data provider, in order to map and obtain the target query data.

[0098] The data processing method provided in the embodiments of this specification, by realizing the intersection of privacy sets between each of the above three parties, not only prevents the other party from knowing the specific interaction information, but also enables the rapid determination of the mapping relationship between the data and the determination of the target query data.

[0099] Furthermore, in the data processing method provided in the embodiments of this specification, a first privacy set intersection protocol is executed; specifically, the first data provider determines the first target pseudo-bridging data of the first target object set based on the received first pseudo-bridging data, including:

[0100] The first data provider determines the set intersection protocol for interacting with the bridging data provider;

[0101] Obtain the bridging data set in the bridging data provider that maps the first pseudo-bridging data to the second target object, wherein the second target object and the first target object set have the same data type;

[0102] Based on the set intersection protocol, the bridging data set and the first target object set are intersected to obtain the first target pseudo-bridging data of the first target object set.

[0103] In practical applications, the first data provider first determines the set intersection protocol for interaction with the bridging data provider, and obtains the bridging data set that maps the first pseudo-bridging data to the second target object from the bridging data provider. Then, the privacy intersection protocol is executed to perform set intersection between the bridging data set and the first target object set to obtain the first target pseudo-bridging data corresponding to the first target object set.

[0104] For example, the set of mobile phone numbers held by the first data provider is denoted as The set of mobile phone numbers held by the bridging data provider is denoted as Meanwhile, the pseudo MAC value corresponding to the mobile phone number held by the bridging data provider is denoted as M′. B Therefore, the first data provider can obtain the intersection. In M′ B The corresponding set of pseudo-random labels (denoted as S) C This refers to the set of pseudo MAC values ​​corresponding to the user terminal mobile phone numbers held by the first data provider.

[0105] In the data processing method provided in the embodiments of this specification, a second privacy set intersection protocol is executed to obtain target query data corresponding to the first target object set; specifically, the first data provider determines the target query data corresponding to the first target object set based on the received second pseudo-bridging data and the first target pseudo-bridging data, including:

[0106] The first data provider determines the set intersection protocol for interacting with the second data provider;

[0107] Obtain the set of query data that maps the second pseudo-bridging data to the query data in the second data provider;

[0108] Based on the set intersection protocol, the first target pseudo-bridging data and the set of data to be queried are subjected to set intersection processing to obtain the target query data corresponding to the first target object set.

[0109] In practical applications, the first data provider and the second data provider determine the set intersection protocol for interaction. At the same time, the second data provider can obtain the set of query data that has a mapping relationship with the second pseudo-bridging data and the query data. Then, based on the first target pseudo-bridging data obtained from the first set intersection protocol and the query data set, the target query data corresponding to the final first target object set is determined.

[0110] Continuing with the previous example, after obtaining the set of pseudo-MAC values ​​corresponding to the user terminal mobile phone numbers it holds, the first data provider can directly obtain the set of pseudo-MAC values ​​and corresponding user behavior scores held by the second data provider. Then, the first data provider intersects the obtained pseudo-MAC values ​​corresponding to the mobile phone numbers with the pseudo-MAC values ​​held by the second data provider, denoted as S. A ∩S C Then, based on the result of the intersection, we can find the answer in M′. A The set of corresponding score values ​​is obtained, denoted as . Complete the privacy set intersection protocol.

[0111] In the data processing method provided in the embodiments of this specification, pseudo-bridging data is used as an intermediate bridge to perform pairwise privacy set intersection protocol among the three parties, so as to realize the mapping query process from the first target object set to the target query data. This not only effectively ensures that each party is completely unaware of the privacy data of the other party, but also guarantees the efficiency of data query execution among the three parties.

[0112] Furthermore, in the data processing method provided in the embodiments of this specification, after obtaining the target query data corresponding to the first target object set, the first data provider can also establish a mapping relationship between the first target object set and the target query data; specifically, after obtaining the target query data corresponding to the first target object set, the first data provider further includes:

[0113] The first data provider maps the target query data to the first target object set to generate a mapping table between the target query data and the first target object set.

[0114] The mapping relationship table between the target query data and the first target object set is displayed.

[0115] In practical applications, the first data provider can establish a mapping table between the first target object set and the target query data, and can also display this mapping table. For example, the first data provider can establish a mapping table between user terminal mobile phone numbers held locally and user behavior scores, and display this mapping table to the merchant. Furthermore, the first data provider can perform data analysis operations based on the user behavior scores. The specific content of the subsequent data analysis, such as whether the merchant should place advertisements on the user or grant the user specific permissions based on the mapping table, is not specifically limited in this embodiment.

[0116] In summary, the data processing method provided in the embodiments of this specification uses an unintentional pseudo-random function to convert bridging labels into pseudo-random labels. On the one hand, it ensures that participant C only obtains the necessary information, while participants A and B do not obtain any information other than the input scale, thus having the ability to protect privacy. This avoids participant C from knowing the attribute information of the bridging data, that is, it does not disclose the label information, and it can still play the bridging role.

[0117] See Figure 3 , Figure 3 A flowchart of a data processing method according to another embodiment of this specification is shown, which specifically includes the following steps.

[0118] It should be noted that the data processing method provided in this embodiment can be applied to a data processing system, which includes a merchant, a bridging data providing platform, and a data service platform. This data processing system can be configured in the cloud or in a local database; no specific limitations are made here. In the above embodiment, the merchant is the first data provider, the bridging data providing platform is the bridging data provider, and the data service platform is the second data provider; further details will not be elaborated upon here.

[0119] Step 302: The bridging data providing platform receives the bridging data information sent by the merchant, determines the first bridging data based on the bridging data information, and performs pseudo-random processing on the first bridging data to obtain the first pseudo bridging data.

[0120] The bridging data information is information associated with user behavior data information related to user communication data.

[0121] In this context, user communication data can be understood as the mobile phone number corresponding to the user terminal, etc., without specific limitations; user behavior data information can be understood as behavioral data information such as the user's browsing preferences and purchasing power that the merchant wants to know; the first bridging data can be understood as data that is associated with user communication data and user behavior data in the bridging data providing platform. In this embodiment, no specific type of bridging data is limited, and different data types of bridging data can be used according to different application scenarios.

[0122] In practical applications, the pseudo-random processing of the first bridging data by the bridging data providing platform can be referred to the description in the above embodiments, and will not be elaborated further here.

[0123] Step 304: The data service platform receives bridging data information sent by the merchant, determines second bridging data based on the bridging data information, and performs pseudo-random processing on the second bridging data to obtain second pseudo-bridging data.

[0124] Similarly, this second bridging data corresponds to the second bridging data in the above embodiments, and can be referred to the description in the above embodiments.

[0125] Step 306: The merchant obtains the target user behavior data corresponding to the user communication data based on the first pseudo-bridging data in the bridging data providing platform and the second pseudo-bridging data in the data service platform.

[0126] The target user behavior data is data of the same type as the user behavior data information.

[0127] It should be noted that the process by which the merchant obtains the target user behavior data corresponding to the user communication data in this embodiment can be referred to the execution process of the two privacy set intersection protocols in the above embodiment, and will not be elaborated on here.

[0128] The data processing method provided in this specification is applied to a three-way interaction scenario where a merchant queries data on a data service platform. It utilizes a bridging data provider platform and the data service platform to perform pseudo-random processing on the bridging data, obtaining pseudo-bridging data. This ensures privacy protection of the bridging data during the three-way interaction. Simultaneously, the merchant uses the pseudo-bridging data as an intermediate "bridge" to query the desired target data. This method not only supports bridging data queries between the three parties but also ensures that none of the parties are aware of the data held by the others, thus solving the privacy leakage problem during data querying, improving data security, and not affecting the merchant's subsequent efficiency in using the target query data.

[0129] Corresponding to the above method embodiments, this specification also provides data processing system embodiments. Figure 4 A schematic diagram of the structure of a data processing system according to one embodiment of this specification is shown. Figure 4 As shown, the system includes: a first data provider 406, a bridging data provider 402, and a second data provider 404;

[0130] The bridging data provider 402 is configured to determine first bridging data and perform pseudo-random processing on the first bridging data to obtain first pseudo bridging data, wherein the first bridging data is information associated with the query data information of the first target object set;

[0131] The second data provider 404 is configured to determine the second bridging data and perform pseudo-random processing on the second bridging data to obtain the second pseudo bridging data, wherein the data type of the second bridging data is consistent with that of the first bridging data;

[0132] The first data provider 406 is configured to obtain target query data corresponding to the first target object set based on the first pseudo-bridging data received from the bridging data provider and the second pseudo-bridging data received from the second data provider, wherein the target query data is data of the same type as the data information to be queried.

[0133] Optionally, the first data provider 406 is further configured to obtain target query data corresponding to the first target object set based on the first pseudo-bridging data received from the bridging data provider and the second pseudo-bridging data received from the second data provider, including:

[0134] Optionally, the first data provider 406 is further configured to determine first target pseudo-bridging data for the first target object set based on the received first pseudo-bridging data; and

[0135] Based on the received second pseudo-bridging data and the first target pseudo-bridging data, the target query data corresponding to the first target object set is determined.

[0136] Optionally, the first data provider 406 is further configured to determine a set intersection protocol for interacting with the bridging data provider;

[0137] Obtain the bridging data set in the bridging data provider that maps the first pseudo-bridging data to the second target object, wherein the second target object and the first target object set have the same data type;

[0138] Based on the set intersection protocol, the bridging data set and the first target object set are intersected to obtain the first target pseudo-bridging data of the first target object set.

[0139] Optionally, the first data provider 406 is further configured to determine a set intersection protocol for interacting with the second data provider;

[0140] Obtain the set of query data that maps the second pseudo-bridging data to the query data in the second data provider;

[0141] Based on the set intersection protocol, the first target pseudo-bridging data and the set of data to be queried are subjected to set intersection processing to obtain the target query data corresponding to the first target object set.

[0142] Optionally, the first data provider 406 is further configured to receive a data query request for a first target object set;

[0143] Based on the data information to be queried carried in the data query request, determine the bridging data information associated with the data information to be queried;

[0144] The bridging data information is sent to the bridging data provider.

[0145] Optionally, the bridging data provider 402 is further configured to determine first bridging data locally based on the received bridging data information.

[0146] Optionally, the first data provider 406 is further configured to receive a data query request for a first target object set;

[0147] Based on the data information to be queried carried in the data query request, determine the bridging data information associated with the data information to be queried;

[0148] The bridging data information is sent to the second data provider;

[0149] Optionally, the second data provider 404 is further configured to determine the second bridging data locally based on the received bridging data information.

[0150] Optionally, the bridging data provider 402 is further configured to determine an unintentional pseudo-random function protocol with the second data provider, select a random key based on the unintentional pseudo-random function protocol, and perform pseudo-random processing on the first bridging data based on the random key to obtain the first pseudo-bridging data.

[0151] Optionally, the second data provider 404 is further configured to use the random key in the bridging data provider to perform pseudo-random processing on the second bridging data to obtain second pseudo-bridging data.

[0152] Optionally, the second data provider 404 is further configured to perform random processing on the second bridging data and send the randomly processed second bridging data to the bridging data provider;

[0153] Optionally, the second data provider 404 is further configured to receive second pseudo-bridging data returned by the bridging data provider, wherein the second pseudo-bridging data is obtained by the bridging data provider through pseudo-random processing of the randomly processed second bridging data based on the random key.

[0154] Optionally, the first data provider 406 is further configured to map the target query data to the first target object set to generate a mapping table between the target query data and the first target object set.

[0155] The mapping relationship table between the target query data and the first target object set is displayed.

[0156] The data processing system described in this specification can be applied to three-party data query scenarios. It can utilize both the bridging data provider and the second data provider to perform pseudo-random processing on the bridging data to obtain pseudo-bridging data, thereby ensuring the privacy protection of the bridging data during the three-party interaction. Simultaneously, the first data provider uses the pseudo-bridging data as an intermediate "bridge" to realize the query processing of the target query data corresponding to the first target object set. This method not only supports bridging data queries between the three parties but also ensures that none of the parties know the data held by the others, thus solving the privacy leakage problem during the data query process, improving data security, and not affecting the user's subsequent use efficiency of the target query data.

[0157] The above is an illustrative scheme of a data processing system according to this embodiment. It should be noted that the technical solution of this data processing system and the technical solution of the data processing method described above belong to the same concept. For details not described in detail in the technical solution of the data processing system, please refer to the description of the technical solution of the data processing method described above.

[0158] Figure 5 A structural block diagram of a computing device 500 according to one embodiment of this specification is shown. The components of the computing device 500 include, but are not limited to, a memory 510 and a processor 520. The processor 520 is connected to the memory 510 via a bus 530, and a database 550 is used to store data.

[0159] The computing device 500 also includes an access device 540, which enables the computing device 500 to communicate via one or more networks 560. Examples of these networks include a Public Switched Telephone Network (PSTN), a Local Area Network (LAN), a Wide Area Network (WAN), a Personal Area Network (PAN), or a combination of communication networks such as the Internet. The access device 540 may include one or more of any type of wired or wireless network interface (e.g., a Network Interface Card (NIC)), such as an IEEE 802.11 Wireless Local Area Network (WLAN) interface, a Wi-MAX interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth interface, a Near Field Communication (NFC) interface, and so on.

[0160] In one embodiment of this specification, the above-described components of the computing device 500 and Figure 5 Other components, not shown, can also be connected to each other, for example, via a bus. It should be understood that... Figure 5 The block diagram of the computing device shown is for illustrative purposes only and is not intended to limit the scope of this specification. Those skilled in the art can add or replace other components as needed.

[0161] The computing device 500 can be any type of stationary or mobile computing device, including mobile computers or mobile computing devices (e.g., tablet computers, personal digital assistants, laptop computers, notebook computers, netbooks, etc.), mobile phones (e.g., smartphones), wearable computing devices (e.g., smartwatches, smart glasses, etc.) or other types of mobile devices, or stationary computing devices such as desktop computers or PCs. The computing device 500 can also be a mobile or stationary server.

[0162] The processor 520 is configured to execute the following computer-executable instructions, which, when executed by the processor, implement the steps of the above-described data processing method.

[0163] The above is an illustrative scheme of a computing device according to this embodiment. It should be noted that the technical solution of this computing device and the technical solution of the data processing method described above belong to the same concept. For details not described in detail in the technical solution of the computing device, please refer to the description of the technical solution of the data processing method described above.

[0164] An embodiment of this specification also provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the above-described data processing method.

[0165] The above is an illustrative scheme of a computer-readable storage medium according to this embodiment. It should be noted that the technical solution of this storage medium and the technical solution of the data processing method described above belong to the same concept. For details not described in detail in the technical solution of the storage medium, please refer to the description of the technical solution of the data processing method described above.

[0166] An embodiment of this specification also provides a computer program, wherein when the computer program is executed in a computer, it causes the computer to perform the steps of the above-described data processing method.

[0167] The above is an illustrative example of a computer program according to this embodiment. It should be noted that the technical solution of this computer program and the technical solution of the data processing method described above belong to the same concept. Details not described in detail in the technical solution of the computer program can be found in the description of the technical solution of the data processing method described above.

[0168] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0169] The computer instructions include computer program code, which may be in the form of source code, object code, executable file, or certain intermediate forms. The computer-readable medium may include any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium may be appropriately added to or subtracted according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media may not include electrical carrier signals and telecommunication signals.

[0170] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments in this specification are not limited to the described order of actions, because according to the embodiments in this specification, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the embodiments in this specification.

[0171] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0172] The preferred embodiments disclosed above are merely illustrative of this specification. The optional embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the embodiments described herein. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the embodiments, thereby enabling those skilled in the art to better understand and utilize this specification. This specification is limited only by the claims and their full scope and equivalents.

Claims

1. A data processing method applied to a data processing system, the data processing system comprising a first data provider, a bridging data provider, and a second data provider; The bridging data provider determines first bridging data and performs pseudo-random processing on the first bridging data to obtain first pseudo-bridging data, wherein... The first bridging data is MAC value information associated with the data information to be queried in the first target object set; The second data provider determines the second bridging data and performs pseudo-random processing on the second bridging data to obtain the second pseudo bridging data, wherein the data type of the second bridging data is consistent with that of the first bridging data; The first data provider determines a set intersection protocol for interacting with the bridging data provider, obtains a bridging data set mapping the first pseudo-bridging data and the second target object from the bridging data provider, performs a set intersection on the bridging data set and the first target object set based on the set intersection protocol to obtain the first target pseudo-bridging data of the first target object set, and determines a set intersection protocol for interacting with the second data provider, obtains a query data set mapping the second pseudo-bridging data and the query data from the second data provider, and performs a set intersection on the first target pseudo-bridging data and the query data set based on the set intersection protocol to obtain the target query data corresponding to the first target object set, wherein the target query data is data of the same type as the query data information.

2. The data processing method according to claim 1, comprising: The second target object has the same data type as the first set of target objects.

3. The data processing method according to claim 1, wherein before determining the first bridging data, the bridging data provider further includes: The first data provider receives a data query request for the first target object set; Based on the data information to be queried carried in the data query request, determine the bridging data information associated with the data information to be queried; The bridging data information is sent to the bridging data provider; Accordingly, the bridging data provider determines the first bridging data, including: The bridging data provider determines the first bridging data locally based on the received bridging data information.

4. The data processing method according to claim 1, further comprising, before determining the second bridging data, the second data provider: The first data provider receives a data query request for the first target object set; Based on the data information to be queried carried in the data query request, determine the bridging data information associated with the data information to be queried; The bridging data information is sent to the second data provider; Accordingly, the second data provider determines the second bridging data, including: The second data provider determines the second bridging data locally based on the received bridging data information.

5. The data processing method according to claim 1, wherein the bridging data provider performs pseudo-random processing on the first bridging data to obtain first pseudo-bridging data, comprising: The bridging data provider determines an unintentional pseudo-random function protocol with the second data provider, selects a random key based on the unintentional pseudo-random function protocol, and performs pseudo-random processing on the first bridging data based on the random key to obtain the first pseudo-bridging data.

6. The data processing method according to claim 5, wherein the second data provider performs pseudo-random processing on the second bridging data to obtain second pseudo-bridging data, comprising: The second data provider uses the random key in the bridging data provider to perform pseudo-random processing on the second bridging data to obtain the second pseudo-bridging data.

7. The data processing method according to claim 6, wherein the second data provider uses the random key in the bridging data provider to perform pseudo-random processing on the second bridging data to obtain second pseudo-bridging data, comprising: The second data provider performs random processing on the second bridging data and sends the randomly processed second bridging data to the bridging data provider. The second data provider receives the second pseudo-bridging data returned by the bridging data provider, wherein the second pseudo-bridging data is obtained by the bridging data provider through pseudo-random processing of the randomly processed second bridging data based on the random key.

8. The data processing method according to claim 1, after the first data provider obtains the target query data corresponding to the first target object set, further includes: The first data provider maps the target query data to the first target object set to generate a mapping table between the target query data and the first target object set. The mapping relationship table between the target query data and the first target object set is displayed.

9. A data processing method applied to a data processing system, the data processing system comprising a merchant, a bridging data providing platform, and a data service platform; The bridging data providing platform determines first bridging data and performs pseudo-random processing on the first bridging data to obtain first pseudo-bridging data, wherein... The first bridging data is MAC value information associated with user behavior data information in user communication data; The data service platform determines the second bridging data and performs pseudo-random processing on the second bridging data to obtain the second pseudo bridging data, wherein the data type of the second bridging data is consistent with that of the first bridging data. The merchant determines a set intersection protocol for interacting with the bridging data providing platform, obtains a bridging data set mapping the first pseudo-bridging data and the second target object in the bridging data providing platform, performs a set intersection between the bridging data set and the user communication data based on the set intersection protocol to obtain the first target pseudo-bridging data of the user communication data, and determines a set intersection protocol for interacting with the data service platform, obtains a user behavior data set mapping the second pseudo-bridging data and user behavior data in the data service platform, and performs a set intersection process between the first target pseudo-bridging data and the user behavior data set based on the set intersection protocol to obtain the target user behavior data corresponding to the user communication data, wherein the target user behavior data is data consistent with the type of the user behavior data information.

10. A data processing system applied in the cloud, comprising: First data provider, bridging data provider, and second data provider; The bridging data provider is configured to determine first bridging data and perform pseudo-random processing on the first bridging data to obtain first pseudo bridging data, wherein the first bridging data is MAC value information associated with the query data information of the first target object set. The second data provider is configured to determine the second bridging data and perform pseudo-random processing on the second bridging data to obtain the second pseudo bridging data, wherein the data type of the second bridging data is consistent with that of the first bridging data; The first data provider is configured to: determine a set intersection protocol for interacting with the bridging data provider; obtain a bridging data set mapping the first pseudo-bridging data and the second target object from the bridging data provider; perform set intersection on the bridging data set and the first target object set based on the set intersection protocol to obtain first target pseudo-bridging data of the first target object set; and determine a set intersection protocol for interacting with the second data provider; obtain a query data set mapping the second pseudo-bridging data and the query data from the second data provider; and perform set intersection processing on the first target pseudo-bridging data and the query data set based on the set intersection protocol to obtain target query data corresponding to the first target object set, wherein the target query data is data of the same type as the query data information.

11. A computing device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the data processing method according to any one of claims 1-8 and 9.

12. A computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the data processing method according to any one of claims 1-8 and 9.

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