Statistical data trusted transmission method and device, electronic equipment and storage medium

Abstract

The invention discloses a statistical data trusted transmission method and device, electronic equipment and a storage medium. The method comprises the following steps: generating a group public key according to a first public key of a first data provider and an obtained second public key of at least one second data provider; generating a first homomorphic ciphertext according to the group public key and acquired first data corresponding to a data user; receiving a second homomorphic ciphertext sent by each second data provider; calculating the first homomorphic ciphertext and each second homomorphic ciphertext, and determining a fusion statistical result; according to a first attribute access control tree corresponding to the first data provider and the first data, generating a first homomorphic authorization ciphertext; and sending the fusion statistical result and the first homomorphic authorization ciphertext to the data user, and calling a multi-party security computing interface according to a first private key corresponding to the first public key. According to the technical scheme of the embodiment of the invention, the security of the multi-source fusion data can be improved.

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 reliable transmission of statistical data. Background Art

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

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

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

[0005] According to one aspect of the present invention, a method for reliable transmission of statistical data is provided, the method being applied to a first data provider, the method comprising:

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

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

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

[0009] 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;

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

[0011] The fused statistical result and the first homomorphic authorization ciphertext are sent to the data user, and according to the first private key corresponding to the first public key, the multi-party secure computing interface of the corresponding computing function of the privacy computing is called, so that the data user verifies the first homomorphic authorization ciphertext according to the fused statistical result, and obtains the plaintext of the statistical result based on the decryption calculation of the verified first homomorphic authorization ciphertext.

[0012] According to one aspect of the present invention, a method for reliable transmission of statistical data is provided, the method being applied to a data user, the method comprising:

[0013] Receiving an authorization resolution key jointly determined by at least one attribute key service party;

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

[0015] Receiving a second homomorphic authorization ciphertext sent by at least one second data provider;

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

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

[0018] According to the statistical operation logic, the first ciphertext to be verified and each of the second ciphertext to be verified are calculated to determine a homomorphic statistical result;

[0019] According to the fusion statistical result, the correctness of the homomorphic statistical result is verified;

[0020] When the correctness check of the homomorphic statistical result passes, the homomorphic statistical result is decrypted to obtain the plain text of the statistical result.

[0021] According to another aspect of the present invention, a device for reliable transmission of statistical data is provided, the device being configured at a first data provider, the device comprising:

[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 acquired second public key of at least one second data provider;

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

[0024] A second homomorphic ciphertext acquisition module, used for receiving the second homomorphic ciphertext sent by each of the second data providers;

[0025] A statistical calculation module, used 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;

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

[0027] A data sending module is used to send the fused 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 fused statistical result, and obtain the plaintext of the statistical result based on the decryption calculation of the verified first homomorphic authorization ciphertext.

[0028] According to another aspect of the present invention, a reliable transmission device for statistical data is provided, the device being configured at a data user, the device comprising:

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

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

[0031] A second ciphertext receiving module, used to receive a second homomorphic authorization ciphertext sent by at least one second data provider;

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

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

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

[0035] A statistical result verification module, used for verifying the correctness of the homomorphic statistical result according to the fused statistical result;

[0036] The plaintext acquisition module is used to decrypt the homomorphic statistical result to obtain the plaintext of the statistical result when the correctness check of the homomorphic statistical result passes.

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

[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 that can be executed 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 trusted transmission method described in any embodiment of the present invention.

[0041] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the statistical data trusted transmission method described in any embodiment of the present invention when executed.

[0042] The technical solution of the embodiment of the present invention obtains a first homomorphic ciphertext generated by itself and a second homomorphic ciphertext provided by at least one second data provider, and generates a fusion statistical result to obtain the encrypted multi-source data statistics result, and generates a first homomorphic authorization ciphertext according to an attribute access control tree and the first data, and sends the fusion statistical result and the homomorphic authorization ciphertext to the data user, and the data user verifies and decrypts the homomorphic authorization ciphertext according to the fusion statistical result to further encrypt the homomorphic ciphertext, which can improve data security, and at the same time, can obtain homomorphic ciphertexts of multiple data sources, and realize encryption of the fusion result of multi-source data in the application scenario of multi-source data fusion, support multi-source data fusion scenarios, and at the same time increase the credibility of the statistical results of multi-source data.

[0043] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0045] Figure 1 is a flow chart of a method for reliable transmission of statistical data provided according to an embodiment of the present invention;

[0046] Figure 2 is a flow chart of a method for reliable transmission of statistical data provided according to an embodiment of the present invention;

[0047] Figure 3 is a schematic diagram of the structure of a reliable transmission device for statistical data provided according to an embodiment of the present invention;

[0048] Figure 4is a schematic diagram of the structure of a reliable transmission device for statistical data provided according to an embodiment of the present invention;

[0049] Figure 5 It is a schematic diagram of the structure of an electronic device that implements the method for reliable transmission of statistical data according to an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0051] It should be noted that the terms "first", "second", etc. in the specification 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 the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0052] Figure 1 A flowchart of a method for trusted transmission of statistical data provided by an embodiment of the present invention. The embodiment of the present invention can be applied to the situation of fusion statistics of multi-source data in an encrypted scenario, and can be specifically applied to the fields of e-commerce, education, cloud computing, and artificial intelligence, etc. The method can be executed by a trusted transmission device for statistical data, which can be implemented in the form of hardware and / or software, and can be configured in an electronic device of the first data provider that carries the trusted transmission function of statistical data, such as a client device or a server device.

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

[0054] Taking the actual application scenario of aggregating regional consumption data on several e-commerce platforms as an example, without exposing commercial secrets such as the sales growth rate of commodity categories, this method is used to encrypt and calculate consumption fluctuations of bulk commodities such as home appliances or automobiles to support macroeconomic policy adjustments.

[0055] Assume that the data user is the statistical party U, the first data provider is the first e-commerce platform P1, and 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 also Figure 1 The statistical data trustworthy transmission methods shown include:

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

[0058] The first data provider and the second data provider are different. The data provider may refer to a 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 may be selected as the first data provider by arbitrary selection, preset rules or business requirements, and the remaining data sources may be used as the second data providers.

[0059] The first data provider generates an asymmetric key pair, which includes a first private key and a first public key. The first private key is a secret key held by the first data provider, and the first public key is a key generated by the first data provider that can be made public. In some embodiments, the key generation function KeyGen(1 λ )Generate a first public key and a first private key.

[0060] For each second data provider, each second data provider generates an asymmetric key pair, the asymmetric key pair including 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 key generated and sent by each second data provider.

[0061] The group public key may refer to a public key obtained by merging the first public key and at least one second public key. In some embodiments, the first public key and each second public key may be concatenated to obtain the group public key. In some embodiments, the reconstruction function ‌GroupPK‌ may be used to reconstruct the first public key and each second public key 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 second data provider O3 is pk3, and the third private key is sk3. The reconstruction function GroupPK(pk1, pk2, pk3) is used to calculate the group public key PK.

[0063] S102. Generate a first homomorphic ciphertext according to the group public key and the first data corresponding to the acquired 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 that needs to be provided based on 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 using homomorphic encryption to obtain the first homomorphic ciphertext. In an example, the first data is data1, and the homomorphic encryption function HE.Enc(PK,data1) can be used to calculate the first homomorphic ciphertext C1.

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

[0066] Each second data provider may generate a second homomorphic ciphertext in the same manner as the first data provider. In one example, the second data provider O2 uses the homomorphic encryption function HE.Enc(PK, data2) to calculate the second homomorphic ciphertext C2. The second data provider O3 uses the homomorphic encryption function HE.Enc(PK, data3) to calculate the third homomorphic ciphertext C3.

[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 to determine a fusion statistical result.

[0068] Among them, the statistical operation logic corresponding to the data user may refer to the logic of homomorphic operation. The data provider and the data user may agree on the statistical operation logic in advance. The first homomorphic ciphertext and each second homomorphic ciphertext are substituted into the statistical operation logic for calculation to obtain a fused statistical result. In one example, the first homomorphic ciphertext and each second homomorphic ciphertext may be added to obtain a fused statistical result. For another example, the first homomorphic ciphertext and each second homomorphic ciphertext may be multiplied to obtain a fused statistical result.

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

[0070] The first attribute access control tree may refer to a tree structure that constrains the attributes of the access object. The attribute access control tree is the core structure used to define fine-grained access policies in the attribute-based encryption scheme (ABE). It constrains the attribute conditions that the access party must meet to decrypt the ciphertext through a tree logic expression.

[0071] In one example, the attribute encryption authorization function ABE.Pow(TY1, data1) may be used to calculate the first homomorphic authorization ciphertext PC1, where TY1 is a leaf node set of the attribute access control tree set by the data provider O1.

[0072] S106. Send the fused 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 fused statistical result, and obtain the plaintext of the statistical result based on the decryption calculation of the verified first homomorphic authorization ciphertext.

[0073] The fused statistical result represents verification information, and the first homomorphic authorization ciphertext represents ciphertext. The data user verifies whether the data user has passed the access control authority verification based on the fused statistical result and the first homomorphic authorization ciphertext, and if the data user has passed the access control authority 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 first homomorphic ciphertext and each of the second homomorphic ciphertexts are calculated according to the statistical operation logic corresponding to the data user to determine a fused statistical result, including: 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 fused statistical result.

[0075] The ciphertext operation result may be the result of operating the homomorphic ciphertext according to the operation method corresponding to homomorphic encryption. Homomorphic encryption may refer to a cryptographic technology that allows direct calculation on encrypted data. The calculation result is consistent with the plaintext calculation after decryption, achieving the effect of data being computable and invisible. The statistical operation logic may be the operation logic that ensures that the operation result of the ciphertext is the same as the operation result of the plaintext after decryption.

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

[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 the calculations to the data user. The calculation logic of the calculation function can be agreed upon in advance and can be specifically configured according to business needs. When calling the multi-party secure computing interface, each party can provide an identifier of the decryption task and data used for the decryption calculation. In an embodiment of the present invention, the data for decryption calculation provided by the first data provider is the first private key. The service corresponding to the multi-party secure computing interface performs decryption calculations 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 statistical operation logic on the homomorphic ciphertext, the ciphertext operation result is obtained, so that the data user can obtain the result after the operation of multi-source data, and the data user can obtain the fusion operation result of multi-source data without obtaining multi-source source data, thereby further improving the security of the source data. The ciphertext operation result is hashed to obtain the fusion statistical result, and the fusion statistical result parameter is given to the data user, thereby further improving the transmission security of the ciphertext operation result.

[0079] The technical solution of the embodiment of the present invention obtains a first homomorphic ciphertext generated by itself and a second homomorphic ciphertext provided by at least one second data provider, and generates a fusion statistical result to obtain the encrypted multi-source data statistics result, and generates a first homomorphic authorization ciphertext according to an attribute access control tree and the first data, and sends the fusion statistical result and the homomorphic authorization ciphertext to the data user, and the data user verifies and decrypts the homomorphic authorization ciphertext according to the fusion statistical result to further encrypt the homomorphic ciphertext, which can improve data security, and at the same time, can obtain homomorphic ciphertexts of multiple data sources, and realize encryption of the fusion result of multi-source data in the application scenario of multi-source data fusion, support multi-source data fusion scenarios, and at the same time increase the credibility of the statistical results of multi-source data.

[0080] Figure 2 A flowchart of a method for trusted transmission of statistical data provided by an embodiment of the present invention. The embodiment of the present invention is applicable to the case of fusion statistics of multi-source data in an encrypted scenario. The method can be executed by a trusted transmission device for statistical data, which can be implemented in the form of hardware and / or software. The trusted transmission device for statistical data can be configured in an electronic device of a data user that carries a trusted transmission function for statistical data, 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 may be made to the descriptions of other embodiments.

[0082] See also Figure 2 The statistical data trustworthy transmission methods shown include:

[0083] S201. Receive an authorization resolution key jointly determined by at least one attribute key service provider.

[0084] The attribute key service provider is used to generate a server for the authorization decryption key. The authorization resolution key is used to decrypt the homomorphic authorization ciphertext. When there is one attribute key service provider, the key generated by the attribute key service provider is determined as the authorization resolution key. When there are two attribute key service providers, the keys generated by each attribute key service provider are merged to obtain the authorization resolution key.

[0085] In one example, the number of attribute key service providers is at least two. The first attribute key service provider generates a first attribute key distribution master key mk1; the second attribute key service provider generates a 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 resolution key skp is generated. The authorization resolution key skp can be calculated using the attribute key generation function AKGen(mk1,mk2).

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

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

[0088] S203. Receive a 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 an 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 the fields of retail and e-commerce inventory management, education resource allocation, and industry network optimization. For example, in the field of education 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. For another example, in the field of retail and e-commerce inventory management, the first consumption data data1 is the consumption amount of 1,000,000, the second consumption data data2 is the consumption amount of 100,000, and the second consumption data data3 is the consumption amount of 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] In fact, 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 an 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 a second ciphertext 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:Statistician

[0101] ├──Purpose of visit: Internal statistics

[0102] └──Compliance status: Passed annual audit

[0103] Nodes in the property function access control tree: Department node: The user must belong to 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 to generate reports and other statistical needs. Access purpose node: The data purpose must be clearly defined as internal statistics, excluding 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 its 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 ciphertext to be verified according to statistical operation logic to determine a homomorphic statistical result.

[0105] 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, the homomorphic statistical result is verified for correctness.

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

[0109] S208. When the correctness check of the homomorphic statistical result passes, the homomorphic statistical result is decrypted to obtain a plain text of the statistical result.

[0110] In some embodiments, when the fused 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 fused statistical result does not correspond to the homomorphic statistical result, it is determined that the homomorphic statistical result is wrong 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 fused statistical result. Accordingly, 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. Failure of the correctness check indicates that the homomorphic statistical result is wrong. Specifically, the homomorphic statistical result does not correspond to the fused statistical result. Accordingly, 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 fused statistical result is the result obtained by calculating the homomorphic ciphertext using statistical operation logic, the correspondence between the fused statistical result and the homomorphic statistical result may mean that the fused statistical result is the same as the homomorphic statistical result, and the non-correspondence between the fused statistical result and the homomorphic statistical result may mean that the fused statistical result is different from the homomorphic statistical result.

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

[0113] The embodiment of the present invention obtains the authorization parsing key to parse the homomorphic authorization ciphertext to obtain the ciphertext to be verified, and uses the statistical operation logic to operate on the ciphertext to be verified to obtain the homomorphic statistical result. Based on the fused statistical result, the homomorphic statistical result is verified for correctness. When the correctness verification of the homomorphic statistical result passes, the data user implements attribute decryption at this time, and then decrypts the homomorphic statistical result to obtain the plaintext of the statistical result. The plaintext of the statistical result that has been attribute encrypted and homomorphically encrypted can be correctly obtained, thereby improving data security. The plaintext of the statistical result is directly obtained without obtaining the plaintext of the source data, thereby avoiding the leakage of the source data and improving the security of the source data. The plaintext of the statistical result is obtained by statistical operation of the source data provided by multiple data providers, thereby achieving the acquisition of the fusion 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, the homomorphic statistical result is decrypted to obtain a plaintext statistical result, including: calling a multi-party secure computing interface of a corresponding computing function of privacy computing according to the homomorphic statistical result to obtain a plaintext statistical result as feedback; the multi-party secure computing interface is used to receive a first private key sent by the first data provider and a second private key sent by each of the second data providers, and decrypting the homomorphic statistical result according to the first private key and each of the second private keys to obtain a plaintext statistical result.

[0115] Among them, the multi-party secure computing interface of the corresponding computing function of the privacy computing is called, and the homomorphic statistical results are passed to 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 key sent by each second data provider, and the homomorphic statistical results are parsed according to the first private key and each second private key to obtain the plaintext statistical results. The first data provider and each second data provider can both call the multi-party secure computing interface of the corresponding computing function of the privacy computing, the first data provider calls the multi-party secure computing interface to send the first private key, and each second data provider calls the multi-party secure computing interface to send each second private key.

[0116] In one 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 ciphertext. The service corresponding to the multi-party secure computing interface decrypts the homomorphic statistical result using the private keys provided by multiple parties that identify the same decryption task to obtain the statistical result in plain text.

[0117] It can be seen that through the multi-party secure computing interface, the homomorphic statistical results can be decrypted based on the first private key and each second private key, without sending the private key to the data user, and the homomorphic statistical results can be decrypted at the same time, which can improve the security of the key for decrypting the homomorphic statistical results.

[0118] In an optional embodiment, the correctness of the homomorphic statistical result is checked based on the fused statistical result, including: performing hash calculation on the homomorphic statistical result to obtain the statistical result to be checked; detecting whether the fused statistical result and the statistical result to be checked are consistent; when the fused statistical result and the statistical result to be checked are consistent, determining that the correctness check of the homomorphic statistical result has passed.

[0119] Among them, the fusion statistical result is obtained by calculating the homomorphic ciphertext according to the statistical operation logic and then performing a hash calculation. 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 to the same, the result obtained by the homomorphic statistical result after the hash calculation 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 by the homomorphic statistical result after the hash calculation should be inconsistent with the fusion statistical result. The calculation logic of the hash calculation can be agreed in advance.

[0120] In an example, the homomorphic statistical result is CF(data1, data2, data3)', and the homomorphic statistical result is hashed to obtain the statistical result to be verified H(CF(data1, data2, data3)'). The fused statistical result is HCC. The correctness verification function EQ(HCC, H(CF(data1, data2, data3)') is used to detect whether HCC and H(CF(data1, data2, data3)') are consistent. When they are consistent, it is determined that the correctness verification has passed. When they are inconsistent, it is determined that the correctness verification has failed.

[0121] As in the previous example, when the fusion statistics result HCC is equal to the homomorphic statistics result H(C(1000000+100000+20000000=21100000)), it is determined that the correctness check has passed.

[0122] It can be seen that by performing hash calculation on the homomorphic statistical results, the statistical results to be verified are obtained, and whether the statistical results to be verified are consistent with the fused statistical results is detected. According to the consistent detection results, the correctness verification results of the homomorphic statistical results are determined to improve the accuracy of the homomorphic statistical results. The statistical results can also be further hashed to improve the security of the statistical results.

[0123] In a specific example, the statistical data trusted transmission method may include:

[0124] First, key distribution stage:

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

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

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

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

[0129] 2. Reconstruct the group public key:

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

[0131] 3. Calculate key distribution parameters:

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

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

[0134] 4. Jointly calculate attribute keys

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

[0136] Phase 2: Homomorphic encryption phase:

[0137] 5. Calculate the homomorphic ciphertext of 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 fused statistical result HCC based on the homomorphic computing 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 3: 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 encryption authorization function ABE.Pow(TY1, data1), wherein 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 encryption authorization function ABE.Pow(TY2, data2), where 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 is data3, and the second homomorphic authorization ciphertext PC3 is calculated based on the attribute encryption authorization function ABE.Pow(TY3, data3), where TY3 is the leaf node set of the attribute access control tree set by the data provider O3.

[0149] Stage 4: Fusion computing stage:

[0150] 8. Parsing 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 authorization parsing function ABE.Dec(PC2,skp); when the attribute of the data user satisfies 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 authorization parsing function ABE.Dec(PC3,skp); when the attribute of the data user satisfies the attribute function access control tree of the second data provider O3, C3'==C3.

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

[0155] 9. Fusion of multi-key homomorphic ciphertext:

[0156] The data user U calculates the homomorphic statistical result CF(data1,data2,data3)' based on the homomorphic computing 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. Verify the correctness of the fusion ciphertext:

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

[0159] 11. Decrypt the fusion ciphertext:

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

[0161] Figure 3A schematic diagram of the structure of a statistical data trustworthy transmission device provided by an embodiment of the present invention. The embodiment of the present invention can be applied to the situation of fusion statistics of multi-source data in an encrypted scenario, the device can execute a statistical data trustworthy transmission method, the device can be implemented in the form of hardware and / or software, and the device can be configured in an electronic device of a first data provider that carries a statistical data trustworthy transmission function.

[0162] See also Figure 3 The statistical data shown are trusted transmission devices, including:

[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 the acquired second public key of at least one second data provider;

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

[0165] A second homomorphic ciphertext acquisition module 303, used to receive the second homomorphic ciphertext sent by each of 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 authorization ciphertext generation module 305 is used to generate a first homomorphic authorization ciphertext according to a first attribute access control tree corresponding to the first data provider and the first data;

[0168] The data sending module 306 is used to send the fused 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 fused statistical result, and obtain the plain text of the statistical result based on the decryption calculation of the verified first homomorphic authorization ciphertext.

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

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

[0171] The hash calculation unit is used to perform hash calculation on the ciphertext operation result to obtain a fusion statistical result.

[0172] The statistical data trusted transmission device provided in the embodiment of the present invention can execute the statistical data trusted transmission method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0173] Figure 4 A schematic diagram of the structure of a statistical data trust transmission device provided in an embodiment of the present invention. The embodiment of the present invention can be applied to the situation of fusion statistics of multi-source data in an encrypted scenario. The device can execute a statistical data trust transmission method. The device can be implemented in the form of hardware and / or software. The device can be configured in an electronic device of a data user that carries a statistical data trust transmission function.

[0174] See also Figure 4 The statistical data shown are trusted transmission devices, including:

[0175] The authorization key receiving module 401 is used to receive the authorization parsing key jointly determined by at least one attribute key service party;

[0176] A first ciphertext receiving module 402 is used to receive the fusion statistical result and the first homomorphic authorization ciphertext sent by the first data provider;

[0177] The second ciphertext receiving module 403 is used to receive a second homomorphic authorization ciphertext 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 is used to parse each of the second homomorphic authorization ciphertexts according to the authorization parsing key to obtain a second ciphertext to be verified corresponding to each of the second homomorphic authorization ciphertexts;

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

[0181] A statistical result verification module 407 is used to verify the correctness of the homomorphic statistical result according to the fusion statistical result;

[0182] The plaintext acquisition module 408 is used to decrypt the homomorphic statistical result to obtain the plaintext of the statistical result when the correctness check of the homomorphic statistical result passes.

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

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

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

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

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

[0188] The correctness checking unit is used to determine that the correctness check of the homomorphic statistical result has passed when the fused statistical result is consistent with the statistical result to be checked.

[0189] The statistical data trusted transmission device provided in the embodiment of the present invention can execute the statistical data trusted transmission method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0190] In the technical solutions of the embodiments of the present invention, the acquisition, storage and application of the data involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0191] Figure 5 A schematic diagram of the structure of an electronic device 500 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0192] like Figure 5As shown, the electronic device 500 includes at least one processor 501, and a memory connected to the at least one processor 501 in communication, such as a read-only memory (ROM) 502, a random access memory (RAM) 503, etc., wherein the memory stores a computer program that can be executed by at least one processor, and 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 to 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, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An 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 disk, an optical disk, 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 may be a variety of general and / or special 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 special 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 a statistical data trusted transmission method.

[0195] In some embodiments, the statistical data trusted transmission method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 508. In some embodiments, part or all of the computer program can be loaded and / or installed on 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 trusted transmission method described above can be performed. Alternatively, in other embodiments, the processor 501 can be configured to execute the statistical data trusted transmission method in any other appropriate manner (for example, by means of firmware).

[0196] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0197] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0198] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, 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 disk 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 may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

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

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

[0202] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0203] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in 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 acquired second public key of at least one second data provider; Generate a first homomorphic ciphertext according to the group public key and the acquired first data corresponding to the data user; Receiving the second homomorphic ciphertext sent by each of the second data providers; 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; Generate a first homomorphic authorization ciphertext according to a first attribute access control tree corresponding to the first data provider and the first data; The fused statistical result and the first homomorphic authorization ciphertext are sent to the data user, and according to the first private key corresponding to the first public key, the multi-party secure computing interface of the corresponding computing function of the privacy computing is called, so that the data user verifies the first homomorphic authorization ciphertext according to the fused statistical result, and obtains the plaintext of the statistical result based on the decryption calculation of the verified first homomorphic authorization ciphertext.

2. The method according to claim 1, characterized in that 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: Calculate 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; A hash calculation is performed 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, including: Receiving an authorization resolution key jointly determined by at least one attribute key service party; Receiving the fusion statistical result and the first homomorphic authorization ciphertext sent by the first data provider; Receiving a second homomorphic authorization ciphertext 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; Parse each of the second homomorphic authorization ciphertexts according to the authorization parsing key to obtain a second ciphertext to be verified corresponding to each of the second homomorphic authorization ciphertexts; According to the statistical operation logic, the first ciphertext to be verified and each of the second ciphertext to be verified are calculated to determine a homomorphic statistical result; According to the fusion statistical result, the correctness of the homomorphic statistical result is verified; When the correctness check of the homomorphic statistical result passes, the homomorphic statistical result is decrypted to obtain the plain text of the statistical result.

4. The method according to claim 3, characterized in that: The decrypting the homomorphic statistical result to obtain the statistical result plaintext includes: According to the homomorphic statistical result, the multi-party secure computing interface of the corresponding calculation function of the privacy computing is called to obtain the plaintext of the feedback 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 key 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.

5. The method according to claim 3, characterized in that: The correctness checking of the homomorphic statistical result according to the fusion statistical result includes: Performing hash calculation on the homomorphic statistical result to obtain the statistical result to be verified; Detecting whether the fused statistical result is consistent with the statistical result to be verified; When the fused statistical result is consistent with the statistical result to be verified, it is determined that the correctness verification of the homomorphic statistical result has passed.

6. A reliable transmission device for statistical data, characterized in that: The device is configured at a first data provider, and includes: 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 acquired second public key of at least one second data provider; A first homomorphic ciphertext acquisition module, used to generate a first homomorphic ciphertext according to the group public key and the first data corresponding to the acquired data user; A second homomorphic ciphertext acquisition module, used for receiving the second homomorphic ciphertext sent by each of the second data providers; A statistical calculation module, used 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; An authorization ciphertext generation module, used to generate a first homomorphic authorization ciphertext according to a first attribute access control tree corresponding to the first data provider and the first data; A data sending module is used to send the fused 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 fused statistical result, and obtain the plaintext of the statistical result based on the decryption calculation of the verified first homomorphic authorization ciphertext.

7. The device according to claim 6, characterized in that The statistical calculation module comprises: a homomorphic computing unit, configured to compute the first homomorphic ciphertext and each of the second homomorphic ciphertexts according to the statistical computing logic corresponding to the data user, to obtain a ciphertext computing result; The hash calculation unit is used to perform hash calculation on the ciphertext operation result to obtain a fusion statistical result.

8. A reliable transmission device for statistical data, characterized in that: The device is configured at a data user, and includes: An authorization key receiving module, used to receive an authorization parsing key jointly determined by at least one attribute key service party; A first ciphertext receiving module, used to receive the fusion statistical result and the first homomorphic authorization ciphertext sent by the first data provider; A second ciphertext receiving module, used to receive a second homomorphic authorization ciphertext sent by at least one second data provider; A first ciphertext parsing module, used to parse the first homomorphic authorization ciphertext according to the authorization parsing key to obtain a first ciphertext to be verified; A second ciphertext parsing module, used to parse each of the second homomorphic authorization ciphertexts according to the authorization parsing key, to obtain a second ciphertext to be verified corresponding to each of the second homomorphic authorization ciphertexts; A homomorphic statistical calculation module, used to calculate the first ciphertext to be verified and each of the second ciphertext to be verified according to statistical operation logic to determine a homomorphic statistical result; A statistical result verification module, used for verifying the correctness of the homomorphic statistical result according to the fused statistical result; The plaintext acquisition module is used to decrypt the homomorphic statistical result to obtain the plaintext of the statistical result when the correctness check of the homomorphic statistical result passes.

9. An electronic device, characterized in that: The electronic device comprises: 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 trusted transmission method described in any one of claims 1-5.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the statistical data trusted transmission method described in any one of claims 1-5 when executed.

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