Medical data auditing method and device

Abstract

The invention provides a medical data auditing method and device. The method comprises the following steps: acquiring an auditing instruction sent by terminal equipment; obtaining a hash structure sent by the cloud server, attributes of the to-be-audited medical data and a first coverage tree; and selecting at least one leaf node in the hash structure to form a second coverage tree by using a preset random number generation algorithm based on the first random number, decrypting the attribute of the to-be-audited medical data by using a pre-acquired public key, and obtaining the to-be-audited medical data based on the attribute of the to-be-audited medical data obtained after decryption. And verifying attributes of each leaf node in the first coverage tree and the second coverage tree, and if the attributes of the corresponding leaf nodes in the first coverage tree and the second coverage tree have a mapping relationship, determining that the verification of the to-be-verified medical data is passed. When the data is not tampered, a mapping relation exists between the first coverage tree and the second coverage tree, the first coverage number and the second coverage tree are separately generated by the cloud server and the auditing algorithm, and the problem of joint cheating between the auditing end and the cloud server is solved.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular to a medical data auditing method and device. Background Art

[0002] As a fusion of the Internet, the medical network has achieved the optimal allocation and efficient use of medical resources. Based on the medical network technology, a hospital stores medical data on the cloud platform, and other hospitals or patients can obtain the medical data through the cloud platform, which is conducive to the realization of cross-hospital medical treatment for patients. However, there is a risk of illegal tampering of data during the storage of data on the cloud platform.

[0003] In the related art, data possession proof is used to solve the above problems. However, data possession proof can only be applied to the scenario where the data is held by one party. When there are multiple data holders, data possession proof is not flexible and cannot meet the needs of practical applications. At the same time, there is a risk of third-party auditors in the data possession proof colluding with other entities to deceive. Summary of the invention

[0004] The present invention provides a medical data auditing method and device.

[0005] The first aspect of the present disclosure provides a medical data audit method, which is applied to an audit end. The method includes:

[0006] Acquire an audit instruction sent by a terminal device, wherein the audit instruction includes a label of the medical data to be audited and a first random number;

[0007] Obtaining a hash structure corresponding to the tag of the medical data to be audited, an attribute of the medical data to be audited, and a first covering tree sent by the cloud server, wherein the first covering tree is a covering tree containing the medical data to be audited, generated by the cloud server in response to the audit instruction according to the first random number and the hash structure, and the attribute of the medical data to be audited is data obtained by encrypting the storage information of the medical data to be audited by the terminal device based on a preset private key;

[0008] Using a preset random number generation algorithm, based on the first random number, select at least one leaf node in the hash structure to form a second covering tree;

[0009] Decrypt the attributes of the medical data to be audited using the pre-acquired public key, and verify the attributes of each leaf node in the first overlay tree and the second overlay tree based on the attributes of the medical data to be audited obtained after decryption. If there is a mapping relationship between the attributes of the corresponding leaf nodes in the first overlay tree and the second overlay tree, send verification pass information to the terminal device.

[0010] An embodiment of a second aspect of the present disclosure provides a medical data audit method, which is applied to a terminal device, and the method includes:

[0011] After sending the medical data to be audited and the attributes of the medical data to be audited to the cloud service end for storage, sending an audit instruction to the cloud service end and the audit end, the audit instruction including the label of the medical data to be audited and a first random number, so that the cloud service end generates a first covering tree and a hash structure according to the label of the medical data to be audited and the first random number, and feeds back the first covering tree, the hash structure and the attributes of the medical data to be audited to the audit end;

[0012] Acquire verification pass information of the audit end based on the audit instruction, the first overlay tree, the hash structure and the attribute feedback of the medical data to be audited.

[0013] The third aspect of the present disclosure provides a medical data audit method, which is applied to a cloud service end. The method includes:

[0014] Acquire the medical data to be audited and the attributes of the medical data to be audited sent by the terminal device, store the medical data to be audited and the attributes of the medical data to be audited, and then acquire the audit instruction sent by the terminal device, wherein the audit instruction includes a label of the medical data to be audited and a first random number;

[0015] Generate a hash structure based on the label of the medical data to be audited and the medical data to be audited;

[0016] Using a preset random number generation algorithm, based on the first random number, at least one leaf node is selected in the hash structure to generate a first covering tree;

[0017] The first overlay tree, the hash structure and the attributes of the medical data to be audited are sent to an audit end.

[0018] An embodiment of a fourth aspect of the present disclosure provides a medical data auditing device, which is applied to an auditing end, and the device includes:

[0019] A first instruction acquisition module, used to acquire an audit instruction sent by a terminal device, wherein the audit instruction includes a label of the medical data to be audited and a first random number;

[0020] A module for obtaining data to be verified, used for obtaining a hash structure corresponding to the tag of the medical data to be audited, an attribute of the medical data to be audited, and a first covering tree sent by the cloud server, wherein the first covering tree is a covering tree containing the medical data to be audited, generated by the cloud server in response to the audit instruction according to the first random number and the hash structure, and the attribute of the medical data to be audited is data obtained by encrypting the storage information of the medical data to be audited by the terminal device based on a preset private key;

[0021] A second covering tree generating module is used to select at least one leaf node in the hash structure based on the first random number by using a preset random number generating algorithm to form a second covering tree;

[0022] A verification module is used to decrypt the attributes of the medical data to be audited using a pre-acquired public key, and verify the attributes of each leaf node in the first overlay tree and the second overlay tree based on the attributes of the medical data to be audited obtained after decryption. If a mapping relationship exists between the attributes of the corresponding leaf nodes in the first overlay tree and the second overlay tree, a verification pass information is sent to the terminal device.

[0023] An embodiment of a fifth aspect of the present disclosure provides a medical data auditing device, which is applied to a terminal device, and the device includes:

[0024] A module for sending data to be audited, used for sending the medical data to be audited and the attributes of the medical data to be audited to a cloud server for storage, and then sending an audit instruction to the cloud server and the audit end, wherein the audit instruction includes a label of the medical data to be audited and a first random number, so that the cloud server generates a first covering tree and a hash structure according to the label of the medical data to be audited and the first random number, and feeds back the first covering tree, the hash structure and the attributes of the medical data to be audited to the audit end;

[0025] The verification information acquisition module is used to obtain the verification information feedback from the audit end based on the audit instruction, the first overlay tree, the hash structure and the attribute feedback of the medical data to be audited.

[0026] An embodiment of a sixth aspect of the present disclosure provides a medical data auditing device, which is applied to a cloud service end, and the device includes:

[0027] a second instruction acquisition module, configured to acquire the medical data to be audited and the attributes of the medical data to be audited sent by the terminal device, and after storing the medical data to be audited and the attributes of the medical data to be audited, acquire the audit instruction sent by the terminal device, wherein the audit instruction includes a label of the medical data to be audited and a first random number;

[0028] A hash structure generating module, used for generating a hash structure based on the label of the medical data to be audited and the medical data to be audited;

[0029] A first covering tree generating module is used to generate a first covering tree by selecting at least one leaf node in the hash structure based on the first random number using a preset random number generating algorithm;

[0030] A sending module is used to send the first covering tree, the hash structure and the attributes of the medical data to be audited to the audit end.

[0031] An embodiment of the seventh aspect of the present disclosure provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the methods described in the first, second and third aspects above.

[0032] An embodiment of an eighth aspect of the present disclosure provides a computer-readable storage medium having a computer program stored thereon, wherein the program is executed by a processor to implement the methods described in the first, second and third aspects above.

[0033] The technical solution provided in the embodiments of the present disclosure has at least the following technical effects or advantages:

[0034] The audit instruction sent by the terminal device is obtained, wherein the audit instruction includes the label of the medical data to be audited and the first random number; the hash structure corresponding to the label of the medical data to be audited, the attribute of the medical data to be audited and the first covering tree are obtained from the cloud service end; at least one leaf node is selected in the hash structure based on the first random number to form a second covering tree using a preset random number generation algorithm, wherein the first covering tree is generated by the cloud service end according to the first random number, thereby decrypting the attribute of the medical data to be audited using the pre-acquired public key, and verifying the attributes of each leaf node in the first covering tree and the second covering tree based on the attribute of the medical data to be audited obtained after decryption, if there is a mapping relationship between the attributes of the corresponding leaf nodes in the first covering tree and the second covering tree, then the medical data to be verified is verified, and a verification passing instruction is sent to the terminal device. If the data has not been tampered with, there is a mapping relationship between the first covering tree and the second covering tree, and since the first covering number and the second covering tree are generated separately by the cloud service end and the audit calculation, respectively, there is no problem of joint deception between the audit end and the cloud service end, thereby ensuring the accuracy of the verification result.

[0035] Additional aspects and advantages of the present disclosure will be given in part in the following description and in part will become apparent from the following description or be learned through practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] By reading the detailed description of the preferred embodiment below, various other advantages and benefits will become clear to those of ordinary skill in the art. The accompanying drawings are only used for the purpose of illustrating the preferred embodiment and are not considered to be limitations of the present disclosure. In addition, the same reference symbols are used to represent the same components throughout the accompanying drawings.

[0037] In the attached picture:

[0038] Figure 1 A schematic diagram of an application scenario of a medical data audit method provided by an embodiment of the present disclosure is shown;

[0039] Figure 2 A flowchart of an audit end execution of a medical data audit method provided by an embodiment of the present disclosure is shown;

[0040] Figure 3 A flowchart of a terminal device executing a medical data audit method provided by an embodiment of the present disclosure is shown;

[0041] Figure 4 A cloud service execution flow chart of a medical data audit method provided by an embodiment of the present disclosure is shown;

[0042] Figure 5 A schematic diagram of an audit terminal of a medical data audit device provided by an embodiment of the present disclosure is shown;

[0043] Figure 6 A schematic diagram showing a terminal device of a medical data auditing apparatus provided by an embodiment of the present disclosure;

[0044] Figure 7 A schematic diagram of a cloud service end of a medical data audit device provided by an embodiment of the present disclosure is shown;

[0045] Figure 8 A schematic diagram of the structure of an electronic device provided by an embodiment of the present disclosure is shown;

[0046] Fig. 9 A schematic diagram of a storage medium provided by an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0047] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0048] It should be noted that, unless otherwise specified, the technical terms or scientific terms used in the present disclosure should have the common meanings understood by those skilled in the art to which the present disclosure belongs.

[0049] The following describes the technical scenarios involved in the embodiments of the present disclosure.

[0050] With the rapid development of information technology and the growing demand for medical health, the medical network, as a product of the integration of the Internet and the medical health field, has realized the popularization of applications such as telemedicine and intelligent diagnosis, and promoted the optimal allocation and efficient use of medical resources. Cloud storage of medical data, an important link in the medical network, plays a key role in breaking the problem of "hospital data islands". It can promote information sharing and collaboration, and avoid problems such as repeated examinations and inconsistent diagnosis and treatment information during patients' medical treatment. However, the data stored in the cloud service end also brings challenges to the medical network. The data stored in the cloud server may be illegally tampered with, or the attack of external hackers may cause data loss. Even the cloud service provider's misoperation or storage system failure may cause incomplete data. The owner of the medical data cannot know whether the data is still safe and complete. In related technologies, this problem is mainly solved by data possession proof (DPB).

[0051] Data possession proof is a security protocol that is mainly used to verify whether the data stored in the cloud server is complete and correct. The verification process mainly involves the data owner, the cloud server, and the third-party audit end used for auditing. If the third-party audit end is used to deceive the cloud server and the data holder (doctors and patients, etc.), the audit results may be unreliable, especially in the case of medical disputes, which will seriously damage the interests of the data owner.

[0052] In view of this, the present disclosure provides a medical data audit method. In the present disclosure, the method can be applied to Figure 1 In the medical data audit system shown, the medical data audit system includes a terminal device 101, a cloud server 102 and an audit terminal 103, wherein the terminal device is used by the data holder to send the data to be audited to the cloud server. Specifically, doctors or patients can send the data they hold to the cloud server for storage by logging into their accounts or other means. When a doctor or patient needs to audit the data stored in the cloud server, an audit instruction is sent to the cloud server and the audit terminal. After receiving the audit instruction, the cloud server sends the stored data to be audited to the audit terminal, which performs an audit based on the acquired data and finally feeds back the audit structure to the terminal device.

[0053] The following describes the medical data auditing method proposed according to the embodiment of the present disclosure with reference to examples.

[0054] Figure 2A medical data audit method provided by an embodiment of the present disclosure is shown. The medical data audit method is applied to an audit end. The medical data audit method of the embodiment of the present disclosure may include the following steps:

[0055] In step S11, the audit instruction sent by the terminal device is obtained.

[0056] The audit instruction includes a label of the medical data to be audited and a first random number.

[0057] In step S12, a hash structure corresponding to the label of the medical data to be audited, the attributes of the medical data to be audited, and the first covering tree sent by the cloud service end are obtained.

[0058] Among them, the first covering tree is a covering tree containing medical data to be audited, generated by the cloud server in response to the audit instruction according to the first random number and hash structure, and the attribute of the medical data to be audited is the data encrypted by the terminal device based on the preset private key on the storage information of the medical data to be audited.

[0059] Exemplarily, after obtaining the audit instruction sent by the terminal device, the data to be audited corresponding to the audit instruction and sent by the cloud server is obtained, wherein the data to be audited is represented in the form of a hash structure. To further verify the data to be audited, it is also necessary to obtain the attributes of the data to be audited sent by the cloud server, as well as a first covering tree generated by the cloud server based on a first random number, wherein the first covering tree is generated by the cloud server alone, thereby avoiding the problem of joint deception with the audit terminal.

[0060] In step S13, a preset random number generation algorithm is used to select at least one leaf node in the hash structure based on the first random number to form a second covering tree.

[0061] Exemplarily, the audit end generates a second random number based on the first random number in the audit instruction, determines at least one leaf node corresponding to the second random number based on the second random number and the preset mapping relationship, and constructs the second cover tree with the leaf node as the leaf node of the second cover tree. Specifically, in some embodiments, the above step S13 can be implemented in the following manner: iterate using a preset random number generation algorithm until the number of iterations reaches the first random number, determine the second random number generated by the preset random number generation algorithm at the current number of iterations; based on the second random number, select at least one leaf node in the hash structure to form a second cover tree.

[0062] Exemplarily, the random number generation algorithm is preset, and in the embodiment of the present disclosure, the second random number may be generated by using a strange attractor. The first random number is used as the number of iterations of the chaotic system of the strange attractor, and when the number of iterations of the chaotic system reaches the first random number, the corresponding second random number is output. Specifically, the following is a specific equation of the four-dimensional chaotic system:

[0063]

[0064] in are the four state variables of the chaotic system, δ j (1≤j≤8) is the system parameter energy.

[0065] Because of the high sensitivity to the first random number and the unpredictability of the iterative results, chaos theory has become a powerful tool for generating high-quality random numbers and enhancing security. Even if the generation algorithm is known, it is difficult to reverse the original information or predict the subsequent second random number due to the inherent complexity of the system and its extreme sensitivity to initial conditions, which greatly improves the level of information security.

[0066] After generating the second random number, the leaf node corresponding to the second random number is determined according to the preset mapping relationship, where the leaf node is the leaf node in the hash structure sent by the cloud server. The position and number of the leaf nodes determined by the second random number in the hash structure are related to the preset mapping relationship, and because the second random number is difficult to predict, the subsequent determination of the leaf nodes based on the second random number is also difficult to predict. Auditing the leaf nodes determined by the second random number does not require auditing all the nodes in the hash structure, which reduces the amount of audit calculations and improves the audit speed.

[0067] Among them, the specific process of generating the second covering tree can be: based on the mapping relationship between the second random number and the audit block, determine at least one audit block corresponding to the second random number; determine the node corresponding to the at least one audit block in the hash structure; take the node corresponding to the at least one audit block as a leaf node to generate the second covering tree.

[0068] Exemplarily, the leaf nodes in the hash structure are data blocks corresponding to the medical files to be audited, that is, when stored on the cloud server, the hash structure is composed of data blocks of the stored medical data to be audited. Auditing the medical data to be audited is to audit whether the corresponding data blocks have been tampered with. Therefore, based on the mapping relationship between the second random number and the audit block, at least one audit block corresponding to the second random number is determined, and each audit block is used as a leaf node to generate a second cover tree.

[0069] In step S14, the attributes of the medical data to be audited are decrypted using the pre-acquired public key. Based on the attributes of the medical data to be audited obtained after decryption, the attributes of each leaf node in the first overlay tree and the second overlay tree are verified. If there is a mapping relationship between the attributes of the corresponding leaf nodes in the first overlay tree and the second overlay tree, verification information is sent to the terminal device.

[0070] Next, the leaf nodes determined by the second random number form a new hash structure, that is, the second cover tree. The second cover tree is used to further verify the first cover tree. If the verification is successful, it means that the medical data to be audited has not been tampered with.

[0071] For example, when the second covering tree is used to verify the first covering tree, it can be verified whether the number of leaf nodes of the covering number and the attributes of the audit blocks constituting each leaf node are the same. If the attributes of the audit blocks constituting each leaf node are the same, it means that the verification of the medical data to be audited has passed.

[0072] Specifically, in some embodiments, step S14 can be implemented in the following manner: parsing each leaf node in the first covering tree; if the number of leaf nodes in the first covering tree is the same as the number of leaf nodes in the second covering tree, then using each leaf node in the second covering tree as a leaf node of the first covering data to generate a verification covering tree; if there is a mapping relationship between the attributes of the leaves of the verification covering tree and the attributes of the medical data to be audited obtained after decryption, the verification of the medical data to be verified is passed.

[0073] Exemplarily, on the basis that the structure of the first cover tree and the second cover tree are the same (the same number of leaf nodes), the leaf nodes in the second cover tree are respectively used as the leaf nodes of the first cover tree to generate a verification cover tree. If the data has not been tampered with, the information contained in each node of the verification cover tree is the same as that of the first cover tree, or there is a mapping relationship between them.

[0074] Specifically, the audit end determines whether the tree structures of the first cover tree T' returned by the cloud service end and the second cover tree T calculated by itself are the same. If they are different, FALSE is directly returned and the audit end is terminated.

[0075] Extract {H2(d i )|s1≤i≤s c}After saving, calculate {h(H2(d i ))|s1≤i≤s c} replace the corresponding value. If all leaf nodes are complete, the root node R can be calculated. Then, the value of the root node R is judged to be correct according to the following test formula. If the verification fails, it means that the returned {H2(d i )|s1≤i≤s c}If the data is incorrect or other data block information outside the challenge verification is incorrect, FALSE is returned directly.

[0076] e(sig R , g) = e(H2(R), g α )

[0077] Continue to judge whether the following test formula is established, where {H(mi )|s1≤i≤s c} has been obtained in the previous section. If the same is true, it means that the medical data stored on the cloud server is correct and passes the integrity audit, and returns TRUE, otherwise it outputs FALSE.

[0078]

[0079] The attributes of the medical data to be audited are generated by encrypting various storage information with a preset private key. In addition, whether it is the hash structure sent by the cloud server, the first overlay tree, or the leaf node in the second overlay tree generated by the audit end, after parsing, it also includes various storage information. The storage information in the attribute is compared with the storage information after parsing the leaf node. If there is a mapping relationship, it means that it has not been tampered with.

[0080] Among them, in the disclosed embodiment, the attributes of the medical data to be audited obtained from the cloud service end include various storage information of the medical data to be audited, wherein the storage information includes the information of the data blocks corresponding to each leaf node in the hash structure after the terminal device sends the medical data to be audited to the cloud service end. Therefore, verification can be performed based on the attributes of the medical data to be audited.

[0081] The verification of the attributes of the leaf nodes in the overlay tree includes at least one of the following: the attributes include verifying the second preset element of the medical data to be audited to which the leaf nodes in the overlay tree belong, the label of the medical data to be audited, the number of leaf nodes of the medical data to be audited, and the timestamp; the verification of the mapping relationship between the attributes of the leaf nodes in the overlay tree and the attributes of the medical data to be audited includes at least one of the following:

[0082] Verify whether the medical document label corresponding to the leaf node in the overlay tree matches the label of the medical data to be audited;

[0083] If the number of leaf nodes in the verification overlay tree corresponding to the medical files to which they belong is the same as the number of leaf nodes in the medical data to be audited;

[0084] If the timestamp corresponding to the leaf node in the verification overlay tree is the same as the timestamp of the medical data to be audited;

[0085] If it is verified whether there is a bilinear mapping relationship between the second preset element corresponding to the leaf node in the coverage tree and the first preset element in the medical data to be audited.

[0086] Specifically, the first preset element is the storage information added by the terminal device before sending the medical data to be audited to the cloud server. If there is a bilinear mapping relationship between the first preset element and the first preset element in the leaf node in the verification coverage tree, it means that the verification is successful.

[0087] At the same time, it is also possible to verify whether the number of leaf nodes (total number of data blocks), labels, and timestamps in the hash structure corresponding to the attributes of each leaf node in the overlay tree correspond.

[0088] In the embodiment of the present disclosure, the first preset element and the second preset element are related properties of the existence of bilinear mapping. Let q be a large prime number, Hehe are two groups of prime order q. Let is an additive group, let is the multiplication group. Function e: It is called a bilinear map. Good properties can make cryptographic schemes simpler and more efficient. It has the following properties:

[0089] Bilinear: For any and Satisfying e(Pa,Qb)=e(P s Q) ab . Or for any There are e(P·Q, R)=e(P, R)·e(Q, R) and e(P, Q·R)=e(P, Q)·e(P, R).

[0090] Non-degeneracy: If P is The generator of , then e(P, P) is That is, e(P, P)≠1.

[0091] Computability: Given P, Q ∈ G1, there exists an efficient algorithm to compute the bilinear pairing e(P, Q).

[0092] Corresponding to the above-mentioned medical data audit method applied to the audit end, the embodiment of the present application also discloses a medical audit method applied to a terminal device, such as Figure 3 As shown, the medical data auditing method of the embodiment of the present disclosure may include the following steps:

[0093] In step S21, after the medical data to be audited and the attributes of the medical data to be audited are sent to the cloud server for storage, an audit instruction is sent to the cloud server and the audit end, and the audit instruction includes a label of the medical data to be audited and a first random number, so that the cloud server generates a first covering tree and a hash structure according to the label of the medical data to be audited and the first random number, and feeds back the first covering tree, hash structure and the attributes of the medical data to be audited to the audit end.

[0094] Exemplarily, the terminal device is used by relevant personnel to organize and send medical data to the cloud server for storage. The relevant personnel may include doctors, nurses, and patients. Before the medical data is organized and sent to the cloud server for storage, a key may also be assigned to the relevant personnel to log in to the terminal device for identity authentication.

[0095] Specifically, the relevant keys can be generated by a dedicated key management center. For example, the security parameter 1 k Input the key management center to generate the relevant keys for medical staff. In addition, the key management center can be a separate server or embedded in the terminal device.

[0096] The following is the specific process of generating user keys: Let the bilinear mapping function Among them, G and It is a multiplicative cyclic group with a generator of g and a large prime order of p. * Let H1, H2 be hash functions {0, 1} mapped to the G domain * →G, f(·) is mapped to Z p One-way hash function for domain: {0,1} * →Z p , h(·) is a hash function based on a hash algorithm. Select a random number d∈Z p Calculate the public key v=g as the master private key α , α is used as the master key, and the corresponding key s is generated according to the unique ID number UIN of the relevant personnel i =H1(UIN i ) a After receiving the key, the relevant personnel will conduct the following verification:

[0097] e(s i , g)=e(H1(UIN i ), v)

[0098] If the equation holds true, it means the key is received successfully and True is returned to the key management center; otherwise, False is returned to the key management center and the other party is asked to resend the key.

[0099] Before the medical data to be audited is obtained and needs to be sent to the cloud server for storage, it is necessary to process the medical data to be audited. Specifically, based on the medical data to be audited and preset classification standards, the medical data to be audited is divided into at least one medical sub-data; each medical sub-data is divided into at least one data block; at least one data block corresponding to any medical sub-data is used as a leaf node of the hash tree of the corresponding medical sub-data to generate a hash tree of the corresponding medical sub-data; the hash tree corresponding to at least one medical sub-data and the attributes of the medical data to be audited are sent to the cloud server for storage.

[0100] For example, the original medical data is first classified into medical sub-data Ti based on the sensitivity of the data field, such as personal attribute data, health status data, medical application data, medical payment data, health resource data and public health data. Thereafter, a number m∈Z is randomly generated.p (used to generate a data block for every several fields), and divide the medical sub-data Ti into blocks:

[0101]

[0102] where |·| represents a medical sub-data T i , sgn(·) represents the sign function, ij represents the jth data block of the ith medical sub-data, so the total number of blocks n = ∑ i，j＝1 d ij And (i∈[1,6],j∈[1,n]).

[0103] It should be noted that when the user is a doctor or related medical personnel, after generating the data blocks of each medical sub-data, an element u can be randomly selected in G, combined with the file identifier FN, the total number of file data blocks n and the current timestamp t, and then the additional private key sk is used to sign the triplet to obtain the file label ft = FN||n||u||t||sig sk (FN||n||u||t). Using the pre-generated private key s i and element u for each data block d ij Signature Constructing a homomorphic signature set φ={σ ij |1≤i≤6,1≤j≤n}.

[0104] According to multiple T i Construct multiple trees with data block hash values ​​{h(H2(d j ))|1≤j≤n} is the MHT forest of leaf nodes, and the root node set R of the tree is obtained i , and calculate the total root node RT = H2 (R1 || ... || R i ) Sign the root node to get Finally, the first tuple {T i ,ft,φ,sig R} is sent to the cloud server, which can be used to identify the data stored in the cloud server to prevent tampering of the cloud server data. The second tuple {F, ft, φ, RT, FN} is sent to the patient. The patient can obtain the corresponding medical data to be audited according to the second tuple analysis, without the need to divide the medical data again, thereby reducing unnecessary calculations.

[0105] In addition, the attributes of the medical data need to be sent to the cloud server. After receiving the audit instruction, the cloud server sends the attributes of the medical data to be audited to the audit end, and then the audit end further verifies whether the data has been tampered with based on the attributes of the medical data to be audited.

[0106] Specifically, in order to ensure the security of the attributes of the medical data to be audited, the storage information of the medical data to be audited that constitutes the attributes is encrypted using a preset private key to generate the attributes, wherein the preset private key is generated using a bilinear mapping rule, and the public key matching the preset private key and the private key conform to a bilinear mapping, thereby improving the security of the attributes.

[0107] In step S22, the verification information of the audit end based on the audit instruction, the first overlay tree, the hash structure and the attribute feedback of the medical data to be audited is obtained. After the audit end performs the audit according to the first overlay tree, the hash structure and the medical data to be audited, the terminal device receives the verification information sent by the audit end.

[0108] Corresponding to the above-mentioned medical data auditing method applied to the audit end and the terminal device, the embodiment of the present application also discloses a medical auditing method applied to the cloud service end, such as Figure 4 As shown, the medical data auditing method of the embodiment of the present disclosure may include the following steps:

[0109] In step S31, the medical data to be audited and the attributes of the medical data to be audited sent by the terminal device are obtained, and after the medical data to be audited and the attributes of the medical data to be audited are stored, the audit instruction sent by the terminal device is obtained.

[0110] The audit instruction includes a label of the medical data to be audited and a first random number.

[0111] Exemplarily, the medical data to be audited and the attributes of the medical data to be audited sent by the terminal device are used to be stored in the cloud server. Afterwards, an audit instruction is received, wherein the tag in the audit instruction corresponds to the medical data to be audited, and the cloud server searches for the medical data to be audited corresponding to the tag.

[0112] In step S32, a hash structure is generated based on the label of the medical data to be audited and the medical data to be audited.

[0113] Exemplarily, after the medical data to be audited is determined, the medical data to be audited is stored in the form of a hash tree on the cloud server, so a corresponding hash structure can be generated according to the hash tree of the medical data to be audited.

[0114] In step S33, a preset random number generation algorithm is used to select at least one leaf node in the hash structure based on the first random number to generate a first covering tree.

[0115] Exemplarily, similar to the audit end, a second random number is generated using a random number generation algorithm, and at least one leaf node is selected in the hash structure according to the second random number to generate a first covering tree.

[0116] Specifically, a preset random number generation algorithm is used to iterate until the number of iterations reaches a first random number, and a second random number generated by the preset random number generation algorithm at the current number of iterations is determined; based on a mapping relationship between the second random number and a data block corresponding to a leaf node in a hash tree, at least one audit block corresponding to the second random number is determined; and a first covering tree is generated using at least one audit block as a leaf node.

[0117] For example, the preset random number generation algorithm can also use the chaotic system of strange attractors to generate the second random number. It should be noted that the preset random number generation algorithm of the server and the preset random number generation algorithm of the audit end are consistent, so as to ensure that the second random numbers generated by the cloud service end and the audit end are the same. Then the leaf nodes determined according to the second random number correspond to each other on the audit end and the cloud service end.

[0118] In step S34, the first covering tree, the hash structure and the attributes of the medical data to be audited are sent to the audit end.

[0119] Corresponding to the implementation of the above medical data audit method, the present disclosure also provides a medical data audit device, which is applied to the audit end and is used to perform the above Figure 2 The medical data audit method of any embodiment is shown. Figure 5 As shown, the medical data auditing device includes:

[0120] A first instruction acquisition module 501, used to acquire an audit instruction sent by a terminal device, wherein the audit instruction includes a label of the medical data to be audited and a first random number;

[0121] The module 502 for obtaining data to be verified is used to obtain the hash structure corresponding to the tag of the medical data to be audited, the attribute of the medical data to be audited, and the first covering tree sent by the cloud server, wherein the first covering tree is a covering tree containing the medical data to be audited generated by the cloud server in response to the audit instruction according to the first random number and the hash structure, and the attribute of the medical data to be audited is the data obtained by encrypting the storage information of the medical data to be audited by the terminal device based on a preset private key;

[0122] A second covering tree generating module 503 is used to select at least one leaf node in the hash structure based on the first random number by using a preset random number generating algorithm to form a second covering tree;

[0123] The verification module 504 is used to decrypt the attributes of the medical data to be audited using the pre-acquired public key, and verify the attributes of each leaf node in the first overlay tree and the second overlay tree based on the attributes of the medical data to be audited obtained after decryption. If there is a mapping relationship between the attributes of the corresponding leaf nodes in the first overlay tree and the second overlay tree, a verification pass information is sent to the terminal device.

[0124] Optionally, the second covering tree generating module 503 is specifically used for:

[0125] Iterate using a preset random number generation algorithm until the number of iterations reaches a first random number, and determine a second random number generated by the preset random number generation algorithm at a current number of iterations;

[0126] Based on the second random number, at least one leaf node is selected in the hash structure to form a second covering tree.

[0127] Optionally, the second covering tree generating module 503 is further configured to:

[0128] Determine at least one audit block corresponding to the second random number based on a mapping relationship between the second random number and the audit block;

[0129] determining, in the hash structure, a node corresponding to the at least one audit block;

[0130] The nodes corresponding to the at least one audit block are taken as leaf nodes to generate a second covering tree.

[0131] Optionally, the verification module 504 is specifically configured to:

[0132] Parsing each leaf node in the first overlay tree;

[0133] If the number of leaf nodes in the first cover tree is the same as the number of leaf nodes in the second cover tree, taking the leaf nodes in the second cover tree as the leaf nodes of the first cover data to generate a verification cover tree;

[0134] If there is a mapping relationship between the attributes of the leaves of the verification coverage tree and the attributes of the medical data to be audited obtained after decryption, the verification of the medical data to be verified is successful.

[0135] Optionally, the attributes of the leaf nodes in the verification coverage tree include at least one of the following: the attributes include a second preset element of the medical data to be audited to which the leaf nodes in the verification coverage tree belong, a label of the medical data to be audited, the number of leaf nodes of the medical data to be audited, and a timestamp; the verification module 504 includes a module for implementing at least one of the following:

[0136] Verify whether the medical file label corresponding to the leaf node in the verification overlay tree matches the label of the medical data to be audited;

[0137] If the number of leaf nodes in the verification overlay tree corresponding to the medical files to which the leaf nodes belong is the same as the number of leaf nodes in the medical data to be audited;

[0138] If the timestamp corresponding to the leaf node in the verification overlay tree is the same as the timestamp of the medical data to be audited;

[0139] If the second preset element corresponding to the leaf node in the verification coverage tree has a bilinear mapping relationship with the first preset element in the medical data to be audited.

[0140] Corresponding to the implementation of the above medical data audit method, the present disclosure also provides a medical data audit device, which is applied to a terminal device and is used to perform the above Figure 3 The medical data audit method of any embodiment is shown. Figure 6 As shown, the medical data auditing device includes:

[0141] The module 601 for sending data to be audited is used to send the medical data to be audited and the attributes of the medical data to be audited to the cloud server for storage, and then send an audit instruction to the cloud server and the audit end, wherein the audit instruction includes a label of the medical data to be audited and a first random number, so that the cloud server generates a first covering tree and a hash structure according to the label of the medical data to be audited and the first random number, and feeds back the first covering tree, the hash structure and the attributes of the medical data to be audited to the audit end;

[0142] The verification information acquisition module 602 is used to obtain verification information feedback from the audit end based on the audit instruction, the first overlay tree, the hash structure and the attribute feedback of the medical data to be audited.

[0143] Optionally, the module 601 for sending data to be audited is specifically used for:

[0144] Based on the medical data to be audited and a preset classification standard, the medical data to be audited is divided into at least one medical sub-data;

[0145] Dividing each medical sub-data into at least one data block;

[0146] Using at least one data block corresponding to any medical sub-data as a leaf node of a hash tree of the corresponding medical sub-data, generating a hash tree of the corresponding medical sub-data;

[0147] The hash tree corresponding to the at least one medical sub-data and the attributes of the medical data to be audited are sent to the cloud service end for storage.

[0148] Optionally, the attributes of the medical data to be audited are obtained by encrypting at least one of the following storage information using a preset private key, and the storage information includes at least one of the following: a first preset element, a label of the medical data to be audited, the number of leaf nodes in the hash tree corresponding to the at least one medical sub-data, and a timestamp.

[0149] Corresponding to the implementation of the above medical data audit method, the present disclosure also provides a medical data audit device, which is applied to the cloud service end and is used to perform the above Figure 4 The medical data audit method of any embodiment is shown. Figure 7 As shown, the medical data auditing device includes:

[0150] The second instruction acquisition module 701 is used to acquire the medical data to be audited and the attributes of the medical data to be audited sent by the terminal device, and after storing the medical data to be audited and the attributes of the medical data to be audited, acquire the audit instruction sent by the terminal device, wherein the audit instruction includes a label of the medical data to be audited and a first random number;

[0151] A hash structure generating module 702 is used to generate a hash structure based on the label of the medical data to be audited and the medical data to be audited;

[0152] A first covering tree generating module 703 is used to generate a first covering tree by selecting at least one leaf node in the hash structure based on the first random number using a preset random number generating algorithm;

[0153] The sending module 704 is used to send the first covering tree, the hash structure and the attributes of the medical data to be audited to the audit end.

[0154] Optionally, the first covering tree generating module 703 is specifically used for:

[0155] Iterate using a preset random number generation algorithm until the number of iterations reaches a first random number, and determine a second random number generated by the preset random number generation algorithm at a current number of iterations;

[0156] Determine at least one audit block corresponding to the second random number based on a mapping relationship between the second random number and the data blocks corresponding to the leaf nodes in the hash tree;

[0157] The at least one audit block is used as a leaf node to generate a first covering tree.

[0158] The medical data auditing device provided by the above-mentioned embodiment of the present disclosure and the medical data auditing method provided by the embodiment of the present disclosure are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0159] The disclosed embodiment also provides an electronic device for executing the above-mentioned medical data audit method. Figure 8 , which shows a schematic diagram of an electronic device provided by some embodiments of the present disclosure. Figure 8 As shown, the electronic device includes: a processor 500, a memory 501, a bus 502 and a communication interface 503, wherein the processor 500, the communication interface 503 and the memory 501 are connected via the bus 502; the memory 501 stores a computer program that can be run on the processor 500, and the processor 500 executes the aforementioned Figure 2-Figure 4 A medical data auditing method is provided by any of the illustrated embodiments.

[0160] The memory 501 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 503 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0161] The bus 502 may be an ISA bus, a PCI bus, or an EISA bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 501 is used to store programs. After receiving the execution instruction, the processor 500 executes the program. Figure 2-Figure 4 Any of the illustrated embodiments discloses that the medical data auditing method may be applied to the processor 500 or implemented by the processor 500 .

[0162] The processor 500 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit in the processor 500 or the instruction in the form of software. The above processor 500 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present disclosure can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in conjunction with the embodiments of the present disclosure can be directly embodied as a hardware decoding processor to be executed, or the hardware and software modules in the decoding processor can be executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 501, and the processor 500 reads the information in the memory 501 and completes the steps of the above method in combination with its hardware.

[0163] The electronic device provided by the embodiment of the present disclosure and the medical data audit method provided by the embodiment of the present disclosure are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by them.

[0164] The present disclosure also provides a computer-readable storage medium corresponding to the medical data audit method provided in the above embodiment. Fig. 9 The computer-readable storage medium shown is a CD 30 on which a computer program (ie, a program product) is stored. When the computer program is run by a processor, the medical data audit method provided by any of the aforementioned embodiments will be executed.

[0165] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical or magnetic storage media, which are not listed here one by one.

[0166] The computer-readable storage medium provided by the above-mentioned embodiments of the present disclosure and the medical data audit method provided by the embodiments of the present disclosure are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0167] It should be noted that:

[0168] In the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present disclosure can be practiced without these specific details. In some instances, well-known structures and technologies are not shown in detail so as not to obscure the understanding of this description.

[0169] Similarly, it should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various inventive aspects, in the above description of the exemplary embodiments of the present disclosure, the various features of the present disclosure are sometimes grouped together into a single embodiment, figure, or description thereof. However, the disclosed method should not be interpreted as reflecting the following schematic diagram: the claimed disclosure requires more features than the features explicitly recited in each claim. More specifically, as reflected in the claims below, the inventive aspects lie in less than all the features of the single embodiment disclosed above. Therefore, the claims that follow the specific embodiment are hereby expressly incorporated into the specific embodiment, with each claim itself serving as a separate embodiment of the present disclosure.

[0170] In addition, those skilled in the art will appreciate that, although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of this disclosure and form different embodiments. For example, in the claims below, any one of the claimed embodiments may be used in any combination.

[0171] The above is only a preferred specific implementation of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with the technical field within the technical scope disclosed in the present disclosure should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A medical data audit method, characterized in that: Applied to the audit end, the method includes: Acquire an audit instruction sent by a terminal device, wherein the audit instruction includes a label of the medical data to be audited and a first random number; Obtaining a hash structure corresponding to the tag of the medical data to be audited, an attribute of the medical data to be audited, and a first covering tree sent by the cloud server, wherein the first covering tree is a covering tree containing the medical data to be audited, generated by the cloud server in response to the audit instruction according to the first random number and the hash structure, and the attribute of the medical data to be audited is data obtained by encrypting the storage information of the medical data to be audited by the terminal device based on a preset private key; Using a preset random number generation algorithm, based on the first random number, select at least one leaf node in the hash structure to form a second covering tree; Decrypt the attributes of the medical data to be audited using the pre-acquired public key, and verify the attributes of each leaf node in the first overlay tree and the second overlay tree based on the attributes of the medical data to be audited obtained after decryption. If there is a mapping relationship between the attributes of the corresponding leaf nodes in the first overlay tree and the second overlay tree, send verification pass information to the terminal device.

2. The method according to claim 1, characterized in that The method of using a preset random number generation algorithm and selecting at least one leaf node in the hash structure based on the first random number to form a second covering tree includes: Iterate using a preset random number generation algorithm until the number of iterations reaches a first random number, and determine a second random number generated by the preset random number generation algorithm at a current number of iterations; Based on the second random number, at least one leaf node is selected in the hash structure to form a second covering tree.

3. The method according to claim 2, characterized in that The selecting at least one leaf node in the hash structure based on the second random number to form a second covering tree includes: Determine at least one audit block corresponding to the second random number based on a mapping relationship between the second random number and the audit block; determining, in the hash structure, a node corresponding to the at least one audit block; The nodes corresponding to the at least one audit block are taken as leaf nodes to generate a second covering tree.

4. The method according to claim 3, characterized in that The method of verifying the attributes of each leaf node in the first overlay tree and the second overlay tree based on the attributes of the medical data to be audited obtained after decryption, and if there is a mapping relationship between the attributes of the corresponding leaf nodes in the first overlay tree and the second overlay tree, then the medical data to be verified is verified, including: Parsing each leaf node in the first overlay tree; If the number of leaf nodes in the first cover tree is the same as the number of leaf nodes in the second cover tree, taking the leaf nodes in the second cover tree as the leaf nodes of the first cover data to generate a verification cover tree; If there is a mapping relationship between the attributes of the leaves of the verification coverage tree and the attributes of the medical data to be audited obtained after decryption, the verification of the medical data to be verified is successful.

5. The method according to claim 4, characterized in that The attributes of the leaf nodes in the verification overlay tree include at least one of the following: the attributes include a second preset element of the medical data to be audited to which the leaf nodes in the verification overlay tree belong, a label of the medical data to be audited, the number of leaf nodes of the medical data to be audited, and a timestamp; there is a mapping relationship between the attributes of the leaf nodes in the verification overlay tree and the attributes of the medical data to be audited, including at least one of the following: Verify whether the medical file label corresponding to the leaf node in the verification overlay tree matches the label of the medical data to be audited; If the number of leaf nodes in the verification overlay tree corresponding to the medical files to which the leaf nodes belong is the same as the number of leaf nodes in the medical data to be audited; If the timestamp corresponding to the leaf node in the verification overlay tree is the same as the timestamp of the medical data to be audited; If the second preset element corresponding to the leaf node in the verification coverage tree has a bilinear mapping relationship with the first preset element in the medical data to be audited.

6. A medical data audit method, characterized in that: Applied to a terminal device, the method comprises: After sending the medical data to be audited and the attributes of the medical data to be audited to the cloud server for storage, sending an audit instruction to the cloud server and the audit end, the audit instruction including the label of the medical data to be audited and a first random number, so that the cloud server generates a first covering tree and a hash structure according to the label of the medical data to be audited and the first random number, and feeds back the first covering tree, the hash structure and the attributes of the medical data to be audited to the audit end; Acquire verification pass information of the audit end based on the audit instruction, the first overlay tree, the hash structure and the attribute feedback of the medical data to be audited.

7. The method according to claim 6, characterized in that The sending of the medical data to be audited and the attributes of the medical data to be audited to the cloud service end for storage includes: Based on the medical data to be audited and a preset classification standard, the medical data to be audited is divided into at least one medical sub-data; Dividing each medical sub-data into at least one data block; Using at least one data block corresponding to any medical sub-data as a leaf node of a hash tree of the corresponding medical sub-data, generating a hash tree of the corresponding medical sub-data; The hash tree corresponding to the at least one medical sub-data and the attributes of the medical data to be audited are sent to the cloud service end for storage.

8. The method according to claim 6, characterized in that: The attribute of the medical data to be audited is obtained by encrypting at least one of the following storage information through a preset private key, and the storage information includes at least one of the following: a first preset element, a label of the medical data to be audited, the number of leaf nodes in the hash tree corresponding to the at least one medical sub-data, and a timestamp.

9. A medical data audit method, characterized in that: Applied to a cloud server, the method includes: Acquire the medical data to be audited and the attributes of the medical data to be audited sent by the terminal device, store the medical data to be audited and the attributes of the medical data to be audited, and then acquire the audit instruction sent by the terminal device, wherein the audit instruction includes a label of the medical data to be audited and a first random number; Generate a hash structure based on the label of the medical data to be audited and the medical data to be audited; Using a preset random number generation algorithm, based on the first random number, at least one leaf node is selected in the hash structure to generate a first covering tree; The first overlay tree, the hash structure and the attributes of the medical data to be audited are sent to an audit end.

10. The method according to claim 9, characterized in that The method of using a preset random number generation algorithm and selecting at least one leaf node in a hash structure corresponding to the label of the medical data to be audited based on the first random number to generate a first covering tree includes: Iterate using a preset random number generation algorithm until the number of iterations reaches a first random number, and determine a second random number generated by the preset random number generation algorithm at a current number of iterations; Determine at least one audit block corresponding to the second random number based on a mapping relationship between the second random number and the data blocks corresponding to the leaf nodes in the hash tree; The at least one audit block is used as a leaf node to generate a first covering tree.

11. A medical data auditing device, characterized in that: Applied to the audit end, the device includes: A first instruction acquisition module, used to acquire an audit instruction sent by a terminal device, wherein the audit instruction includes a label of the medical data to be audited and a first random number; A module for obtaining data to be verified, used for obtaining a hash structure corresponding to the tag of the medical data to be audited, an attribute of the medical data to be audited, and a first covering tree sent by the cloud server, wherein the first covering tree is a covering tree containing the medical data to be audited, generated by the cloud server in response to the audit instruction according to the first random number and the hash structure, and the attribute of the medical data to be audited is data obtained by encrypting the storage information of the medical data to be audited by the terminal device based on a preset private key; A second covering tree generating module is used to select at least one leaf node in the hash structure based on the first random number by using a preset random number generating algorithm to form a second covering tree; A verification module is used to decrypt the attributes of the medical data to be audited using a pre-acquired public key, and verify the attributes of each leaf node in the first overlay tree and the second overlay tree based on the attributes of the medical data to be audited obtained after decryption. If a mapping relationship exists between the attributes of the corresponding leaf nodes in the first overlay tree and the second overlay tree, a verification pass information is sent to the terminal device.

12. A medical data auditing device, characterized in that: Applied to a terminal device, the device comprises: A module for sending data to be audited, used for sending the medical data to be audited and the attributes of the medical data to be audited to a cloud server for storage, and then sending an audit instruction to the cloud server and the audit end, wherein the audit instruction includes a label of the medical data to be audited and a first random number, so that the cloud server generates a first covering tree and a hash structure according to the label of the medical data to be audited and the first random number, and feeds back the first covering tree, the hash structure and the attributes of the medical data to be audited to the audit end; The verification information acquisition module is used to obtain the verification information feedback from the audit end based on the audit instruction, the first overlay tree, the hash structure and the attribute feedback of the medical data to be audited.

13. A medical data auditing device, characterized in that: Applied to a cloud server, the device comprises: a second instruction acquisition module, configured to acquire the medical data to be audited and the attributes of the medical data to be audited sent by the terminal device, and after storing the medical data to be audited and the attributes of the medical data to be audited, acquire the audit instruction sent by the terminal device, wherein the audit instruction includes a label of the medical data to be audited and a first random number; A hash structure generating module, used for generating a hash structure based on the label of the medical data to be audited and the medical data to be audited; A first covering tree generating module is used to generate a first covering tree by selecting at least one leaf node in the hash structure based on the first random number using a preset random number generating algorithm; A sending module is used to send the first covering tree, the hash structure and the attributes of the medical data to be audited to the audit end.

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