Statistical Data Trusted Transmission Method, Device, Electronic Device and Storage Medium

Abstract

The present invention discloses a method, apparatus, electronic device and storage medium for securely transmitting statistical data. The method includes: generating a group public key according to the first public key of the first data provider and the second public keys of at least one obtained second data provider; generating a first homomorphic ciphertext according to the group public key and the first data corresponding to the obtained data user; receiving second homomorphic ciphertexts sent by each of the second data providers; calculating the first homomorphic ciphertext and each of the second homomorphic ciphertexts to determine a fused statistical result; generating a first homomorphic authorization ciphertext according to the first attribute access control tree corresponding to the first data provider and the first data; sending the fused statistical result and the first homomorphic authorization ciphertext to the data user, and invoking a multi-party secure computing interface according to the first private key corresponding to the first public key. The technical solution of the embodiment of the present invention can improve the security of multi-source fused data.

Description

Technical Field

[0001] The present invention relates to the field of data encryption, and in particular to a method, device, electronic device, and storage medium for secure transmission of statistical data. Background Art

[0002] With the development of Internet technology, the security and privacy of user data have received increasing attention.

[0003] Existing data sharing and exchange platforms have many limitations, including: the direct sharing method between the supply and demand sides makes the data have security risks while exchanging data, and when the data sources required by the data users are different, the current method is difficult to support the data fusion of multiple parties, resulting in a risk of data leakage. Summary of the Invention

[0004] The present invention provides a method, device, electronic device, and storage medium for secure transmission of statistical data, which can improve the security of multi-source fusion data.

[0005] According to one aspect of the present invention, there is provided a method for secure transmission of statistical data, which is applied to a first data provider, and the method includes:

[0006] Generating a group public key according to the first public key of the first data provider and the second public keys of at least one second data provider obtained;

[0007] Generating a first homomorphic ciphertext according to the group public key and the first data corresponding to the data user obtained;

[0008] Receiving the second homomorphic ciphertexts sent by each of the second data providers;

[0009] Calculating the first homomorphic ciphertext and each of the second homomorphic ciphertexts according to the statistical operation logic corresponding to the data user to determine a fusion statistical result;

[0010] Generating a first homomorphic authorization ciphertext according to the first attribute access control tree corresponding to the first data provider and the first data;

[0011] Sending the fusion statistical result and the first homomorphic authorization ciphertext to the data user, and calling the multi-party secure computing interface of the corresponding computing function of the privacy computing according to the first private key corresponding to the first public key, so that the data user can verify the first homomorphic authorization ciphertext according to the fusion statistical result, and calculate and decrypt the first homomorphic authorization ciphertext that passes the verification to obtain the plaintext of the statistical result.

[0012] According to one aspect of the present invention, there is provided a method for secure transmission of statistical data, which is applied to a data user, and the method includes:

[0013] Receive an authorized parsing key jointly determined by at least one attribute key service party;

[0014] Receive the fusion statistical result and the first homomorphic authorized ciphertext sent by the first data provider;

[0015] Receive the second homomorphic authorized ciphertexts sent by at least one second data provider;

[0016] Parse the first homomorphic authorized ciphertext according to the authorized parsing key to obtain a first ciphertext to be verified;

[0017] Parse each of the second homomorphic authorized ciphertexts according to the authorized parsing key to obtain a second ciphertext to be verified corresponding to each of the second homomorphic authorized ciphertexts;

[0018] Calculate the first ciphertext to be verified and each of the second ciphertexts to be verified according to the statistical operation logic to determine a homomorphic statistical result;

[0019] Verify the correctness of the homomorphic statistical result according to the fusion statistical result;

[0020] When the correctness verification of the homomorphic statistical result passes, decrypt the homomorphic statistical result to obtain the plaintext of the statistical result.

[0021] According to another aspect of the present invention, there is provided a statistical data trusted transmission device, which is configured in the first data provider, and the device includes:

[0022] A group public key generation module, configured to generate a group public key according to the first public key of the first data provider and the second public keys of at least one second data provider obtained;

[0023] A first homomorphic ciphertext acquisition module, configured to generate a first homomorphic ciphertext according to the group public key and the first data corresponding to the data user obtained;

[0024] A second homomorphic ciphertext acquisition module, configured to receive the second homomorphic ciphertexts sent by each of the second data providers;

[0025] A statistical calculation module, configured to calculate the first homomorphic ciphertext and each of the second homomorphic ciphertexts according to the statistical operation logic corresponding to the data user to determine a fusion statistical result;

[0026] An authorized ciphertext generation module, configured to generate a first homomorphic authorized ciphertext according to the first attribute access control tree corresponding to the first data provider and the first data;

[0027] A data sending module, configured to send the fusion statistical result and the first homomorphic authorized ciphertext to the data user, and call the multi-party secure computing interface of the corresponding computing function of the privacy computing according to the first private key corresponding to the first public key, so that the data user verifies the first homomorphic authorized ciphertext according to the fusion statistical result, and decrypts and calculates the first homomorphic authorized ciphertext passing the verification to obtain the plaintext of the statistical result.

[0028] According to another aspect of the present invention, there is provided a statistical data trusted transmission device, which is configured in the data user, and the device includes:

[0029] An authorized key receiving module, configured to receive an authorized parsing key jointly determined by at least one attribute key service party;

[0030] A first ciphertext receiving module, configured to receive the fusion statistical result and the first homomorphic authorized ciphertext sent by the first data provider;

[0031] A second ciphertext receiving module, configured to receive the second homomorphic authorized ciphertexts sent by at least one second data provider;

[0032] A first ciphertext parsing module, configured to parse the first homomorphic authorized ciphertext according to the authorized parsing key to obtain a first ciphertext to be verified;

[0033] A second ciphertext parsing module, configured to parse each of the second homomorphic authorized ciphertexts according to the authorized parsing key to obtain second ciphertexts to be verified corresponding to each of the second homomorphic authorized ciphertexts;

[0034] A homomorphic statistical calculation module, configured to calculate the first ciphertext to be verified and each of the second ciphertexts to be verified according to the statistical operation logic to determine a homomorphic statistical result;

[0035] A statistical result verification module, configured to perform a correctness verification on the homomorphic statistical result according to the fusion statistical result;

[0036] A plaintext obtaining module, configured to decrypt the homomorphic statistical result to obtain the plaintext of the statistical result when the correctness verification of the homomorphic statistical result passes.

[0037] According to another aspect of the present invention, there is provided an electronic device, and the electronic device includes:

[0038] At least one processor; and

[0039] A memory communicatively connected to the at least one processor; wherein,

[0040] The memory stores a computer program executable by the at least one processor, and when the computer program is executed by the at least one processor, the at least one processor is enabled to execute the statistical data trusted transmission method according to any embodiment of the present invention.

[0041] According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement the statistical data trusted transmission method according to any embodiment of the present invention when executed.

[0042] The technical solution of the embodiment of the present invention is to obtain a first homomorphic ciphertext generated by itself and second homomorphic ciphertexts provided by at least one second data provider, and generate a fused statistical result, so as to obtain a result obtained by statistically encrypting multi-source data, and generate a first homomorphic authorization ciphertext according to an attribute access control tree and first data, and send the fused statistical result and the homomorphic authorization ciphertext to a data user. The data user verifies and decrypts the homomorphic authorization ciphertext according to the fused statistical result, so as to further encrypt the homomorphic ciphertext, which can improve data security. At the same time, homomorphic ciphertexts of multiple data sources can be obtained, and in the application scenario of multi-source data fusion, the fused result of multi-source data is encrypted, supporting the scenario of multi-source data fusion, and at the same time, the credibility of the statistical result of multi-source data can be increased.

[0043] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. Description of the Drawings

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0045] Figure 1 is a flowchart of a statistical data trusted transmission method according to an embodiment of the present invention;

[0046] Figure 2 is a flowchart of a statistical data trusted transmission method according to an embodiment of the present invention;

[0047] Figure 3 is a schematic structural diagram of a statistical data trusted transmission device according to an embodiment of the present invention;

[0048] Figure 4It is a schematic structural diagram of a statistical data trustworthy transmission device provided according to an embodiment of the present invention;

[0049] Figure 5 It is a schematic structural diagram of an electronic device for implementing the statistical data trustworthy transmission method according to an embodiment of the present invention. Detailed implementation manners

[0050] In order to enable those skilled in the art of the present technology to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0051] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that such data used in appropriate cases can be interchanged so that the embodiments of the present invention described herein can be implemented in an order different from those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0052] Figure 1 It is a flowchart of a statistical data trustworthy transmission method provided according to an embodiment of the present invention. The embodiments of the present invention are applicable to the situation of fusing and statistically analyzing multi-source data in an encryption scenario, and can be specifically applied to fields such as the e-commerce field, the education field, the cloud computing field, and the artificial intelligence field. This method can be executed by a statistical data trustworthy transmission device, which can be implemented in the form of hardware and / or software. The statistical data trustworthy transmission device can be configured in an electronic device with the function of statistical data trustworthy transmission of a first data provider, such as a client device or a server device.

[0053] In the embodiments of the present invention, the multi-source data can be the number of teachers and students in multiple schools. For another example, the multi-source data can be the consumption data of multiple e-commerce platforms. For another example, the multi-source data can be the traffic data of multiple clouds. For another example, the sample data provided by multiple data sources of a federated model.

[0054] Taking the actual application scenario of aggregating regional consumption data on several e-commerce platforms without exposing business secrets such as the sales growth rate of commodity categories as an example, this method encrypts and calculates the consumption fluctuations of large commodities such as household appliances or automobiles to support the adjustment of macroeconomic policies.

[0055] Suppose the data user is the statistical party U, the first data provider is the first e-commerce platform P1, the corresponding first consumption data is data1, the second data provider is the second e-commerce platform P2, and the third e-commerce platform P3, and the corresponding second consumption data is data2 and the third consumption data is data3. The above data providers and data users agree that the statistical operation logic function is f(data1 + data2 + data3).

[0056] See Figure 1 The statistical data trustworthy transmission method shown includes:

[0057] S101. Generate a group public key according to the first public key of the first data provider and the obtained second public keys of at least one second data provider.

[0058] Among them, the first data provider and the second data provider are different. The data provider can refer to the data source. Different second data providers are different. The number of second data providers is at least one. Among multiple data sources, one data source can be arbitrarily selected, preset rules, or business requirements, etc., to select one data source as the first data provider, and the remaining data sources as the second data providers.

[0059] The first data provider generates an asymmetric key pair, and the asymmetric key pair includes a first private key and a first public key. Among them, the first private key is the confidential key held by the first data provider, and the first public key is the publicly available key generated by the first data provider. In some embodiments, the first public key and the first private key can be generated according to the key generation function KeyGen(1 λ )

[0060] For each second data provider, each second data provider generates an asymmetric key pair, and the asymmetric key pair includes a second public key and a second private key. Each second data provider sends the generated second public key to the first data provider. The first data provider can obtain the first public key generated by itself and the second public keys generated and sent by each second data provider.

[0061] The group public key can refer to the public key obtained by fusing the first public key and at least one second public key. In some embodiments, the first public key and each second public key can be concatenated to obtain the group public key. In some embodiments, the first public key and each second public key can be reconstructed by using the reconstruction function ‌GroupPK‌ to generate the group public key.

[0062] In one example, the number of second data providers is two. The first public key of the first data provider O1 is pk1, and the first private key is sk1. The second public key of the second data provider O2 is pk2, and the second private key is sk2. The third public key of the third data provider O3 is pk3, and the third private key is sk3. The group public key PK is calculated using the reconstruction function GroupPK(pk1, pk2, pk3).

[0063] S102. Generate a first homomorphic ciphertext based on the group public key and the first data corresponding to the obtained data user.

[0064] Among them, the data user may refer to the user who needs to obtain the source data. The first data corresponding to the data user may refer to the data that the data user needs to use at the first data provider. Usually, the data user will first agree with each data provider on the description information of the required data, and the data provider can determine the plaintext data to be provided according to the description information of the data. The first homomorphic ciphertext is the ciphertext of the first data. The first data is encrypted using the group public key to obtain the first homomorphic ciphertext. The first data can be encrypted in a homomorphic encryption manner to obtain the first homomorphic ciphertext. In one example, the first data is data1, and the first homomorphic ciphertext C1 can be calculated using the homomorphic encryption function HE.Enc(PK, data1).

[0065] S103. Receive the second homomorphic ciphertexts sent by each of the second data providers.

[0066] Among them, each second data provider can generate the second homomorphic ciphertext in the same way as the first data provider generates the homomorphic ciphertext. In one example, the second data provider O2 calculates the second homomorphic ciphertext C2 using the homomorphic encryption function HE.Enc(PK, data2). The second data provider O3 calculates the third homomorphic ciphertext C3 using the homomorphic encryption function HE.Enc(PK, data3).

[0067] S104. Calculate the first homomorphic ciphertext and each of the second homomorphic ciphertexts according to the statistical operation logic corresponding to the data user, and determine the fusion statistical result.

[0068] Among them, the statistical operation logic corresponding to the data user may refer to the logic of homomorphic operations. The data provider and the data user can pre-agree on the statistical operation logic. Substitute the first homomorphic ciphertext and each second homomorphic ciphertext into the statistical operation logic for calculation to obtain the fusion statistical result. In one example, the first homomorphic ciphertext and each second homomorphic ciphertext can be added to obtain the fusion statistical result. Another example is that the first homomorphic ciphertext and each second homomorphic ciphertext can be multiplied to obtain the fusion statistical result.

[0069] S105. Generate a first homomorphic authorization ciphertext based on the first attribute access control tree corresponding to the first data provider and the first data.

[0070] Among them, the first attribute access control tree may refer to a tree structure that restricts the attributes of the access object. The attribute access control tree (Access Tree) is the core structure used to define fine-grained access policies in the Attribute-Based Encryption (ABE) scheme. It restricts the attribute conditions that the access party needs to meet through a tree-shaped logical expression to decrypt the ciphertext.

[0071] In an example, the attribute encryption authorization function ABE.Pow(TY1, data1) can be used to calculate the first homomorphic authorization ciphertext PC1. Among them, TY1 is the set of leaf nodes of the attribute access control tree set by the data provider O1.

[0072] S106. Send the fusion statistical result and the first homomorphic authorization ciphertext to the data user, and call the multi-party secure computing interface of the corresponding computing function of the privacy computing according to the first private key corresponding to the first public key, so that the data user can verify the first homomorphic authorization ciphertext according to the fusion statistical result, and calculate and decrypt the passed first homomorphic authorization ciphertext to obtain the statistical result plaintext.

[0073] Among them, the fusion statistical result represents the verification information, and the first homomorphic authorization ciphertext represents the ciphertext. Based on the fusion statistical result and the first homomorphic authorization ciphertext, the data user verifies whether the data user passes the access control permission verification. When passing the access control permission verification, the first homomorphic authorization ciphertext and the second homomorphic authorization ciphertext of the second data provider are decrypted to obtain the statistical result plaintext.

[0074] In an optional embodiment, the calculating the first homomorphic ciphertext and each of the second homomorphic ciphertexts according to the statistical operation logic corresponding to the data user to determine the fusion statistical result includes: calculating the first homomorphic ciphertext and each of the second homomorphic ciphertexts according to the statistical operation logic corresponding to the data user to obtain a ciphertext operation result; performing a hash calculation on the ciphertext operation result to obtain the fusion statistical result.

[0075] Among them, the ciphertext operation result may be the result obtained by operating the homomorphic ciphertext according to the operation method corresponding to the homomorphic encryption. Among them, homomorphic encryption may refer to a cryptographic technology that allows direct calculation on encrypted data. The calculation result is the same as that of the plaintext calculation after decryption, achieving the effect of "data can be calculated but not seen". The statistical operation logic may be an operation logic that ensures that the operation result of the ciphertext is the same as that of the plaintext after decryption.

[0076] The result of ciphertext operation can be the result obtained by performing operations on homomorphic ciphertexts from multiple sources after homomorphic encryption using statistical operation logic. Hash calculation is used to further encrypt the result of ciphertext operation.

[0077] In one example, the service corresponding to the multi-party secure computing interface can provide the function of performing calculations based on data provided by multiple parties and sending it to the data user. The calculation logic of the calculation function can be pre-agreed and can be specifically configured according to business requirements. When each party invokes the multi-party secure computing interface, it can provide the identifier of the decryption task and the data used for decryption calculation. In the embodiments of the present invention, the data provided by the first data provider for decryption calculation is the first private key. The service corresponding to the multi-party secure computing interface performs decryption calculation on the private keys provided by multiple parties with the same decryption task identifier and the ciphertext to be decrypted to obtain the corresponding plaintext.

[0078] It can be seen that by performing calculations using statistical operation logic on the homomorphic ciphertext, the result of ciphertext operation is obtained, enabling the data user to obtain the result of the operation of multi-source data, realizing that the data user can obtain the fusion operation result of multi-source data without obtaining the source data of multiple sources, further improving the security of the source data, and performing hash calculation on the result of ciphertext operation to obtain the fusion statistical result and passing the fusion statistical result parameter to the data user, further improving the transmission security of the result of ciphertext operation.

[0079] The technical solution of the embodiments of the present invention is to obtain the first homomorphic ciphertext generated by itself and the second homomorphic ciphertext provided by at least one second data provider, generate a fusion statistical result, obtain the result obtained by statistically calculating the encrypted multi-source data, and generate a first homomorphic authorization ciphertext according to the attribute access control tree and the first data, and send the fusion statistical result and the homomorphic authorization ciphertext to the data user. The data user performs verification and decryption on the homomorphic authorization ciphertext according to the fusion statistical result, realizing further encryption of the homomorphic ciphertext, which can improve data security. At the same time, homomorphic ciphertexts of multiple data sources can be obtained, enabling encryption of the fusion result of multi-source data in the application scenario of multi-source data fusion, supporting the scenario of multi-source data fusion, and increasing the credibility of the statistical result of multi-source data.

[0080] Figure 2 It is a flowchart of a method for securely transmitting statistical data provided by the embodiments of the present invention. The embodiments of the present invention are applicable to the situation of performing fusion statistics on multi-source data in an encrypted scenario. This method can be executed by a statistical data secure transmission device, which can be implemented in the form of hardware and / or software. The statistical data secure transmission device can be configured in an electronic device that bears the statistical data secure transmission function of the data user, such as a client device or a server device.

[0081] It should be noted that for the parts not described in detail in the embodiments of the present invention, reference can be made to the descriptions of other embodiments.

[0082] See Figure 2 The statistical data credible transmission method shown in the figure includes:

[0083] S201. Receive the authorization parsing key jointly determined by at least one attribute key service party.

[0084] Among them, the attribute key service party is a server for generating an authorization decryption key. The authorization parsing key is used to decrypt the homomorphic authorization ciphertext. When the number of attribute key service parties is one, the key generated by the attribute key service party is determined as the authorization parsing key. When the number of attribute key service parties is two, the keys respectively generated by each attribute key service party are fused to obtain the authorization parsing key.

[0085] In one example, the number of attribute key service parties is at least two. The first attribute key service party generates the first attribute key distribution master key mk1; the second attribute key service party generates the second attribute key distribution master key mk2. According to the first attribute key distribution master key mk1 and the second attribute key distribution master key mk2, an authorization parsing key skp is generated. The authorization parsing key skp can be calculated by using the attribute key generation function AKGen(mk1, mk2).

[0086] In one example, each attribute key service party can call a multi-party secure computing interface to implement key fusion to obtain the authorization parsing key and send it to the data user. When each attribute key service party calls the multi-party secure computing interface, it can provide the identifier of the key generation task and the data used for key calculation. In the embodiments of the present invention, the data used for key calculation provided by the first attribute key service party is the first attribute key distribution master key mk1, and the data used for key calculation provided by the second attribute key service party is the second attribute key distribution master key mk2. The service corresponding to the multi-party secure computing interface fuses and calculates the attribute key distribution master keys provided by multiple parties with the same identifier of the key generation task to obtain the authorization parsing key.

[0087] S202. Receive the fused statistical result and the first homomorphic authorization ciphertext sent by the first data provider.

[0088] S203. Receive the second homomorphic authorization ciphertext sent by at least one second data provider.

[0089] For each second data provider, each second data provider generates a second homomorphic authorization ciphertext according to the second attribute access control tree and the second data corresponding to the second data provider. In one example, the second data of the second data provider O2 is data2, and the attribute encryption authorization function ABE.Pow(TY2, data2) can be used to calculate the second homomorphic authorization ciphertext PC2. Among them, TY2 is the leaf node set of the attribute access control tree set by the data provider O2. The second data of the second data provider O3 is data3, and the attribute encryption authorization function ABE.Pow(TY3, data3) can be used to calculate the second homomorphic authorization ciphertext PC3. Among them, TY3 is the leaf node set of the attribute access control tree set by the data provider O3.

[0090] In one example, the first data provider, the second data provider, and the third data provider provide different types of data. For example, different types of data in fields such as retail and e-commerce inventory management, educational resource allocation, and industrial network optimization. For example, in the field of educational resource allocation, the first teacher data data1 is the number of teachers 10, the second teacher data data2 is the number of teachers 15, and the second teacher data data3 is the number of teachers 24. Another example is that in the field of retail and e-commerce inventory management, the first consumption data data1 is the consumption amount 1,000,000, the second consumption data data2 is the consumption amount 100,000, and the second consumption data data3 is the consumption amount 20,000,000.

[0091] S204. Parse the first homomorphic authorization ciphertext according to the authorization parsing key to obtain a first ciphertext to be verified.

[0092] Actually, when the attributes of the data user satisfy the attribute function access control tree of the data provider, the ciphertext to be verified is the same as the homomorphic ciphertext. Specifically, when the attributes of the data user satisfy the attribute function access control tree of the first data provider, the first ciphertext to be verified is the same as the first homomorphic ciphertext. In one example, the first ciphertext to be verified C1' is calculated based on the authorization parsing function ABE.Dec(PC1, skp); when the attributes of the data user satisfy the attribute function access control tree of the first data provider O1, C1' == C1.

[0093] S205. Parse each of the second homomorphic authorization ciphertexts according to the authorization parsing key to obtain second ciphertexts to be verified corresponding to each of the second homomorphic authorization ciphertexts.

[0094] Specifically, when the attributes of the data user satisfy the attribute function access control tree of the second data provider, the second ciphertext to be verified is the same as the second homomorphic ciphertext. In an example, the second ciphertext to be verified C2' is calculated based on the authorization parsing function ABE.Dec(PC2, skp); when the attributes of the data user satisfy the attribute function access control tree of the second data provider O2, C2' == C2. The second ciphertext to be verified C3' is calculated based on the authorization parsing function ABE.Dec(PC3, skp); when the attributes of the data user satisfy the attribute function access control tree of the second data provider O3, C3' == C3.

[0095] As in the previous example, the attribute function access control tree structure is as follows:

[0096] AND (logical AND)

[0097] ├── Department: Academic Affairs Office

[0098] ├── OR (logical OR)

[0099] │├── Role: School-level Administrator

[0100] │└── Role: Data Statistician

[0101] ├── Purpose of Access: Internal Statistics

[0102] └── Compliance Status: Annual Audit Passed

[0103] ‌Nodes in the attribute function access control tree:‌ Department node‌: The department to which the user belongs must be the‌ Academic Affairs Office‌ to ensure that data access rights match responsibilities.‌ Role node‌: The user must have the role of‌ school-level administrator‌ or‌ data statistician‌. The former is used for global management, and the latter is used for statistical requirements such as generating reports.‌ Purpose of access node‌: The purpose of the data must be clearly defined as‌ internal statistics‌ to exclude potential risk scenarios such as external cooperation or business analysis.‌ Compliance status node‌: The user must pass the‌ annual data security audit‌ to ensure that their data processing process complies with the school's privacy protection regulations.

[0104] S206. Calculate the first ciphertext to be verified and each of the second ciphertexts to be verified according to the statistical operation logic to determine the homomorphic statistical result.

[0105] Among them, the homomorphic statistical result may refer to the result obtained by calculating the ciphertext to be verified through the statistical operation logic. When the first ciphertext to be verified is the same as the first homomorphic ciphertext, and the second ciphertext to be verified is the same as the second homomorphic ciphertext, the homomorphic statistical result corresponds to the fusion statistical result.

[0106] As in the previous example, the statistical operation logic is to calculate the sum of the first data and each second data. The homomorphic statistical result is the ciphertext of the sum of the first consumption data data1, the second consumption data data2, and the second consumption data data3, that is, 1000000 + 100000 + 20000000 = 21100000.

[0107] S207. According to the fusion statistical result, perform a correctness check on the homomorphic statistical result.

[0108] Among them, the correctness check is used to check whether the homomorphic statistical result is correct. It can detect whether the fusion statistical result corresponds to the homomorphic statistical result, so as to perform a correctness check on the homomorphic statistical result.

[0109] S208. When the correctness check of the homomorphic statistical result passes, decrypt the homomorphic statistical result to obtain the plaintext of the statistical result.

[0110] In some embodiments, when the fusion statistical result corresponds to the homomorphic statistical result, it is determined that the homomorphic statistical result is correct and the correctness check passes. When the fusion statistical result does not correspond to the homomorphic statistical result, it is determined that the homomorphic statistical result is incorrect and the correctness check fails. In fact, passing the correctness check indicates that the homomorphic statistical result is correct. Specifically, the homomorphic statistical result corresponds to the fusion statistical result. Correspondingly, it is determined that the first ciphertext to be verified of the homomorphic statistical result is the same as the first homomorphic ciphertext, and the second ciphertext to be verified is the same as the second homomorphic ciphertext, indicating that the attributes of the data user satisfy the attribute access control tree of the first data provider, and the attributes of the data user satisfy the attribute access control tree of the second data provider. Failing the correctness check indicates that the homomorphic statistical result is incorrect. Specifically, the homomorphic statistical result does not correspond to the fusion statistical result. Correspondingly, it is determined that the first ciphertext to be verified of the homomorphic statistical result is different from the first homomorphic ciphertext, or the second ciphertext to be verified is different from the second homomorphic ciphertext, indicating that the attributes of the data user do not satisfy the attribute access control tree of the first data provider, or the attributes of the data user do not satisfy the attribute access control tree of the second data provider.

[0111] In some embodiments, when the fusion statistical result is the result obtained by calculating the homomorphic ciphertext using the statistical operation logic, the correspondence between the fusion statistical result and the homomorphic statistical result may mean that the fusion statistical result is the same as the homomorphic statistical result, and the non-correspondence between the fusion statistical result and the homomorphic statistical result may mean that the fusion statistical result is different from the homomorphic statistical result.

[0112] When the homomorphic statistical result is correct, perform joint decryption of the homomorphic statistical result and the private key provided by the data provider to decrypt the homomorphic statistical result to obtain the plaintext of the statistical result. The plaintext of the statistical result may refer to the result obtained by calculating the data of multiple data sources according to the statistical operation logic.

[0113] In an embodiment of the present invention, a homomorphic authorization ciphertext is parsed by obtaining an authorized parsing key to obtain a ciphertext to be verified, and a statistical operation logic is used to operate on the ciphertext to be verified to obtain a homomorphic statistical result. Based on the fused statistical result, the correctness of the homomorphic statistical result is verified. When the correctness verification of the homomorphic statistical result passes, at this time, the data user realizes attribute decryption, and then decrypts the homomorphic statistical result to obtain the plaintext of the statistical result. The plaintext of the statistical result encrypted by attribute encryption and homomorphic encryption can be correctly obtained, improving data security. And directly obtaining the plaintext of the statistical result without obtaining the plaintext of the source data can avoid the leakage of the source data, improve the security of the source data, and the plaintext of the statistical result is obtained by statistical operations on the source data provided by multiple data providers, realizing the acquisition of the fused result of multi-source data after layer-by-layer encryption, supporting the scenario of multi-source data fusion, and improving the data security of multi-source data.

[0114] In an optional embodiment, decrypting the homomorphic statistical result to obtain the plaintext of the statistical result includes: according to the homomorphic statistical result, invoking the multi-party secure computing interface of the corresponding computing function of the privacy computing to obtain the feedback plaintext of the statistical result; the multi-party secure computing interface is used to receive the first private key sent by the first data provider and the second private keys sent by each of the second data providers, and decrypt the homomorphic statistical result according to the first private key and each of the second private keys to obtain the plaintext of the statistical result.

[0115] Among them, the multi-party secure computing interface of the corresponding computing function of the privacy computing is invoked, and the homomorphic statistical result is passed into the multi-party secure computing interface. The multi-party secure computing interface is also used to receive the first private key sent by the first data provider and the second private keys sent by each of the second data providers, and parse the homomorphic statistical result according to the first private key and each of the second private keys to obtain the plaintext of the statistical result. Both the first data provider and each of the second data providers can invoke the multi-party secure computing interface of the corresponding computing function of the privacy computing. The first data provider invokes the multi-party secure computing interface to send the first private key, and each of the second data providers invokes the multi-party secure computing interface to send each of the second private keys.

[0116] In an example, the data for decryption calculation provided by the first data provider is the first private key. The data for decryption calculation provided by the second data provider is the second private key. The data for decryption calculation provided by the data user is the homomorphic statistical result, where the homomorphic statistical result is a ciphertext. The service corresponding to the multi-party secure computing interface decrypts the homomorphic statistical result with the private keys provided by multiple parties with the same decryption task identifier to obtain the plaintext of the statistical result.

[0117] It can be seen that by using the multi-party secure computing interface to decrypt the homomorphic statistical result based on the first private key and each second private key, it is possible to avoid sending the private key to the data user and at the same time decrypt the homomorphic statistical result, which can improve the security of the key for decrypting the homomorphic statistical result.

[0118] In an optional embodiment, performing a correctness verification on the homomorphic statistical result according to the fusion statistical result includes: performing a hash calculation on the homomorphic statistical result to obtain a statistical result to be verified; detecting whether the fusion statistical result is consistent with the statistical result to be verified; when the fusion statistical result is consistent with the statistical result to be verified, determining that the correctness verification of the homomorphic statistical result passes.

[0119] Among them, the fusion statistical result is obtained by performing a hash calculation after calculating the homomorphic ciphertext according to the statistical operation logic. The homomorphic statistical result is the result obtained after calculating the ciphertext to be verified according to the same statistical operation logic. When the ciphertext to be verified and the homomorphic ciphertext correspond identically, the result obtained after performing a hash calculation on the homomorphic statistical result should be consistent with the fusion statistical result. When there is at least one ciphertext to be verified that is different from the corresponding homomorphic ciphertext, the result obtained after performing a hash calculation on the homomorphic statistical result should be inconsistent with the fusion statistical result. The calculation logic of the hash calculation can be pre-agreed.

[0120] In an example, the homomorphic statistical result is CF(data1,data2,data3)', and performing a hash calculation on the homomorphic statistical result, the statistical result to be verified obtained is H(CF(data1,data2,data3)'). The fusion statistical result is HCC. Using the correctness verification function EQ(HCC, H(CF(data1,data2,data3)'), detect whether HCC and H(CF(data1,data2,data3)') are consistent. When they are consistent, determine that the correctness verification passes. When they are inconsistent, determine that the correctness verification fails.

[0121] As in the previous example, when the fusion statistical result HCC is equal to the homomorphic statistical result H(C(1000000 + 100000 + 20000000 = 21100000)), determine that the correctness verification passes.

[0122] It can be seen that by performing a hash calculation on the homomorphic statistical result to obtain a statistical result to be verified and detecting whether the statistical result to be verified is consistent with the fusion statistical result, according to the detection result of whether they are consistent, determine the correctness verification result of the homomorphic statistical result, improve the accuracy of the homomorphic statistical result, and can further perform hash encryption on the statistical result to improve the security of the statistical result.

[0123] In a specific example, the method for securely transmitting statistical data may include:

[0124] First, the key distribution phase:

[0125] 1. Generate a distributed asymmetric key pair:

[0126] The first data provider O1 generates a first asymmetric key pair based on the key generation function KeyGen(1 λ ): The first public key is pk1 and the first private key is sk1.

[0127] The second data provider O2 generates a second asymmetric key pair based on the key generation function KeyGen(1 λ ): The second public key is pk2 and the second private key is sk2.

[0128] The third data provider O3 generates a third asymmetric key pair based on the key generation function KeyGen(1 λ ): The third public key is pk3 and the third private key is sk3.

[0129] 2. Reconstruct the group public key:

[0130] The group public key PK is calculated using the reconstruction function GroupPK(pk1, pk2, pk3).

[0131] 3. Calculate the key distribution parameters:

[0132] The first attribute key service provider S1 generates a first attribute key distribution master key mk1 based on the master key generation function MKGen(1 λ );

[0133] The second attribute key service provider S2 generates a second attribute key distribution master key mk2 based on the master key generation function MKGen(1 λ );

[0134] 4. Jointly calculate the attribute key

[0135] The first attribute key service provider S1 and the second attribute key service provider S2 generate an authorization resolution key skp based on the attribute key generation function AKGen(mk1, mk2), call the secure calculation result, and send it to the data user U.

[0136] Phase two, the homomorphic encryption phase:

[0137] 5. Calculate the homomorphic ciphertext of the fused data:

[0138] The first data of the first data provider O1 is data1, and the first homomorphic ciphertext C1 is calculated based on the homomorphic encryption function HE.Enc(PK, data1).

[0139] The second data of the second data provider O2 is data2. The second homomorphic ciphertext C2 is calculated using the homomorphic encryption function HE.Enc(PK, data2) and sent to the first data provider O1.

[0140] The second data of the second data provider O3 is data3. The third homomorphic ciphertext C3 is calculated using the homomorphic encryption function HE.Enc(PK, data3) and sent to the first data provider O1.

[0141] 6. Calculate the verification code of the fusion calculation result

[0142] The first data provider O1 calculates the fusion statistical result HCC using the homomorphic calculation function HE.Eval(F, C1, C2, C3) and the secure hash function H, and sends it to the data user U.

[0143] Among them, F is the statistical operation logic, which is determined by the data user U.

[0144] Phase Three: Ciphertext Authorization Phase:

[0145] 7. Calculate the homomorphic authorization ciphertext:

[0146] The first data provider O1 calculates the first homomorphic authorization ciphertext PC1 based on the attribute-based encryption authorization function ABE.Pow(TY1, data1). Among them, TY1 is the leaf node set of the attribute access control tree set by the data provider O1.

[0147] The second data provider O2 calculates the second homomorphic authorization ciphertext PC2 based on the attribute-based encryption authorization function ABE.Pow(TY2, data2). Among them, TY2 is the leaf node set of the attribute access control tree set by the data provider O2.

[0148] The second data of the second data provider O3, data3, calculates the second homomorphic authorization ciphertext PC3 based on the attribute-based encryption authorization function ABE.Pow(TY3, data3). Among them, TY3 is the leaf node set of the attribute access control tree set by the data provider O3.

[0149] Phase Four: Fusion Calculation Phase:

[0150] 8. Parse the authorization ciphertext:

[0151] The data user U calculates the first ciphertext to be verified C1' based on the authorization parsing function ABE.Dec(PC1, skp); when the attributes of the data user satisfy the attribute function access control tree of the first data provider O1, C1' == C1.

[0152] The second ciphertext to be verified C2' is calculated based on the authorized parsing function ABE.Dec(PC2, skp); when the attributes of the data user satisfy the attribute function access control tree of the second data provider O2, C2' == C2.

[0153] The second ciphertext to be verified C3' is calculated based on the authorized parsing function ABE.Dec(PC3, skp); when the attributes of the data user satisfy the attribute function access control tree of the second data provider O3, C3' == C3.

[0154] When C1' == C1, C2' == C2, and C3' == C3, the data user U successfully accesses the homomorphic ciphertext.

[0155] 9. Fusing multi-key homomorphic ciphertexts:

[0156] The data user U calculates the homomorphic statistical result CF(data1, data2, data3)' based on the homomorphic calculation function HE.Eval(F, C1', C2', C3'). And uses the same secure hash function H as before to calculate the statistical result to be verified H(CF(data1, data2, data3)'.

[0157] 10. Verifying the correctness of the fused ciphertext:

[0158] The data user U, based on the correctness verification function EQ(HCC, H(CF(data1, data2, data3)'), detects whether HCC and H(CF(data1, data2, data3)') are consistent, so as to determine whether the homomorphic statistical result CF(data1, data2, data3)' is correct.

[0159] 11. Decrypting the fused ciphertext:

[0160] When the correctness verification of the homomorphic statistical result passes, the data user U, in combination with the first data provider O1, the second data provider O2, and the second data provider O3, based on the decryption function HE.Dec(sk1, sk2, sk3, CF(data1, data2, data3)'), calls the multi-party secure calculation interface of the corresponding calculation function of the privacy calculation, and jointly decrypts to obtain the plaintext of the statistical result F(data_1, data_2, data_3).

[0161] Figure 3A structural schematic diagram of a statistical data trusted transmission device provided by an embodiment of the present invention. The embodiment of the present invention is applicable to the situation of fusing and statistically analyzing multi-source data in an encryption scenario. The device can execute a statistical data trusted transmission method, and the device can be implemented in the form of hardware and / or software. The device can be configured in an electronic device of a first data provider that bears the statistical data trusted transmission function.

[0162] See Figure 3 The statistical data trusted transmission device shown in the figure includes:

[0163] A group public key generation module 301, configured to generate a group public key according to the first public key of the first data provider and at least one second public key of the obtained second data providers;

[0164] A first homomorphic ciphertext acquisition module 302, configured to generate a first homomorphic ciphertext according to the group public key and the first data corresponding to the obtained data user;

[0165] A second homomorphic ciphertext acquisition module 303, configured to receive second homomorphic ciphertexts sent by the second data providers;

[0166] A statistical calculation module 304, configured to calculate the first homomorphic ciphertext and each of the second homomorphic ciphertexts according to the statistical operation logic corresponding to the data user, and determine a fusion statistical result;

[0167] An authorized ciphertext generation module 305, configured to generate a first homomorphic authorized ciphertext according to the first attribute access control tree corresponding to the first data provider and the first data;

[0168] A data sending module 306, configured to send the fusion statistical result and the first homomorphic authorized ciphertext to the data user, and call a multi-party secure calculation interface of a privacy calculation corresponding calculation function according to the first private key corresponding to the first public key, so that the data user verifies the first homomorphic authorized ciphertext according to the fusion statistical result, and decrypts and calculates the first homomorphic authorized ciphertext that passes the verification to obtain a statistical result plaintext.

[0169] Optionally, the statistical calculation module 304 includes:

[0170] A homomorphic calculation unit, configured to calculate the first homomorphic ciphertext and each of the second homomorphic ciphertexts according to the statistical operation logic corresponding to the data user, and obtain a ciphertext operation result;

[0171] A hash calculation unit, configured to perform a hash calculation on the ciphertext operation result to obtain a fusion statistical result.

[0172] The statistical data trustworthy transmission device provided by the embodiments of the present invention can execute the statistical data trustworthy transmission method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0173] Figure 4 It is a schematic structural diagram of a statistical data trustworthy transmission device provided by an embodiment of the present invention. The embodiments of the present invention are applicable to the situation of fusing and counting multi-source data in an encryption scenario. This device can execute the statistical data trustworthy transmission method, and can be implemented in the form of hardware and / or software. This device can be configured in an electronic device of a data user that bears the statistical data trustworthy transmission function.

[0174] See Figure 4 the statistical data trustworthy transmission device shown in

[0175] An authorization key receiving module 401, configured to receive an authorization parsing key jointly determined by at least one attribute key service party;

[0176] A first ciphertext receiving module 402, configured to receive a fused statistical result and a first homomorphic authorization ciphertext sent by a first data provider;

[0177] A second ciphertext receiving module 403, configured to receive second homomorphic authorization ciphertexts sent by at least one second data provider;

[0178] A first ciphertext parsing module 404, configured to parse the first homomorphic authorization ciphertext according to the authorization parsing key to obtain a first ciphertext to be verified;

[0179] A second ciphertext parsing module 405, configured to parse each of the second homomorphic authorization ciphertexts according to the authorization parsing key to obtain second ciphertexts to be verified corresponding to each of the second homomorphic authorization ciphertexts;

[0180] A homomorphic statistical calculation module 406, configured to calculate the first ciphertext to be verified and each of the second ciphertexts to be verified according to a statistical operation logic to determine a homomorphic statistical result;

[0181] A statistical result verification module 407, configured to perform a correctness verification on the homomorphic statistical result according to the fused statistical result;

[0182] A plaintext obtaining module 408, configured to decrypt the homomorphic statistical result to obtain a statistical result plaintext when the correctness verification of the homomorphic statistical result passes.

[0183] Optionally, the plaintext obtaining module 408 includes:

[0184] A secure computing call unit, configured to call a multi-party secure computing interface of a corresponding computing function of privacy computing according to the homomorphic statistical result, and obtain a plaintext of the statistical result as feedback; the multi-party secure computing interface is configured to receive a first private key sent by the first data provider and second private keys sent by each of the second data providers, and decrypt the homomorphic statistical result according to the first private key and each of the second private keys to obtain a plaintext of the statistical result.

[0185] Optionally, the statistical result verification module 407 includes:

[0186] A hash calculation unit, configured to perform a hash calculation on the homomorphic statistical result to obtain a statistical result to be verified;

[0187] A consistency detection unit, configured to detect whether the fused statistical result is consistent with the statistical result to be verified;

[0188] A correctness verification unit, configured to determine that the correctness verification of the homomorphic statistical result passes when the fused statistical result is consistent with the statistical result to be verified.

[0189] The statistical data trustworthy transmission device provided by the embodiments of the present invention can execute the statistical data trustworthy transmission method provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method.

[0190] In the technical solution of the embodiments of the present invention, the acquisition, storage, and application of the data involved all comply with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0191] Figure 5 FIG. shows a schematic structural diagram of an electronic device 500 that can be used to implement the embodiments of the present invention. The electronic device is intended to represent various forms of digital computers, such as, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, a personal digital processor, a cellular phone, a smart phone, a wearable device (such as a helmet, glasses, a watch, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present invention described herein and / or claimed.

[0192] As Figure 5As shown, the electronic device 500 includes at least one processor 501 and a memory communicatively connected to the at least one processor 501, such as read-only memory (ROM) 502, random access memory (RAM) 503, etc. The memory stores a computer program executable by the at least one processor. The processor 501 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 502 or the computer program loaded from the storage unit 508 into the random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic device 500 can also be stored. The processor 501, ROM 502, and RAM 503 are connected to each other via a bus 504. The input / output (I / O) interface 505 is also connected to the bus 504.

[0193] Multiple components in the electronic device 500 are connected to the I / O interface 505, including: an input unit 506, such as a keyboard, a mouse, etc.; an output unit 507, such as various types of displays, speakers, etc.; a storage unit 508, such as a magnetic disk, an optical disc, etc.; and a communication unit 509, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 509 allows the electronic device 500 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0194] The processor 501 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 501 include but are not limited to a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 501 executes the various methods and processes described above, such as the statistical data trustworthy transmission method.

[0195] In some embodiments, the statistical data trustworthy transmission method can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as the storage unit 508. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 500 via the ROM 502 and / or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the processor 501, one or more steps of the statistical data trustworthy transmission method described above can be executed. Alternatively, in other embodiments, the processor 501 can be configured to execute the statistical data trustworthy transmission method by any other appropriate means (such as by means of firmware).

[0196] The various embodiments of the systems and techniques described above in this specification can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGA), application specific integrated circuits (ASIC), application specific standard products (ASSP), systems on chip (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that receives data and instructions from, and transmits data and instructions to, a storage system, at least one input device, and at least one output device.

[0197] The computer programs for implementing the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The computer programs can be executed entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

[0198] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, 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), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0199] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and a pointing device (e.g., a mouse or a trackball), by which the user can provide input to the electronic device. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0200] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.

[0201] The computing system can include a client and a server. The client and the server are generally far from each other and usually interact through a communication network. The client-server relationship is created by computer programs running on respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, and solves the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS (Virtual Private Server) services.

[0202] It should be understood that various forms of the processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is imposed herein.

[0203] The above specific embodiments do not constitute a limitation to the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for reliable transmission of statistical data, characterized in that, The method is applied to a first data provider and includes: Generating a group public key according to the first public key of the first data provider and the obtained second public keys of at least one second data provider; Generating a first homomorphic ciphertext according to the group public key and the first data corresponding to the obtained data user; Receiving second homomorphic ciphertexts sent by each of the second data providers; Calculating the first homomorphic ciphertext and each of the second homomorphic ciphertexts according to the statistical operation logic corresponding to the data user to determine a fusion statistical result; Generating a first homomorphic authorization ciphertext according to the first attribute access control tree corresponding to the first data provider and the first data; the first attribute access control tree is a tree structure that restricts the attributes of an access object and is used to define a fine-grained access policy; Sending the fusion statistical result and the first homomorphic authorization ciphertext to the data user, so that the data user verifies the first homomorphic authorization ciphertext according to the fusion statistical result. When the verification passes, according to the first private key corresponding to the first public key of the first data provider and the second private keys corresponding to the second public keys of each of the second data providers, calling the multi-party secure computing interface of the corresponding computing function of the privacy computing, decrypting and calculating the verified first homomorphic authorization ciphertext to obtain the plaintext of the statistical result, and sending the plaintext of the statistical result to the data user.

2. The method according to claim 1, wherein The calculating the first homomorphic ciphertext and each of the second homomorphic ciphertexts according to the statistical operation logic corresponding to the data user to determine a fusion statistical result includes: Calculating the first homomorphic ciphertext and each of the second homomorphic ciphertexts according to the statistical operation logic corresponding to the data user to obtain a ciphertext operation result; Performing a hash calculation on the ciphertext operation result to obtain a fusion statistical result.

3. A method for reliable transmission of statistical data, characterized in that, The method is applied to a data user and includes: Receiving an authorization parsing key jointly determined by at least one attribute key service provider; Receiving a fusion statistical result and a first homomorphic authorization ciphertext sent by a first data provider; Receiving second homomorphic authorization ciphertexts sent by at least one second data provider; Parsing the first homomorphic authorization ciphertext according to the authorization parsing key to obtain a first ciphertext to be verified; Parsing each of the second homomorphic authorization ciphertexts according to the authorization parsing key to obtain second ciphertexts to be verified corresponding to each of the second homomorphic authorization ciphertexts; Calculating the first ciphertext to be verified and each of the second ciphertexts to be verified according to the statistical operation logic to determine a homomorphic statistical result; Performing a correctness verification on the homomorphic statistical result according to the fusion statistical result; When the correctness verification of the homomorphic statistical result passes, according to the homomorphic statistical result, calling the multi-party secure computing interface of the corresponding computing function of the privacy computing to obtain the fed-back plaintext of the statistical result; the multi-party secure computing interface is used to receive the first private key sent by the first data provider and the second private keys sent by each of the second data providers, and decrypt the homomorphic statistical result according to the first private key and each of the second private keys to obtain the plaintext of the statistical result.

4. The method according to claim 3, wherein Performing a correctness verification on the homomorphic statistical result according to the fusion statistical result includes: Performing a hash calculation on the homomorphic statistical result to obtain a statistical result to be verified; Detecting whether the fusion statistical result is consistent with the statistical result to be verified; When the fusion statistical result is consistent with the statistical result to be verified, determining that the correctness verification of the homomorphic statistical result passes.

5. A statistical data reliable transmission device, characterized in that, The device is configured in a first data provider, and the device includes: A group public key generation module, configured to generate a group public key according to a first public key of the first data provider and at least one second public key of the obtained second data providers; A first homomorphic ciphertext obtaining module, configured to generate a first homomorphic ciphertext according to the group public key and first data corresponding to the obtained data user; A second homomorphic ciphertext obtaining module, configured to receive second homomorphic ciphertexts sent by the second data providers; A statistical calculation module, configured to perform calculations on the first homomorphic ciphertext and the second homomorphic ciphertexts according to the statistical operation logic corresponding to the data user to determine a fusion statistical result; An authorized ciphertext generation module, configured to generate a first homomorphic authorized ciphertext according to a first attribute access control tree corresponding to the first data provider and the first data; the first attribute access control tree is a tree structure that restricts the attributes of an access object and is used to define a fine-grained access policy; A data sending module, configured to send the fusion statistical result and the first homomorphic authorized ciphertext to the data user, so that the data user performs a verification on the first homomorphic authorized ciphertext according to the fusion statistical result. When the verification passes, according to a first private key corresponding to the first public key of the first data provider and second private keys corresponding to the second public keys of the second data providers, call a multi-party secure calculation interface of a corresponding calculation function of the privacy calculation, decrypt and calculate the verified first homomorphic authorized ciphertext to obtain a statistical result plaintext, and send the statistical result plaintext to the data user.

6. The device according to claim 5, characterized in that The statistical calculation module includes: A homomorphic calculation unit, configured to perform calculations on the first homomorphic ciphertext and the second homomorphic ciphertexts according to the statistical operation logic corresponding to the data user to obtain a ciphertext operation result; A hash calculation unit, configured to perform a hash calculation on the ciphertext operation result to obtain a fusion statistical result.

7. A statistical data reliable transmission device, characterized in that The device is configured in a data user, and the device includes: An authorized key receiving module, configured to receive an authorized parsing key jointly determined by at least one attribute key service provider; A first ciphertext receiving module, configured to receive a fusion statistical result and a first homomorphic authorized ciphertext sent by a first data provider; A second ciphertext receiving module, configured to receive second homomorphic authorized ciphertexts sent by at least one second data provider; A first ciphertext parsing module, configured to parse the first homomorphic authorized ciphertext according to the authorized parsing key to obtain a first ciphertext to be verified; A second ciphertext parsing module, configured to parse the second homomorphic authorized ciphertexts according to the authorized parsing key to obtain second ciphertexts to be verified corresponding to the second homomorphic authorized ciphertexts; A homomorphic statistical calculation module, configured to perform calculations on the first ciphertext to be verified and each of the second ciphertexts to be verified according to statistical operation logic, and determine a homomorphic statistical result; A statistical result verification module, configured to perform a correctness verification on the homomorphic statistical result according to the fused statistical result; A plaintext acquisition module, configured to, when the correctness verification of the homomorphic statistical result passes, call a multi-party secure calculation interface of a corresponding calculation function of privacy calculation according to the homomorphic statistical result, and obtain a fed-back plaintext of the statistical result; the multi-party secure calculation interface is configured to receive a first private key sent by the first data provider and second private keys sent by each of the second data providers, and decrypt the homomorphic statistical result according to the first private key and each of the second private keys to obtain a plaintext of the statistical result.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the statistical data trustworthy transmission method according to any one of claims 1-4.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, and the computer instructions are used to implement the statistical data trustworthy transmission method according to any one of claims 1-4 when executed by a processor.

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