Lightweight batch tamper detection method for medical iot outsourcing anonymous data

Abstract

The application discloses a lightweight batch tamper-proof detection method for anonymous outsourcing data of a medical Internet of Things, relates to the technical field of medical Internet of Things data security, and comprises a trusted authority (TA) initialization system, which generates a master key and publishes public parameters. A terminal user submits identity registration, and the TA assigns a symmetric key and generates an anonymous identity after verification, and synchronizes a tamper-proof detection vector to a medical cloud. The user encrypts medical data, generates a homomorphic signature, and then uploads the homomorphic signature, the cloud server stores the homomorphic signature after verifying the data integrity through the detection vector, a third-party auditor initiates a challenge regularly, the cloud server returns an aggregated signature and a ciphertext proof, and the auditor completes batch tamper-proof verification through a bilinear mapping equation. When malicious behavior occurs, the TA traces the real identity by means of the master key, and updates the detection vector to quickly revoke the outsourcing permission of the user. Through the aggregated signature and the tamper-proof detection vector mechanism, unified batch verification is realized, and the system load in the resource-restricted medical device environment is effectively reduced.

Description

Technical Field

[0001] This invention relates to the field of medical Internet of Things (IoT) data security technology, specifically to a lightweight batch anti-tampering detection method for anonymous outsourced medical IoT data. Background Technology

[0002] With the rapid development of smart healthcare, telemedicine, and the Internet of Things in healthcare, a massive amount of medical data generated by wearable devices, medical sensors, mobile terminals, and electronic medical record systems continues to grow. This type of real-time data (such as physiological data collected by wearable devices, electronic medical records, and medical images) is typically characterized by high real-time requirements, large scale, and high privacy sensitivity. Furthermore, medical terminal devices have inherent limitations in terms of computing power, storage space, and energy capacity. Therefore, medical institutions and end-users usually upload the massive amounts of medical data they collect to cloud servers for outsourced storage and computation processing.

[0003] However, in cloud-assisted medical IoT environments, once users outsource their data to the cloud, they lose direct control over the physical storage location and status of their data. Cloud servers may intentionally or unintentionally delete, tamper with, or even falsify user data due to hardware failures, software vulnerabilities, cyberattacks, or commercial interests. Especially in medical scenarios, if critical medical data is tampered with, it could lead to misdiagnosis, medical accidents, or even serious privacy breaches.

[0004] To address the issue of outsourced data integrity, existing research has proposed numerous cloud storage auditing and anti-tampering detection solutions. However, the following prominent technical bottlenecks still exist in the medical IoT environment:

[0005] First, the computational and communication overhead is high, making it difficult to adapt to resource-constrained medical terminal environments. Traditional integrity auditing solutions often rely on complex bilinear pairing, cumbersome certificate management, extensive signature verification, or frequent data interactions. As the number of medical terminals and the size of data blocks continue to increase, the overall system overhead grows linearly, severely impacting the operational efficiency of mobile medical devices and wireless environments.

[0006] Second, existing solutions struggle to balance patient privacy protection with the traceability of malicious behavior. Some solutions, in order to achieve public auditing, directly expose user identity information, putting patient privacy at risk of leakage; while other completely anonymous solutions cannot effectively track identities and revoke permissions when users maliciously upload illegal data or forge signatures.

[0007] Third, batch auditing is inefficient. Most traditional solutions can only perform integrity verification for a single user or a single file, making them unsuitable for multi-user, multi-file concurrent outsourcing scenarios. In large-scale medical data environments, frequent independent verification significantly increases the computational burden on both the cloud and the auditing end.

[0008] Fourth, some solutions rely excessively on complex certificate management mechanisms or high-cost blockchain structures, which introduce additional storage, computing, and economic costs while ensuring security, which is not conducive to the deployment of actual medical IoT systems.

[0009] Therefore, given the problems of high computational overhead, insufficient privacy protection, low efficiency of batch anti-tampering detection, and difficulty in tracking malicious users in existing technologies, designing a batch anti-tampering detection solution for medical outsourcing data that combines lightweight, high efficiency, privacy protection, and batch auditing capabilities has become an urgent and important challenge in the field of medical cloud security. Summary of the Invention

[0010] The purpose of this invention is to overcome the shortcomings of the prior art and provide a lightweight batch anti-tampering detection method for anonymous outsourced medical IoT data.

[0011] The objective of this invention is achieved through the following technical solution:

[0012] This application discloses a lightweight, batch anti-tampering detection method for anonymized outsourced data in the medical Internet of Things (IoT), comprising the following steps:

[0013] System initialization phase: The trusted authority (TA) initializes the public parameters, sets up a bilinear pairing mapping, a collision-resistant hash function, and a lightweight symmetric encryption algorithm, generates the system's master key, and stores it secretly; after completing the system initialization, the trusted authority (TA) publishes the system's public parameter set.

[0014] User registration and anonymous identity generation stage: The end user submits identity information to the trusted authority TA for system identity registration. After the trusted authority TA verifies the identity information, it assigns a symmetric key to the end user and generates an anonymous identity. At the same time, it generates an anti-tampering detection vector and sends it to the medical cloud server.

[0015] Outsourced upload stage: The end user uses the symmetric key assigned to them by the TA to symmetrically encrypt the medical data, generate a homomorphic signature, and upload it to the medical cloud server along with the ciphertext. After receiving the signature and ciphertext, the medical cloud server uses the pre-stored anti-tampering detection vector and auxiliary integrity detection parameters to verify the signature. The data is stored only after ensuring its legality and integrity.

[0016] Batch anti-tampering detection phase: Third-party auditors periodically issue challenges to the medical cloud server. After receiving the challenge, the medical cloud server calculates the aggregate signature and aggregate ciphertext using the anti-tampering detection vector based on the challenge information and returns the proof information. After receiving the proof information, the third-party auditor determines whether the batch medical data has been tampered with by verifying the bilinear mapping equation.

[0017] Identity Tracking and Revocation Phase: When the system detects malicious behavior, the trusted authority (TA) uses its master key to quickly track the true identity of the malicious user and promptly updates the anti-tampering detection vector to send to the medical cloud server, thereby enabling the rapid revocation of the malicious user's outsourced permissions.

[0018] Preferably, the system initialization phase specifically includes the following sub-steps:

[0019] S11, Trusted Authority (TA) based on finite field Choose a non-singular elliptic curve as E: ,in, and The coefficients of the elliptic curve equation are selected by the trusted and authoritative organization TA on the elliptic curve. addition cyclic group G1 and Factorial cyclic group G2, and determine bilinear mappings. Select the first generator P1 and the second generator P2 of G1, and randomly select the system master key. Through formula Computing system master public key ;

[0020] S12. The Trusted Authority (TA) sets up three collision-resistant hash functions: , , Then set a symmetric key length of... Enc, a lightweight symmetric encryption algorithm;

[0021] S13, Trusted Authoritative Institution (TA) Releases System Public Parameter Set And through a secure channel to the i-th medical terminal user Sending System Common Parameter Set ,in, It indicates the number of medical terminal users and secretly stores the master key used to generate user symmetric keys. .

[0022] Preferably, the user registration and anonymous identity generation stage specifically includes the following steps:

[0023] S21. Each medical terminal user submits their identity information to a trusted authority (TA) for anonymous registration within the system. The i-th terminal user... Randomly select the first random number Sign identity information and define the message to be signed. Used to calculate the signature of user registration information After signing, the i-th terminal user Registration information Send to a trusted and authoritative organization (TA); among them, This represents the region where the i-th terminal user is located. Indicates the first timestamp. This indicates the signature of the first part of the user registration information. This indicates the signature of the second part of the user registration information;

[0024] S22. When the trusted authority (TA) receives the registration information At that time, obtain the second timestamp. And calculate the time difference of registration information transmission. ,like If the trusted and authoritative organization (TA) refuses to accept the registration information, then the registration information will be rejected. Otherwise, a trusted and authoritative organization (TA) will verify the signature. Indicates the preset maximum transmission delay;

[0025] S23, Verification Formula for Trusted Authoritative Institutions (TA) If the formula is not true, it means the signature verification failed, and the i-th terminal user... The submitted registration information is invalid; otherwise, it indicates that the i-th terminal user... Registration successful, and for Issue anonymous identities;

[0026] S24. A trusted and authoritative institution (TA) selects a second random number. And calculate the length as symmetric key Subsequently, the credible and authoritative organization TA used a formula... Calculate the i-th terminal user Corresponding anonymous identity ,in, Used to protect the anonymity of end users; a trusted authority (TA) communicates with the i-th end user through a secure channel. send Meanwhile, it is securely stored by a trusted and authoritative organization, TA. , , and The information relating between them;

[0027] S25. A trusted and authoritative institution (TA) periodically selects a third random number. As a blinding parameter, auxiliary blinding factors were calculated. Generate the first anti-tamper detection vector and the first auxiliary parameter ,in , express The i-th detection vector component in the data; subsequently, the trusted authority (TA) will use the first tamper-proof detection vector. and the first auxiliary parameter Send to the medical cloud server MCS.

[0028] Preferably, the outsourced upload stage specifically includes the following steps:

[0029] S31, Each with an anonymous identity End users utilize symmetric keys allocated by a trusted authority (TA). Medical sensitive data is encrypted using the lightweight symmetric encryption algorithm Enc. Encrypt and generate ciphertext. ;

[0030] S32, Through the formula Calculation parameters The end user, based on the ciphertext Generate homomorphic signatures Subsequently, end users outsourced sensitive medical data, transferring encrypted-signature pairs... Uploaded to the medical cloud server MCS;

[0031] S33, Based on the first anti-tampering detection vector V and the first auxiliary parameter Medical cloud server MCS utilizes equation Ciphertext-signature pair The validity of the ciphertext-signature pair is verified; if the equation holds, then the ciphertext-signature pair is valid. Valid; if the equation does not hold, it indicates a ciphertext-signature pair. Invalid or the anonymous identity of the terminal user The outsourcing authority has been revoked, and the medical cloud server refuses to store data.

[0032] Preferably, the batch anti-tampering detection stage specifically includes the following steps:

[0033] S41. Anonymous identity for each end user Third-party auditors (TPA) are randomly selected. And send challenge information to the medical cloud server MCS , This represents the challenge coefficient randomly selected by the third-party auditor (TPA) for the i-th end user.

[0034] S42, Medical Cloud Server MCS received the challenge information. Then, using the first anti-tamper detection vector Calculate the aggregate signature separately and combined ciphertext After that, the anti-tampering detection information will be... Send it to a third-party auditor (TPA) to verify the integrity of the information;

[0035] S43. Third-party auditor TPA pre-calculation Through pre-calculation With each end user's anonymous identity Binding prevents cloud servers from aggregating data in advance and from using other users' data to forge, tamper with, or lose data and signatures;

[0036] S44, Based on the system common parameter set and tamper-proof detection information Detection equation If the equation holds true, it means that all encrypted medical data from end users in different regions has not been tampered with or forged.

[0037] Preferably, the identity tracking and revocation phase specifically includes the following steps:

[0038] S51. Trusted and authoritative institutions (TAs) receive anonymous information from malicious users. Then, based on the master key Calculate and recover the real identity of malicious users ;

[0039] S52, A trusted authority (TA) obtains the user's symmetric key. Then, through the formula Calculate its corresponding second random number Afterwards, the credible and authoritative organization TA re-randomized the blinding factor. And update auxiliary blinding factors Calculate the second anti-tampering detection vector Second auxiliary parameter ;

[0040] S53, If the user index in the second anti-tampering detection vector and Then the trusted and authoritative institution TA sets Otherwise set Subsequently, the trusted authority (TA) sent a message to the medical cloud server. ;

[0041] S54, Medical Cloud Server Received Then, using the latest right Update to achieve anonymity for malicious users. The outsourcing authority was revoked, including the updated anti-tampering detection vector. Auxiliary integrity detection parameters .

[0042] The beneficial effects of this invention are:

[0043] 1) Significantly reduce system computing and communication overhead: This application achieves unified batch verification of multi-user and multi-file data through aggregated signature and anti-tampering detection vector mechanism, which effectively reduces the system load in resource-constrained medical device environments.

[0044] 2) Balancing anonymity and privacy protection with traceability: The conditional anonymity mechanism prevents third-party auditors from obtaining the patient's true identity, while trusted authoritative institutions can quickly restore the true identity and revoke permissions in the event of malicious behavior.

[0045] 3) Support for integrity verification in encrypted state: This application combines lightweight symmetric encryption with homomorphic signatures to achieve integrity verification of medical data without decryption, effectively protecting patient privacy information.

[0046] 4) Possesses excellent batch processing capabilities: Supports multi-user, multi-file concurrent outsourcing scenarios, and can complete batch auditing with approximately constant overhead, making it suitable for massive medical IoT data environments.

[0047] 5) Strong security: This application builds a security foundation based on the discrete logarithm difficulty problem and the computational Diffie-Hellman difficulty problem, which can effectively resist data forgery, replay attacks and unauthorized user impersonation attacks.

[0048] 6) Wide range of applications: This application is not only applicable to smart healthcare and telemedicine scenarios, but can also be extended to mobile health monitoring, regional medical collaboration and wearable device data security outsourcing. Attached Figure Description

[0049] Figure 1 This is a schematic diagram illustrating the steps of a lightweight batch anti-tampering detection method for anonymous outsourced medical IoT data according to an embodiment of the present invention. Detailed Implementation

[0050] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] See Figure 1This application proposes a lightweight batch anti-tampering detection method for anonymous outsourced medical IoT data. It achieves aggregated verification of multi-user, multi-file medical data by constructing a lightweight homomorphic signature and anti-tampering detection vector. Through a conditional anonymity mechanism, it protects the privacy of patients' real identities while supporting trusted authoritative institutions to restore the identities and revoke the permissions of malicious users. Lightweight symmetric encryption technology enables integrity detection of sensitive medical data in encrypted form. Simultaneously, the aggregated verification mechanism significantly reduces the communication and computational overhead of the system in batch audit scenarios. Specifically, the method includes the following steps:

[0052] S1. System Initialization Phase: The Trusted Authority (TA) initializes the public parameters, sets up a bilinear pairing mapping, a collision-resistant hash function, and a lightweight symmetric encryption algorithm, generates the system's master key, and stores it secretly; after completing the system initialization, the Trusted Authority (TA) publishes the system's public parameter set.

[0053] S2, User Registration and Anonymous Identity Generation Stage: The end user submits identity information to the trusted authority TA for system identity registration. After verifying the identity information, the trusted authority TA assigns a symmetric key to the end user and generates an anonymous identity. At the same time, it generates an anti-tampering detection vector and sends it to the medical cloud server.

[0054] S3, Outsourced Upload Stage: The end user uses the symmetric key assigned to them by the TA to symmetrically encrypt the medical data, generate a homomorphic signature, and upload it to the medical cloud server along with the ciphertext. After receiving the signature and ciphertext, the medical cloud server verifies the signature using pre-stored anti-tampering detection vectors and auxiliary integrity detection parameters. It stores the data only after ensuring its legality and integrity.

[0055] S4, Batch Anti-Tampering Detection Phase: Third-party auditors periodically issue challenges to the medical cloud server. After receiving the challenge, the medical cloud server calculates the aggregate signature and aggregate ciphertext using the anti-tampering detection vector based on the challenge information and returns the proof information. After receiving the proof information, the third-party auditors determine whether the batch medical data has been tampered with by verifying the bilinear mapping equation.

[0056] S5. Identity Tracking and Revocation Phase: When the system detects malicious behavior, the trusted authority (TA) uses its master key to quickly track the real identity of the malicious user and promptly updates the anti-tampering detection vector to send to the medical cloud server, thereby enabling the rapid revocation of the malicious user's outsourced permissions.

[0057] For example, in the system initialization phase, a trusted authority sets common system parameters, including elliptic curve parameters, bilinear mapping, generators, master key, and collision-resistant hash function, and secretly stores the master key. This specifically includes the following sub-steps:

[0058] S11, Trusted Authority (TA) based on finite field Choose a non-singular elliptic curve as E: ,in, and Denotes the coefficients and satisfies The credible and authoritative institution TA selects the elliptic curve. addition cyclic group G1 and Factorial cyclic group G2, and determine bilinear mappings. Select the first generator P1 and the second generator P2 of G1, and randomly select the master key. Through formula Calculate the master public key ;

[0059] S12. The Trusted Authority (TA) sets up three collision-resistant hash functions: , , Then set a symmetric key length of... Enc, a lightweight symmetric encryption algorithm; where, Used to map input to , Used to map input to , Used to map the input to a length of The bit string;

[0060] S13, Trusted Authoritative Institution (TA) Release System Public Parameter Set And through a secure channel to the i-th medical terminal user Sending System Common Parameter Set ,in, It indicates the number of medical terminal users and secretly stores the master key used to generate user symmetric keys. .

[0061] For example, in the user registration and anonymous identity generation stage, the end user submits registration information to a trusted authority (TA). After verifying the user's legitimacy, the TA generates an anonymous identity, a symmetric key, and an anti-tampering detection vector for the user. This specifically includes the following steps:

[0062] S21. Each medical terminal user submits their identity information to a trusted authority (TA) for anonymous registration within the system. The i-th terminal user... Randomly select the first random number Sign identity information and define the message to be signed. Used to calculate the signature of user registration information After signing, the i-th terminal user Registration information Send to a trusted and authoritative organization (TA); among them, This represents the region where the i-th terminal user is located. Indicates the first timestamp. This indicates the signature of the first part of the user registration information. This indicates the signature of the second part of the user registration information;

[0063] S22. When the trusted authority (TA) receives the registration information At that time, obtain the second timestamp. And calculate the time difference of registration information transmission. ,like If the trusted and authoritative organization (TA) refuses to accept the registration information, then the registration information will be rejected. Otherwise, a trusted and authoritative organization (TA) will verify the signature. Indicates the preset maximum transmission delay;

[0064] S23, Verification Formula for Trusted Authoritative Institutions (TA) If the formula is not true, it means the signature verification failed, and the i-th terminal user... The submitted registration information is invalid; otherwise, it indicates that the i-th terminal user... Registration successful, and for Issue anonymous identities;

[0065] S24. A trusted and authoritative institution (TA) selects a second random number. And calculate the length as symmetric key Subsequently, the credible and authoritative organization TA used a formula... Calculate the i-th terminal user Corresponding anonymous identity ,in, Used to protect the anonymity of end users; a trusted authority (TA) communicates with the i-th end user through a secure channel. send Meanwhile, it is securely stored by a trusted and authoritative organization, TA. , , and The correlation information between them is used for subsequent conditional identity tracking; among them, Used for end-user verification of anonymous identity and symmetric key timeliness;

[0066] S25. A trusted and authoritative institution (TA) periodically selects a third random number. As a blinding parameter, auxiliary blinding factors were calculated. Generate the first anti-tamper detection vector and the first auxiliary parameter ,in , express The i-th detection vector component in the data, and then the trusted authority TA will use the first tamper-proof detection vector. and the first auxiliary parameter Send to the medical cloud server MCS.

[0067] For example, in the outsourced upload stage, the end user outsources the encryption of data. The end user uses a symmetric key to encrypt medical privacy data, generates a homomorphic signature, and uploads it to the medical cloud server. The medical cloud server verifies the validity of the signature and then stores it. Specifically, this includes the following steps:

[0068] S31, Each with an anonymous identity End users utilize symmetric keys allocated by a trusted authority (TA). Medical sensitive data is encrypted using the lightweight symmetric encryption algorithm Enc. Encrypt and generate ciphertext. ;

[0069] S32, Through the formula Calculation parameters , The end user, based on the ciphertext Generate homomorphic signatures Subsequently, end users outsourced sensitive medical data, transferring encrypted-signature pairs... Uploaded to the medical cloud server MCS;

[0070] S33, Based on the first anti-tampering detection vector V and the first auxiliary parameter Medical cloud server MCS utilizes equation Ciphertext-signature pair The validity of the ciphertext-signature pair is verified; if the equation holds, then the ciphertext-signature pair is valid. Valid; if the equation does not hold, it indicates a ciphertext-signature pair. Invalid or the anonymous identity of the terminal user The outsourcing authority has been revoked, and the medical cloud server refuses to store data.

[0071] For example, in the batch anti-tampering detection stage, a third-party auditor challenges the medical cloud server. The cloud server aggregates and calculates data from multiple users and multiple files to generate batch anti-tampering proofs. The third-party auditor completes the aggregation verification through a bilinear mapping equation and performs batch auditing, specifically including the following steps:

[0072] S41. Anonymous identity for each end user Third-party auditors (TPA) are randomly selected. And send challenge information to the medical cloud server MCS , This represents the challenge coefficient randomly selected by the third-party auditor (TPA) for the i-th end user.

[0073] S42, Medical Cloud Server MCS received the challenge information. Then, using the first anti-tamper detection vector Calculate the aggregate signature separately and combined ciphertext After that, the anti-tampering detection information will be... Send it to a third-party auditor (TPA) to verify the integrity of the information;

[0074] S43. Third-party auditor TPA pre-calculation Through pre-calculation With each end user's anonymous identity Binding prevents cloud servers from aggregating data in advance and from using other users' data to forge, tamper with, or lose data and signatures;

[0075] S44, Based on the system common parameter set and tamper-proof detection information Detection equation If the equation holds true, it means that all encrypted medical data from end users in different regions has not been tampered with or forged.

[0076] For example, in the identity tracking and revocation phase, when malicious user behavior occurs, a trusted authority tracks the malicious user's true identity and updates the anti-tampering detection vector to revoke the user's permission to outsource data to the medical cloud server. The trusted authority then revokes the permission, specifically including the following steps:

[0077] S51. Trusted and authoritative institutions (TAs) receive anonymous information from malicious users. Then, based on the master key Calculate and recover the real identity of malicious users ;

[0078] S52, A trusted authority (TA) obtains the user's symmetric key. Then, through the formula Calculate its corresponding second random number Afterwards, the credible and authoritative organization TA re-randomized the blinding factor. And update auxiliary blinding factors Calculate the second anti-tampering detection vector Second auxiliary parameter ;

[0079] S53, If the user index in the second anti-tampering detection vector and Then the trusted and authoritative institution TA sets Otherwise set Subsequently, the trusted authority (TA) sent a message to the medical cloud server. ;

[0080] S54, Medical Cloud Server Received Then, using the latest right Update to achieve anonymity for malicious users. The outsourcing authority was revoked, including the updated anti-tampering detection vector. Auxiliary integrity detection parameters .

[0081] For example, a correctness analysis of a lightweight batch anti-tampering detection method for cloud-assisted anonymized outsourced medical IoT data is performed:

[0082] During the batch anti-tampering detection phase, the medical cloud server generates aggregated signatures and aggregated ciphertext based on the challenge information. Because homomorphic signatures satisfy the aggregatability property, the signatures of multiple data blocks can maintain verification correctness after linear combination. A third-party auditor uses publicly available system parameters and aggregation proofs to verify the aggregation result using a bilinear mapping equation. Based on the bilinear, non-degenerate, and computable properties of bilinear mappings, it can be proven that the aggregated signature verification process is equivalent to the individual signature verification result. The verification equation always holds when all challenged data blocks are unaltered; however, if data is missing, forged, or illegally replaced, the aggregation equation fails, thus achieving accurate detection of malicious behavior.

[0083] Meanwhile, because the anonymous identity and the real identity are mapped through a master private key from a trusted authority, ordinary auditors cannot recover the patient's real identity. Therefore, the system can ensure the privacy and security of the patient's identity while achieving public auditing.

[0084] For example, the correctness of aggregate signature verification includes:

[0085] Lightweight batch anti-tampering detection In the batch anti-tampering detection phase, third-party auditors This involves challenging the medical cloud server and performing some pre-calculations. After receiving confirmation of the cloud's response, a third-party auditor... Use the properties of bilinear mappings to check the equations. Whether it holds true or not, to verify the anti-tampering detection information. The correctness of the statement is verified through the following process:

[0086]

[0087]

[0088]

[0089]

[0090]

[0091] ;

[0092] The final conclusion is .

[0093] The above description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the concept described herein through the above teachings or related technologies or knowledge. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

Claims

1. A lightweight batch anti-tampering detection method for anonymous outsourced data in the medical Internet of Things, characterized in that, Includes the following steps: System initialization phase: The trusted authority (TA) initializes the public parameters, sets up a bilinear pairing mapping, a collision-resistant hash function, and a lightweight symmetric encryption algorithm, generates the system's master key, and stores it secretly; after completing the system initialization, the trusted authority (TA) publishes the system's public parameter set. User registration and anonymous identity generation stage: The end user submits identity information to the trusted authority TA for system identity registration. After the trusted authority TA verifies the identity information, it assigns a symmetric key to the end user and generates an anonymous identity. At the same time, it generates an anti-tampering detection vector and sends it to the medical cloud server. Outsourced upload stage: The end user uses the symmetric key assigned to them by the TA to symmetrically encrypt the medical data, generate a homomorphic signature, and upload it to the medical cloud server along with the ciphertext. After receiving the signature and ciphertext, the medical cloud server uses the pre-stored anti-tampering detection vector and auxiliary integrity detection parameters to verify the signature. The data is stored only after ensuring its legality and integrity. Batch anti-tampering detection phase: Third-party auditors periodically issue challenges to the medical cloud server. After receiving the challenge, the medical cloud server calculates the aggregate signature and aggregate ciphertext based on the challenge information using anti-tampering detection vectors, and returns the proof information. After receiving the evidence, the third-party auditors determine whether the batch of medical data has been tampered with by verifying the bilinear mapping equation. Identity Tracking and Revocation Phase: When the system detects malicious behavior, the trusted authority (TA) uses its master key to quickly track the true identity of the malicious user and promptly updates the anti-tampering detection vector to send to the medical cloud server, thereby enabling the rapid revocation of the malicious user's outsourced permissions.

2. The lightweight batch anti-tampering detection method for anonymous outsourced medical IoT data according to claim 1, characterized in that, The system initialization phase specifically includes the following sub-steps: S11, Trusted Authority (TA) based on finite field Choose a non-singular elliptic curve as E: ,in, and The coefficients of the elliptic curve equation are selected by the trusted and authoritative organization TA on the elliptic curve. addition cyclic group G1 and Factorial cyclic group G2, and determine bilinear mappings. Select the first generator P1 and the second generator P2 of G1, and randomly select the system master key. Through formula Computing system master public key ; S12. The Trusted Authority (TA) sets up three collision-resistant hash functions: , , Then set a symmetric key length of... Enc, a lightweight symmetric encryption algorithm; S13, Trusted Authoritative Institution (TA) publishes the system's public parameter set. And through a secure channel to the i-th medical terminal user Sending System Common Parameter Set ,in, It indicates the number of medical terminal users and secretly stores the master key used to generate user symmetric keys. .

3. The lightweight batch anti-tampering detection method for anonymous outsourced medical IoT data according to claim 2, characterized in that, The user registration and anonymous identity generation stage specifically includes the following steps: S21. Each medical terminal user submits their identity information to a trusted authority (TA) for anonymous registration within the system. The i-th terminal user... Randomly select the first random number Sign the identity information and define the message to be signed. Used to calculate the signature of user registration information After signing, the i-th terminal user Registration information Send to a trusted and authoritative organization (TA); among them, This represents the region where the i-th terminal user is located. Indicates the first timestamp. This indicates the signature of the first part of the user registration information. This indicates the signature of the second part of the user registration information; S22. When the trusted authority (TA) receives the registration information At that time, obtain the second timestamp. And calculate the time difference of registration information transmission. ,like If the trusted and authoritative organization (TA) refuses to accept the registration information, then the registration information will be rejected. Otherwise, a trusted and authoritative organization (TA) will verify the signature. Indicates the preset maximum transmission delay; S23, Verification Formula for Trusted Authoritative Institutions (TA) If the formula is not true, it means the signature verification failed, and the i-th terminal user... The submitted registration information is invalid; otherwise, it indicates that the i-th terminal user... Registration successful, and for Issue anonymous identities; S24. A trusted and authoritative institution (TA) selects a second random number. And calculate the length as symmetric key Subsequently, the credible and authoritative organization TA used a formula... Calculate the i-th terminal user Corresponding anonymous identity ,in, Used to protect the anonymity of end users; a trusted authority (TA) communicates with the i-th end user through a secure channel. send Meanwhile, it is securely stored by a trusted and authoritative organization, TA. , , and The information relating between them; S25. A trusted and authoritative institution (TA) periodically selects a third random number. As a blinding parameter, auxiliary blinding factors were calculated. Generate the first anti-tamper detection vector and the first auxiliary parameter ,in , express The i-th detection vector component in the data; subsequently, the trusted authority (TA) will use the first tamper-proof detection vector. and the first auxiliary parameter Send to the medical cloud server MCS.

4. The lightweight batch anti-tampering detection method for anonymous outsourced medical IoT data according to claim 3, characterized in that, The outsourced upload stage specifically includes the following steps: S31, Each with an anonymous identity End users utilize symmetric keys allocated by a trusted authority (TA). Medical sensitive data is encrypted using the lightweight symmetric encryption algorithm Enc. Encrypt and generate ciphertext. ; S32, Through the formula Calculation parameters The end user, based on the ciphertext Generate homomorphic signatures Subsequently, end users outsourced sensitive medical data, transferring the encrypted-signature pairs... Uploaded to the medical cloud server MCS; S33, Based on the first anti-tampering detection vector V and the first auxiliary parameter Medical cloud server MCS utilizes equation Ciphertext-signature pair The validity of the ciphertext-signature pair is verified; if the equation holds, then the ciphertext-signature pair is valid. Valid; if the equation does not hold, it indicates a ciphertext-signature pair. Invalid or the anonymous identity of the terminal user The outsourcing authority has been revoked, and the medical cloud server refuses to store data.

5. The lightweight batch anti-tampering detection method for anonymous outsourced medical IoT data according to claim 4, characterized in that, The batch anti-tampering detection stage specifically includes the following steps: S41. Anonymous identity for each end user Third-party auditors (TPA) are randomly selected. And send challenge information to the medical cloud server MCS , This represents the challenge coefficient randomly selected by the third-party auditor (TPA) for the i-th end user. S42, Medical Cloud Server MCS received the challenge information. Then, using the first anti-tamper detection vector Calculate the aggregate signature separately and combined ciphertext After that, the anti-tampering detection information will be... Send it to a third-party auditor (TPA) to verify the integrity of the information; S43. Third-party auditor TPA pre-calculation Through pre-calculation With each end user's anonymous identity Binding prevents cloud servers from aggregating data in advance and from using other users' data to forge, tamper with, or lose data and signatures; S44, Based on the system common parameter set and tamper-proof detection information Detection equation If the equation holds true, it means that all encrypted medical data from end users in different regions has not been tampered with or forged.

6. The lightweight batch anti-tampering detection method for anonymous outsourced medical IoT data according to claim 5, characterized in that, The identity tracking and revocation phase specifically includes the following steps: S51. Trusted and authoritative institutions (TAs) receive anonymous information from malicious users. Then, based on the master key Calculate and recover the real identity of malicious users ; S52, A trusted authority (TA) obtains the user's symmetric key. Then, through the formula Calculate its corresponding second random number Afterwards, the credible and authoritative organization TA re-randomized the blinding factor. And update auxiliary blinding factors Calculate the second anti-tampering detection vector Second auxiliary parameter ; S53, If the user index in the second anti-tampering detection vector and Then the trusted and authoritative institution TA sets Otherwise set Subsequently, the trusted authority (TA) sent a message to the medical cloud server. ; S54, Medical Cloud Server Received Then, using the latest right Update to achieve anonymity for malicious users. The outsourcing authority was revoked, including the updated anti-tampering detection vector. Auxiliary integrity detection parameters .

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