A medical data infringement detection method and system based on image mapping

Abstract

The application discloses a kind of medical data infringement detection method and system based on image mapping, belong to data security and privacy protection field, trusted execution environment is based on the normal range of the detection index to the medical data of reporter and the medical data of the reported person are handled, obtain the data image of reporter and the data image of the reported person;Trusted execution environment carries out feature extraction to the data image of reporter and the data image of the reported person, obtain the feature set of both, similarity calculation is carried out based on the feature set of both, obtain similarity score and send to trusted third party;Trusted third party according to the comparison of the similarity score and set threshold, judge whether there is infringement between the medical data of the reported person and the medical data of reporter or not.It can extract more robust, more can represent the characteristics of different numerical data to cope with the challenge of data being modified, when similarity comparison is carried out, with high accuracy.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of data security and privacy protection, and particularly relates to a medical data infringement detection method and system based on image mapping. BACKGROUND

[0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute the prior art.

[0003] In today's digital age, the medical field is rapidly developing, generating a large amount of medical data with potential value. More and more users are willing to sell their own medical data for economic benefits or to support medical research. In order to ensure the security and ownership of data transactions, users use blockchain technology for transactions. Blockchain technology, through its decentralized and tamper-proof characteristics, can effectively prevent data tampering and forgery during transactions. Each transaction record is stored in an encrypted form on the blockchain, ensuring the transparency and credibility of the transaction.

[0004] However, in the process of data transactions, due to the high similarity of physiological indicators of medical data among different individuals, traditional numerical data similarity detection methods (such as based on statistical features, time dynamic regularization, etc.) are difficult to effectively distinguish these data, thereby increasing the risk of data infringement. In addition, when the data is partially modified or some unimportant information is deleted, the conventional detection means may not be able to accurately identify the true similarity of the data, providing an opportunity for hidden data infringement. These problems lead to frequent unauthorized use and privacy leakage in data transactions. SUMMARY

[0005] To overcome the shortcomings of the prior art, the present application provides a medical data infringement detection method and system based on image mapping, which maps medical numerical data into images according to whether it is in the normal range in the SGX trusted execution environment, and performs infringement detection through image similarity judgment.

[0006] To achieve the above purpose, one or more embodiments of the present application provide the following technical solutions:

[0007] In a first aspect, the present application provides a medical data infringement detection method based on image mapping, comprising:

[0008] A trusted third party obtains the medical data and decryption key ciphertext of the reporter and the reported person and signs, obtains the normal range of each detection index in the medical data of the reporter, and sends the signed medical data and decryption key ciphertext of the reporter and the reported person and the normal range of the detection index to a trusted execution environment;

[0009] The trusted execution environment verifies the received data, and after successful verification, decrypts the medical data of the reporter and the reported person based on the ciphertext, to obtain the medical data of the reporter and the medical data of the reported person.

[0010] The trusted execution environment processes the medical data of the reporter and the medical data of the reported person based on the normal range of the detection index, to obtain a data image of the reporter and a data image of the reported person.

[0011] The trusted execution environment extracts features from the data image of the reporter and the data image of the reported person, to obtain feature sets of the two, and performs similarity calculation based on the feature sets of the two, to obtain a similarity score and send it to a trusted third party.

[0012] The trusted third party determines whether there is an infringement between the medical data of the reported person and the medical data of the reporter according to a comparison of the similarity score with a set threshold.

[0013] Further, the verification of the received data by the trusted execution environment includes verifying the reliability of the data source based on the public key of the trusted third party, and verifying whether the data content is tampered with based on the hash value of the data.

[0014] Further, the processing of the medical data of the reporter and the medical data of the reported person by the trusted execution environment based on the normal range of the detection index specifically includes:

[0015] sequentially determining whether the specific value of each detection index in the medical data of the reporter and the medical data of the reported person is in the normal range of the detection index;

[0016] If the detection value of a certain detection index is in the normal range, it is mapped to a certain color system;

[0017] If the detection value of a certain detection index is out of the normal range, it is mapped to another contrast color system;

[0018] Through the above mapping method, the medical data of the reporter and the medical data of the reported person are converted into a data image of the reporter and a data image of the reported person reflecting the data features.

[0019] Further, the feature extraction of the data image of the reporter and the data image of the reported person by the trusted execution environment, to obtain the feature sets of the two, specifically includes: using a scale-invariant feature transformation algorithm to extract features from the data image of the reporter and the data image of the reported person, to generate a robust feature set of the reporter data and a robust feature set of the reported person data.

[0020] Further, the similarity calculation based on the feature sets of the two specifically includes:

[0021] The robust feature set of the reporter data and the feature points in the robust feature set of the reported data are matched one by one through a brute force search method, and the best matching between all the feature points is found.

[0022] The average value of the matching distance is calculated through the distance between all the matching points, and then converted into a similarity score.

[0023] Further, the trusted third party judges whether there is an infringement between the medical data of the reported person and the medical data of the reporter according to the comparison of the similarity score and the set threshold, specifically including:

[0024] When the similarity score is higher than the set threshold, there is an infringement, and the trusted third party finds the registered user through the smart contract, judges the true owner of the data according to the registered timestamp, and punishes the infringer;

[0025] When the similarity score is lower than the set threshold, there is no infringement.

[0026] Further, the infringement detection result is stored in the blockchain through the smart contract.

[0027] In a second aspect, the present application provides a medical data infringement detection system based on image mapping, comprising:

[0028] An infringement reporting module configured to: the trusted third party obtains the medical data and decryption key ciphertext of the reporter and the reported person and signs, obtains the normal range of each detection index in the medical data of the reporter, and sends the signed medical data and decryption key ciphertext of the reporter and the reported person and the normal range of the detection index to the trusted execution environment;

[0029] A data verification module configured to: the trusted execution environment verifies the received data, and after successful verification, decrypts the medical data of the reporter and the reported person based on the ciphertext to obtain the medical data of the reporter and the reported person.

[0030] A data processing module configured to: the trusted execution environment processes the medical data of the reporter and the reported person based on the normal range of the detection index to obtain the data image of the reporter and the data image of the reported person;

[0031] A similarity calculation module configured to: the trusted execution environment extracts features from the data image of the reporter and the data image of the reported person to obtain the feature sets of the two, calculates the similarity based on the feature sets of the two, obtains the similarity score and sends it to the trusted third party;

[0032] An infringement judgment module configured to judge, by the trusted third party, whether there is infringement between the medical data of the reported person and the medical data of the reporter according to comparison of the similarity score with a set threshold.

[0033] In a third aspect, the present application provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the medical data infringement detection method based on image mapping according to the first aspect.

[0034] In a fourth aspect, the present application provides a computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the medical data infringement detection method based on image mapping according to the first aspect when executing the program.

[0035] The above one or more technical solutions have the following beneficial effects:

[0036] The present application maps the data into different specified color systems according to whether each medical numerical data is in the range of normal value in the SGX trusted execution environment, and in the same color system, the numerical difference is intuitively reflected through the change of color, so as to generate an image reflecting the characteristics of the data, and whether there is infringement is judged through similarity detection of the robust features of the image. Compared with the way of directly extracting features from numerical data, more robust and representative features of different numerical data can be extracted to cope with the challenge of modified data; compared with the way of directly mapping data into images, the accuracy is higher when performing similarity comparison, and the similarity between two data can be more accurately judged. It is suitable for the scene of medical data transaction and infringement judgment, can realize the authentication and supervision of the identity of the data owner in the transaction process, and provide efficient infringement detection of transaction data. While ensuring that the user has control over the data, the present application effectively prevents data buyers and hospitals from selling data of data owners privately, ensures the security and privacy of transaction data, ensures the legality of data sources, and prevents data from being misused.

[0037] The present application constructs a blockchain transaction platform, links the examination data and the ciphertext data by generating ciphertext data with identity authentication when the patient visits a doctor and signing the ciphertext data and the examination data, avoids that the hospital with a large amount of medical data privately sells data by impersonating the patient, verifies the correctness of the identity authentication data and the key of the patient in the form of plaintext in the trusted execution environment by using SGX, does not disclose any related information of the plaintext, and solves the problem that the data owner cannot be identified, the key information is encrypted by SGX and stored by the trusted third party, which prevents a curious third party from checking the key of the user and further checking the data of the user, ensures the privacy of the data, and solves the problem of limited memory in SGX. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0039] Figure 1 is a flow chart of a medical data infringement detection method based on image mapping according to an embodiment of the present invention;

[0040] Figure 2 is an interactive diagram of a medical data infringement detection method based on image mapping according to an embodiment of the present invention;

[0041] Figure 3 This is a schematic diagram of a blockchain transaction platform according to an embodiment of the present invention. DETAILED DESCRIPTION

[0042] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0043] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0044] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0045] Explanation of terms:

[0046] SGX, or software guard extensions, is an extension of the Intel instruction set architecture that provides an encrypted trusted execution area in memory. It uses hardware security as a mandatory guarantee and does not rely on the security status of firmware and software. The CPU protects data and privacy from malicious theft.

[0047] IPFS, short for interplanetary file system, is a global, peer-to-peer distributed file transfer system. Its goal is to complement the current Internet's Hypertext Transfer Protocol (HTTP) and connect all computing devices with the same file system. It works by replacing domain-based addresses with content-based addresses. This means users search for content stored somewhere, not just an address. Verifying the sender's identity requires only the hash of the content, making data transfer faster, more secure, and more robust.

[0048] Example 1

[0049] like Figure 1 As shown, this embodiment discloses a medical data infringement detection method based on image mapping, which includes the following steps:

[0050] S1: The trusted third party obtains and signs the medical data and decryption key ciphertext of the reporter and the reported person, obtains the normal range of each test indicator in the reporter's medical data, and sends the signed medical data and decryption key ciphertext of the reporter and the reported person, as well as the normal range of the test indicator, to the trusted execution environment;

[0051] In this embodiment, the medical data infringement detection method based on image mapping is based on the use of blockchain technology for data transactions, and realizes the publication of data descriptions, data ownership registration and data transactions based on blockchain; through on-chain and off-chain collaboration, the user's identity and decryption key are verified in the trusted execution environment SGX, and credentials are generated and key ciphertext is stored with the help of a third party; infringement detection is performed in the trusted execution environment SGX.

[0052] Ciphertext refers to the ciphertext stored by a trusted third party. After the trusted execution environment encrypts information such as the decoding key, it will be sent to the trusted third party for storage, that is, the ciphertext.

[0053] The data owner registers the feature in the blockchain based on the hash value hash (FE) and the data description ID DS Initially determine whether your data has been infringed. If so, provide your and the person being reported’s address and data identifier (Add i , ID DSi ) is sent to a trusted third party, where i=I represents the information of the reporter, i=H represents the information of the person being reported, Add i Refers to the ID of the data owner, and this definition also applies to the following content.

[0054] The trusted third party Download data from IPFS and get Refers to the two medical data ciphertexts of the reporter and the reported person, which are encrypted by the hospital using different symmetric keys K h encryption; Refers to the address where the reporter and the reported person store ciphertext data; use their own private key PK HD right ID DSi 、Add i 、 Signature, Generate and will The data are sent together to SGX for infringement detection, where TA is the normal range of each detection indicator in the whistleblower's medical data.

[0055] S2: The trusted execution environment verifies the received data. After successful verification, the medical data of the reporter and the reported person are decrypted based on the ciphertext to obtain the medical data of the reporter and the medical data of the reported person.

[0056] In this embodiment, the trusted execution environment verifies the received data by: verifying whether the data source is reliable based on the public key of the trusted third party, and verifying whether the data content has been tampered with based on the hash value of the data.

[0057] SGX first uses the public key PK of a trusted third party HD Verify sign HD The correctness of the data to ensure the reliability of the source; use your own private key SK SGX Decryption get calculate Hash value And judge with Are they equal? ​​If they are equal, it indicates that the data content has not been tampered with and the ciphertext is indeed the ciphertext of the decryption key.

[0058] S3: The trusted execution environment processes the medical data of the reporter and the reported person based on the normal range of the detection indicator to obtain the data image of the reporter and the data image of the reported person;

[0059] In this embodiment, the medical data of the reporter and the reported person are processed, specifically including:

[0060] Determine in sequence whether the specific values ​​of each test indicator in the medical data of the reporter and the reported person are within the normal range of the test indicator;

[0061] If the detection value of a certain detection indicator is within the normal range, it will be mapped to a specific color system;

[0062] If the detection value of a certain detection index exceeds the normal range, it is mapped to another contrast color system;

[0063] Through the above mapping mode, the medical data of the reporter and the medical data of the reported person are converted into the data image of the reporter and the data image of the reported person reflecting the data characteristics.

[0064] Specifically, the SGX uses decryption get The SGX judges according to the specific value of each detection index in Data i whether it is within the normal range defined by TA, and classifies the data in this way; if a detection value is within the normal range, it is mapped to a specific color system, and if the detection value exceeds the normal range, it is mapped to another contrast color system; this process is performed on all detection values in Data i ; to further reflect the difference of the data, each detection item corresponds to an independent color system, and different item data is mapped into different color groups, and even in the same color system, the subtle changes of the specific detection value will be reflected through the color brightness. Through this mapping mode, the SGX converts the data in Data i into an image image i reflecting the data characteristics.

[0065] S4: The trusted execution environment extracts features from the data image of the reporter and the data image of the reported person, obtains the feature sets of the two, performs similarity calculation based on the feature sets of the two, obtains a similarity score and sends it to a trusted third party;

[0066] In this embodiment, the feature sets of the two are obtained, specifically including: using a scale-invariant feature transform algorithm (SIFT) to extract features from the data image of the reporter and the data image of the reported person, generating a robust feature set of the reporter data and a robust feature set of the reported data.

[0067] The similarity calculation based on the feature sets of the two specifically includes: matching the feature points in the robust feature set of the reporter data and the robust feature set of the reported data one by one through a brute force search method, and finding the best match between all feature points; through the distance between all matching points, the average value of the matching distance is calculated, and then converted into a similarity score.

[0068] Specifically, the SGX extracts features from image I and image HThe two images are subjected to feature extraction using the SIFT algorithm to identify the key points in each image and their corresponding local features, generating a robust feature set FE that can represent the core structure and information of the image. i , and FE i Generate hash value hash(FE i ). FE is searched by brute force method. I and FE H The feature points in the two feature sets are matched one by one, and the best match between all feature points in the two images is found; the average value of the matching distance is calculated through the distance between these matching points (best matches), and then converted into a similarity score Simil; SGX converts (ID DSI , ID DSH ,Simil,hash FEI , hash FEH ,) is returned to the trusted third party.

[0069] S5: The trusted third party determines whether there is any infringement between the medical data of the person being reported and the medical data of the reporter based on a comparison of the similarity score with a set threshold.

[0070] In this embodiment, the trusted third party determines whether there is any infringement between the medical data of the person being reported and the medical data of the reporter based on the comparison of the similarity score with the set threshold. Specifically, when the similarity score is higher than the set threshold, there is infringement, and the trusted third party finds the registered user through the smart contract, determines the true owner of the data based on the registration timestamp, and punishes the infringer; when the similarity score is lower than the set threshold, there is no infringement.

[0071] Specifically, the trusted third party judges the similarity of the data based on whether Simil is lower than the set threshold, thereby determining whether there is infringement between the two data; when it is higher than the set threshold, there is infringement, and the trusted third party registers the data ownership registry according to the smart contract. Find a registered user, or Add i Find the registered user and determine the real owner of the data based on the registration timestamp, and add the infringer H Finally, through the smart contract Stored in the blockchain; if there is no infringement, The information will be stored in the blockchain, and part of the whistleblower’s deposit will be deducted as the detection fee.

[0072] In this embodiment, medical data is traded on a blockchain trading platform. The construction of the blockchain trading platform is as follows:

[0073] 1. Hospital

[0074] 1. Hospital identity authentication: The hospital applies for qualification review from a trusted third party (such as a national or local health department); after the trusted third party successfully reviews the hospital, it creates a hospital identity account for the hospital in the blockchain and obtains the hospital address Add h ; Add h , review status and other information are uploaded to the blockchain; the hospital generates its own public key PK h and private key SK h , by calling the smart contract to Add h and PK h The above realizes off-chain qualification review and on-chain identity registration.

[0075] 2. Deployment algorithm: Deploy symmetric encryption and signature algorithms in hospital inspection equipment to generate secret information that only the patient (data owner) knows and signs, and the hospital cannot modify it. D Add the hospital address h Stored in the blockchain, the smart contract deploys the two algorithms into the trusted execution environment SGX.

[0076] 2. Data Owner (Patient)

[0077] 1. Generate the patient's identity authentication ciphertext: When the patient undergoes the examination, he / she enters the encryption key PK p and plaintext data M for identity authentication; check device encryption M and generate ciphertext data for identity authentication The device uses its own private key SK D To D p Signature, Generate

[0078] 2. Obtaining data: When the data owner (patient) requests his or her medical data, the hospital will HD , sign infor , C HK , S D , D p ,PK,S h} is sent to the data owner. HD The symmetric key K h Encrypt the ciphertext of medical data Data, C HK The public key PK of the data owner is used u Encrypted data details infor and K h The ciphertext, sign inforis the result of the ring signature of infor, PK is the set of all hospital public keys in the ring signature, S h The hospital uses SK h Signature of relevant content. After the data owner verifies the correctness of the transmitted content, C HD Store in IPFS and return the storage address Add D 。Add D This is the IPFS storage address for the encrypted data. After receiving the encrypted data from the hospital, the data owner will verify it. If verification is successful, the encrypted data will be stored. This address is the storage address for the encrypted data.

[0079] 3. Release data description: The data owner releases the data description in the smart contract and obtains the data description identifier ID DS , which means publishing the data to be sold in a smart contract and depositing a certain amount of security into the smart contract. This security deposit is used to deduct a portion of the security deposit from the reporter (the data owner) as compensation for the infringement detection process if no infringement is determined.

[0080] The data owner, trusted third party and Enclave in SGX perform remote authentication and establish a secure channel.

[0081] 4. Apply for verification information: The data owner uses the SGX public key PK SGX Encrypted TX, TX = {K h , C HD ,infor,PK,sign infor , ID DS , S D , PK p , M, D p},get And calculate its hash hash (C tra ). Through the secure channel {C tra ,hash(C tra )} is sent to SGX for key verification and credential generation.

[0082] 5. Verify key and identity authentication ciphertext: SGX computes C tra The hash value hash(C tra ′), and hash(C tra ) comparison to verify the integrity of the data; use PK to verify the signature sign infor The correctness of the data source is determined by the reliability of the data source; use your own private key SK SGX Decryption C tra , using the obtained K hThe decrypted C HD , and the relevant parameters and Data' are obtained; hash values hash(Data') and hash(C HD ) of Data' and C HD are calculated, respectively, to determine whether they are the same as the plaintext hash value and the ciphertext hash value in infor, respectively, to verify the integrity of the data, the correctness of the decryption key, and the relevance of Data and infor; the SGX uses the stored public key PK D of the device to successfully verify the correctness of the signature S D , and uses the identity authentication key PK p sent by the data owner to decrypt D p , to obtain hash values hash(M) and hash(M') are calculated and compared to determine whether they are equal, to verify the identity of the data owner.

[0083] 6. Generating credentials and storing keys: after successful verification, the SGX generates credentials cred for the data owner, including Add o , ID DS , and PK; the SGX uses its own private key SK SGX to sign cred, to generate sign SGXP and cred are returned to the data owner; the SGX uses its own private key SK SGX to encrypt K h , hash(C HD ), ID DS , and Add o , to obtain Enc SGX = Enc SGX (K h ||hash(C HD )||ID DS ||Add o ), and to calculate its hash value hash(Enc SGX ); {Enc SGX , ID DS , hash(Enc SGX )} are sent to the trusted third party through a secure channel, and are stored by the trusted third party. Enc SGX represents ciphertext, which is the result of the SGX encrypting data with its own private key, and can be referred to as ciphertext of the decryption key information.

[0084] 7. Registering data ownership: the data owner sends {Add D , hash(C HD ), PK, sign SGXP , cred, hash(FE), Addo , ID DS} to register data ownership, where FE is the feature of medical numerical data (the specific generation process is described in S4); the smart contract verifies sign SGXP to confirm the identity of the user and the storage of the key; check whether the PK' in cred is consistent with PK, and whether the public key in PK corresponds to the registered hospital; after verification, register the data for the data owner.

[0085] III. Data buyer

[0086] 1. Transaction data: when the data buyer purchases data from the data owner through the smart contract, the data owner sends {r o , C bh , cred, Add D , ID DS , hash(C bh ), sign SGXP} to the smart contract, where C bh is the ciphertext encrypted with the public key PK b of the data buyer K h , r o is the signature of C bh with the private key SK o of the data owner, and ID DS is the corresponding data description; the smart contract verifies sign SGXP and Add o and ID DS are consistent with the corresponding contents in cred, and then transacts with the data buyer.

[0087] Compared with the prior art without using blockchain, in the present application, the data description publishing on the chain, data ownership registration, hospital qualification checking, data transaction, data ownership tracing and transaction record storage are realized based on blockchain, the key verification and storage, user identity verification and data infringement detection are realized by using the on-chain and off-chain collaborative mode, the infringement result is stored on the chain, the whole process is open and transparent, the infringement judgment can be correctly made, and the data owner's rights and interests are protected and data abuse is prevented. Compared with the prior art using blockchain, in the present application, the medical numerical data is mapped into an image according to whether it is in a normal range in the SGX trusted execution environment, and the infringement detection is performed through the similarity of the image.

[0088] The application discloses a medical data infringement detection method and system based on image mapping, supports the identity authentication of a user off-chain, verifies and stores a decryption key, realizes the publication of data description, data ownership registration and data transaction based on a block chain, verifies the identity of the user and the decryption key in an SGX trusted execution environment through an on-chain and off-chain collaborative mode, generates a credential and stores a key ciphertext with the aid of a third party, maps medical numerical data into an image according to whether the medical numerical data is in a normal range in the SGX trusted execution environment, and performs infringement detection through the similarity judgment of the image. The application is suitable for the scene of medical data transaction and infringement judgment, can realize the authentication and supervision of the identity of a data seller in a transaction process, and provides efficient transaction data infringement detection. The scheme ensures that the user has control over the data, effectively prevents the data buyer and the hospital from privately selling patient data, guarantees the security and privacy of the transaction data, ensures that the data source is legal, and prevents the data from being misused.

[0089] Embodiment two

[0090] The embodiment provides a medical data infringement detection system based on image mapping, which comprises:

[0091] An infringement reporting module is configured to enable a trusted third party to acquire the medical data and decryption key ciphertext of a reporter and a reported person and sign, acquire the normal range of each detection index in the medical data of the reporter, and send the signed medical data and decryption key ciphertext of the reporter and the reported person and the normal range of the detection index to a trusted execution environment.

[0092] A data verification module is configured to enable the trusted execution environment to verify the received data, decrypt the medical data of the reporter and the reported person based on the ciphertext after successful verification, and obtain the medical data of the reporter and the medical data of the reported person.

[0093] A data processing module is configured to enable the trusted execution environment to process the medical data of the reporter and the medical data of the reported person based on the normal range of the detection index, and obtain the data image of the reporter and the data image of the reported person.

[0094] A similarity calculation module is configured to enable the trusted execution environment to extract features from the data image of the reporter and the data image of the reported person, obtain the feature sets of the two, perform similarity calculation based on the feature sets of the two, obtain a similarity score, and send the similarity score to the trusted third party.

[0095] An infringement judgment module is configured to enable the trusted third party to judge whether there is an infringement between the medical data of the reported person and the medical data of the reporter according to the comparison between the similarity score and a set threshold.

[0096] Embodiment three

[0097] The object of the present embodiment is to provide a computing device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method of the embodiment when executing the program.

[0098] Embodiment three

[0099] The object of the present embodiment is to provide a computer readable storage medium, a computer readable storage medium having stored thereon a computer program, the program being executable by a processor to perform the steps of the method of the embodiment.

[0100] The steps involved in the devices of embodiments three and four above correspond to the method of embodiment one, and the detailed description can be found in the relevant description section of embodiment one. The term "computer readable storage medium" should be understood to include a single medium or multiple media that can store a set of instructions that can be executed by a processor and cause the processor to perform any of the methods of the present invention.

[0101] It should be apparent to one skilled in the art that the modules or steps of the present invention described above can be implemented using a general purpose computer device, alternatively, they can be implemented using program code executable by a computing device, so that they can be stored in a storage device and executed by a computing device, or they can be made into individual integrated circuit modules, or a plurality of modules or steps can be made into a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.

[0102] The above description is merely the preferred embodiments of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0103] The above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, but is not a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications or variations made by those skilled in the art on the basis of the technical solutions of the present invention without inventive labor are still within the protection scope of the present invention.

Claims

1. A medical data infringement detection method based on image mapping, characterized in that: include: The trusted third party obtains and signs the medical data and decryption key ciphertext of the reporter and the reported person, obtains the normal range of each test indicator in the reporter's medical data, and sends the signed medical data and decryption key ciphertext of the reporter and the reported person, as well as the normal range of the test indicator, to the trusted execution environment; The trusted execution environment verifies the received data, and upon successful verification, decrypts the medical data of the reporter and the reported person based on the ciphertext to obtain the medical data of the reporter and the reported person; The trusted execution environment processes the medical data of the reporter and the reported person based on the normal range of the detection indicators, specifically by: sequentially determining whether the specific values ​​of each detection indicator in the medical data of the reporter and the reported person are within the normal range of the detection indicator; if the detection value of a detection indicator is within the normal range, mapping it to a specific color system; if the detection value of a detection indicator is outside the normal range, mapping it to another contrasting color system; through the above mapping method, the medical data of the reporter and the reported person are converted into data images of the reporter and the reported person that reflect the data characteristics; The trusted execution environment extracts features from the data image of the reporter and the data image of the person being reported to obtain feature sets of the two, calculates similarity based on the feature sets of the two, obtains a similarity score, and sends it to a trusted third party; The trusted third party determines whether there is any infringement between the medical data of the person being reported and the medical data of the reporter based on a comparison of the similarity score with a set threshold.

2. The medical data infringement detection method based on image mapping according to claim 1, characterized in that: The trusted execution environment verifies the received data by: verifying whether the data source is reliable based on the public key of the trusted third party, and verifying whether the data content has been tampered with based on the hash value of the data.

3. The medical data infringement detection method based on image mapping according to claim 1, characterized in that: The trusted execution environment performs feature extraction on the data image of the reporter and the data image of the reported person to obtain feature sets of the two, specifically including: using a scale-invariant feature transformation algorithm to extract features from the data image of the reporter and the data image of the reported person to generate a robust feature set of the reporter's data and a robust feature set of the reported person's data.

4. The medical data infringement detection method based on image mapping as claimed in claim 3, characterized in that: The similarity calculation is performed based on the feature sets of the two, specifically including: The robust feature set of the informant's data and the robust feature set of the reported person's data are matched one by one by a brute force search method, and the best match between all feature points is found; The distances between all matching points are used to calculate the average of the matching distances, which is then converted into a similarity score.

5. The medical data infringement detection method based on image mapping according to claim 1, characterized in that: The trusted third party determines whether there is any infringement between the medical data of the person being reported and the medical data of the reporter based on the comparison of the similarity score with the set threshold, including: When the similarity score is higher than the set threshold, there is infringement. The trusted third party finds the registered user through the smart contract, determines the true owner of the data based on the registration timestamp, and punishes the infringer. When the similarity score is below the set threshold, there is no infringement.

6. The medical data infringement detection method based on image mapping according to claim 5, characterized in that: The infringement detection results are stored in the blockchain through smart contracts.

7. A medical data infringement detection system based on image mapping, characterized in that: include: The infringement reporting module is configured to: a trusted third party obtains and signs the medical data and decryption key ciphertext of the reporter and the reported person, obtains the normal range of each test indicator in the reporter's medical data, and sends the signed medical data and decryption key ciphertext of the reporter and the reported person, as well as the normal range of the test indicator, to the trusted execution environment; The data verification module is configured to: verify the received data in a trusted execution environment, and upon successful verification, decrypt the medical data of the reporter and the reported person based on the ciphertext to obtain the medical data of the reporter and the reported person; The data processing module is configured to: process the medical data of the reporter and the reported person based on the normal range of the detection indicators in the trusted execution environment, specifically: determine in sequence whether the specific value of each detection indicator in the medical data of the reporter and the reported person is within the normal range of the detection indicator; if the detection value of a detection indicator is within the normal range, map it to a specific color system; if the detection value of a detection indicator is outside the normal range, map it to another contrasting color system; and through the above mapping method, convert the medical data of the reporter and the reported person into data images of the reporter and the reported person that reflect the data characteristics; A similarity calculation module is configured to: extract features from the data image of the reporter and the data image of the person being reported in a trusted execution environment to obtain feature sets of the two, perform similarity calculation based on the feature sets of the two, obtain a similarity score, and send the similarity score to a trusted third party; The infringement determination module is configured to: a trusted third party determines whether there is infringement between the medical data of the person being reported and the medical data of the reporter based on a comparison of the similarity score with a set threshold.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the medical data infringement detection method based on image mapping as described in any one of claims 1 to 6 are implemented.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the medical data infringement detection method based on image mapping as described in any one of claims 1 to 6 are implemented.

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