Efficient Group Blind Signature for Medical Data Anonymous Authentication in Blockchain-Enabled IoMT
College of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
* Corresponding Author: Chaoyang Li. Email:
Computers, Materials & Continua 2023, 76(1), 591-606. https://doi.org/10.32604/cmc.2023.038129
Received 28 November 2022; Accepted 15 March 2023; Issue published 08 June 2023
AbstractBlockchain technology promotes the development of the Internet of medical things (IoMT) from the centralized form to distributed trust mode as blockchain-based Internet of medical things (BIoMT). Although blockchain improves the cross-institution data sharing ability, there still exist the problems of authentication difficulty and privacy leakage. This paper first describes the architecture of the BIoMT system and designs an anonymous authentication model for medical data sharing. This BIoMT system is divided into four layers: perceptual, network, platform, and application. The model integrates an anonymous authentication scheme to guarantee secure data sharing in the network ledger. Utilizing the untampered blockchain ledger can protect the privacy of medical data and system users. Then, an anonymous authentication scheme called the group blind signature (GBS) scheme is designed. This scheme can provide anonymity for the signer as that one member can represent the group to sign without exposing his identity. The blind property also can protect the message from being signed as it is anonymous to the signer. Moreover, this GBS scheme is created with the lattice assumption, which makes it more secure against quantum attacks. In addition, the security proof shows that this GBS scheme can achieve the security properties of dynamical-almost-full anonymity, blindness, traceability, and non-frameability. The comparison analysis and performance evaluation of key size show that this GBS scheme is more efficient than similar schemes in other literature.
Internet of medical things (IoMT) is the direction for traditional healthcare service systems with the increasing number of wearable and mobile medical devices . IoMT establishes a medical network that aggregates dispersive medical data created by many smart medical devices. Although these massive amounts of medical data can contribute much to patient treatment, drug discovery, and medical equipment manufacturing, they also face many security issues as they contain sensitive information about the patient and medical institution. Therefore, the security of medical data and user privacy is more important for data sharing through IoMT .
Blockchain technology brings new vitality to traditional IoMT and helps to establish the distributed medical data management platform called blockchain-based Internet of medical things (BIoMT) . The public blockchain ledger guarantees that data and operation records are not tampered with, which can well solve the centralized management problem in traditional IoMT . Through this distributed platform, the patient can freely choose the needed medical institution and get more precise treatment with historical medical data, the doctor can improve the efficiency of diagnosis with more comprehensive medical data, and the researcher can develop the level of drug discovery and medical equipment manufacturing with massive amounts of medical data. By focusing on these goals, more and more BIoMT frameworks and proposes are emerging in recent years, such as Healthchain, Fortified-chain, and so on [5–15]. Meanwhile, some AI-powered methods also appear to improve data-handling capacity and analysis efficiency, such as machine learning and federated learning [16,17]. However, these works mainly focus on medical data management and utilization but rarely care about the security of medical data and user privacy using the cryptographic algorithm .
Privacy-preserving is the main challenge for BIoMT . As identity privacy is generally inserted in medical data, it can be divulged easily with centralized management forms, insecure data transmission, or man-made sabotage. Meanwhile, medical data are indicators of patients’ physical condition, which are also personal privacies, especially for important persons. Blockchain transactions contain the operator’s signature, essential in verifying transaction legitimacy, confirming the operator’s identity, and data traceability for medical disputes . For the privacy security of medical data in BIoMT, the signer’s anonymity is a more critical issue. A group signature allows one member to represent the group for signing, and the verifier can verify the signature’s legitimacy but not confirm which one is the real signer [21–26]. Meanwhile, a blind signature utilizes a blind factor to blind the message to be signed [27–30]. Some protocols with these two properties have been proposed, which can guarantee anonymous of the user identity and data information [31–33]. Therefore, this paper plans to combine these two signatures to design a GBS scheme and establish an anonymous authentication model for privacy-preserving in BIoMT.
This paper mainly focuses on the problems of authentication difficulty and privacy leakage in the medical data-sharing process. It contributes to the privacy-preserving method for medical data and users in the BIoMT system. A four-ledger architecture for the BIoMT system has been introduced first, and an anonymous authentication model has been designed to strengthen the security of the data-sharing process in the network ledger. Then, a GBS scheme has been proposed to achieve anonymous authentication. The detailed contributions of this work are as follows:
• A four-ledger architecture for the BIoMT system has been introduced, which contains four layers of the perceptual, network, platform, and application. From data collection to application, this distributed peer-to-peer platform can guarantee the transparency and integrity of medical data. Meanwhile, an anonymous authentication model has been designed, which can strengthen the security of the data-sharing process in the network ledger.
• A GBS scheme has been proposed to achieve anonymous authentication. It provides anonymity for the signer as the group can be represented by one member for signing. The blind property can protect the message to be signed as it is anonymous to the signer, but the signer cannot deny a valid signature signed by himself. Meanwhile, this scheme is based on lattice assumption, which can also guarantee the security of anti-quantum attacks.
• The correctness analysis and security proof have been given, which show that the GBS scheme can capture properties of blindness, traceability, and non-flammability. The comparison analysis and performance evaluation of key size show that the GBS scheme is efficient.
In the following, Section 2 presents the reviews of related work, Section 3 gives an anonymous model for BIoMT, Section 4 proposes a new GBS scheme, Section 5 shows the security proof and analysis, section 6 presents the efficiency comparison and performance, and Section 7 concludes.
The distributed BIoMT platform and application are constructed by blockchain, Hyperledger, management model, cryptographic algorithm, etc. Xu et al. proposed a double chain Healthchain system for large-scale medical data management, which contains the userchain and doctorchain . Using blockchain and Hyperledger Fabric, Chenthara et al. constructed a Healthchain system to protect patient privacy and medical data . Li et al. established a novel peer-to-peer platform for medical data management and proposed a Stackelberg pricing algorithm to promote medical data sharing between different medical institutions . Moreover, Hylock et al. presented a Healthchain system around the patient, which can help patients participate in medical data curation and dissemination . Rahoof et al. established a Healthchain system using private and consortium blockchain technology for intra-regional and inter-regional communication . Egala et al. proposed a Fortified-chain for security and privacy-assured IoMT based on blockchain, which mainly focuses on the access control mechanism for medical data . These distributed peer-to-peer platforms give new directions to realize secure medical sharing among different medical users and institutions.
Some new frameworks are also based on blockchain and artificial intelligence (AI) technologies. Singh et al. designed a privacy-preserving model for IoT healthcare data based on federated learning and blockchain . Qahtan et al. focused on security in IoT healthcare industry 4.0 systems and presented a multi-security and privacy benchmarking framework with blockchain . AI-Sumaidaee et al. utilized the Hyperledger fabric to perform the distributed healthcare platform with private blockchain . Zhao et al. established a Brooks-Iyengar quantum Byzantine agreement-centered blockchain networking for smart healthcare . Baucas et al. gave a federated learning and blockchain-enabled fog-IoT platform for wearables in predictive healthcare . Although these platforms based on blockchain and AI has certain security capabilities for medical data, they cannot resist attacks from malicious adversaries with high computing power. There is also a need the cryptographic protocols, such as encryption schemes, signature schemes, and key agreement schemes, to strengthen the system security of BIoMT.
Many anonymous authentication methods exist to blind the signer’s identity or message. The group signature and blind signature are standard protocols that can achieve this function. In the group signature (GS) scheme, one group member can serve as the representative to perform the signing operation. The signature can be verified valid without knowing who signs it . Perera et al. gave a GS scheme with lattice assumption, which can achieve verifier-local revocation with time-bound keys . Xie et al. presented a GS scheme to strengthen the security of anonymous authentication for IoT users . Şahin et al. proposed dynamic GS scheme based on lattice assumptions, and applied the quantum random oracle model to show that the proposed scheme could achieve of anonymity, traceability, and non-fremability . Zhang et al. gave a GS scheme with the verifier-local revocation mechanism based on lattice assumption . Tang et al. designed a GS scheme with multiple managers, which can achieve privacy-preserving for BIoMT . Then, the blind signature (BS) scheme utilizes the blind factor to blind the message to be signed by the signer, which can provide anonymity for this message. Vora et al. proposed a BS scheme that depends on the hardness of big integer decomposition to protect medical data in an e-health system . Li et al. constructed a BS scheme and a proxy BS scheme with lattice assumption, which can strengthen the anti-quantum property for blockchain-based systems [28,29]. Xu et al. introduced a certificateless signcryption scheme with blockchain technology to improve privacy security in an edge computing environment . GS or BS only provides individual security properties for information systems, so it cannot satisfy the demands for group anonymity and message blinding.
Moreover, there also exist some group blind signature schemes, which aggregate the merits of the former two kinds of signature schemes. Kong et al. introduced a practical GBS scheme for privacy-preserving in smart grids . Fan et al. applied the fast GBS scheme to construct a refreshing algorithm of dynamic nodes . Kastner et al. proposed a pairing-free blind signature scheme based on the Algebraic group model. They proved it could reduce to the weakest possible assumption compared with known reduction techniques . These schemes can provide both group anonymity and message blinding and improve the privacy security for users and data in information systems. However, considering the difficult authentication problems and privacy leakage in the medical data-sharing process, these protocols are unsuitable for anonymous authentication for privacy-preserving in the BIoMT system.
This section first descripts the architecture of BIoMT. Then, the anonymous authentication model is presented for BIoMT system.
Fig. 1 shows the architecture of BIoMT, which mainly contains four layers for medical data management. Here, the layers are divided according the four life cycle phases of data collection, storage, share and analyze. Therefore, from the medical data generation to expiration, it generally needs to pass four layers of the perceptual, network, platform, and application. Detail function introductions of every layer are as follows:
• Perceptual layer: This layer contains the smart medical device, such as the clinical thermometer, digital blood pressure monitor, cardio kickboxing, and glucose meter. The patient’s physical condition data will be collected by these devices and uploaded to the BIoMT network. This layer is the edge data collection network, which also preprocesses the data to improve their interoperability for cross-institutional data sharing. Then, First, the data transmission mode generally contains Bluetooth, WIFI, and Zigbee, and these modes upload the medical data to the data management network in the network layer.
• Network layer: This is a distributed IoMT network based on blockchain technology, and it can solve the centralized problem compared with the traditional IoMT system. An anonymous authentication model has been introduced to protect the patient’s privacy. After the collection process, the medical data will be signed with the signer's private key. This model also adds the function of GBS signature to strengthen the anonymity of medical data. Here, real medical data will be stored in a native storage server, and storage address and operation behavior will be recorded onto the blockchain ledger. Here, the user must first visit the public blockchain ledger, and then he can get the storage address and obtain real medical data. This mechanism will prevent the problems of data loss and privacy leakage by direct access to data. Meanwhile, the lightweight storage of address and operation behavior will provide data traceability and audit links and decrease the redundancy of the public blockchain ledger. This layer guarantees the security of storage and sharing processes by utilizing the distributed peer-to-peer network and untampered blockchain ledger.
• Platform layer: medical workers can access and operate the medical data from BIoMT servers through these platforms. The watch, desktop, telephone, and other mobile medical devices and apps can all link to the BIoMT network. Note that the current smartwatch can serve as a medical data management platform and a perceptual device to collect medical data. This layer connects the medical in-network layer and the application layer. A valid user can access medical data from these platforms. Meanwhile, this layer takes responsibility for presetting access control privileges, which can protect users' and medical data privacy more refined.
• Application layer: This layer contains different kinds of medical workers, such as doctors, nurses, researchers, insurance salespeople, and supervisors. These medical data can be used for disease diagnosis, scientific research, and drug development. They also provide evidence for market regulation and medical dispute tracing. Medical workers take responsibility for data management and can utilize these data for disease diagnosis and scientific research. For a piece of medical data, the signature embedded in it can help check its validity. The GBS scheme allows one group member to perform a signing operation on behalf of the group. This signature can be verified as legitimate without knowing who the signer is. Meanwhile, the blind function makes the signature anonymous to the signer.
This BIoMT performs data acquisition, storage, and management roles to support data sharing between medical institutions. The medical data are collected by the smart medical device, stored in the native storage server, shared by the distributed blockchain ledger, managed by different medical platforms, and used by different medical workers. Blockchain technology guarantees transparency and integrity, but user authentication and privacy security are still the weak aspects of BIoMT. There always exist some problems of authentication difficulties and privacy leakage in the medical data sharing process. Especially in the face of quantum computation attacks, current mathematic hard problems-based cryptographic protocols are vulnerable. An anonymous authentication model is introduced for BIoMT in the following subsection, focusing on privacy security in the network layer.
The framework of the model is shown in Fig. 2. This model mainly contains an anonymous authentication scheme and an IoMT blockchain ledger. Here, the anonymous authentication scheme is established with a GBS scheme which can ensure the anonymous verifiability of the signer and the authenticity of medical data. The IoMT blockchain ledger takes responsibility for recording the data store address and operation records to guarantee storage security and traceability.
• Anonymous authentication scheme: This scheme establishes an anonymous authentication mechanism for cross-institutional medical data sharing. This scheme is a GBS scheme based on lattice assumption. When smart medical devices collect medical data, they will be signed by the corresponding medical workers. Then, the signed medical data will be uploaded and packaged into the blockchain transaction. Here, as the patient’s condition needs to synthesize various detection results for comprehensive judgment, it generally needs various detection equipment to cooperate. There is also a need for some medical workers to consult together when the patient’s condition is very complex. The GBS improves the security of medical data and allows one group member to sign messages. The message to be signed is also blind to the signer, which can improve medical data security. Moreover, the medical store address and operations are recorded as transactions and uploaded into the ledger. This scheme establishes an anonymous authentication mechanism for cross-institutional medical data sharing.
• IoMT blockchain ledger: This is a unified ledger for the whole network of BIoMT, which establishes a secure cross-institutional medical data-sharing platform. This ledger only contains lightweight messages as the storage addresses and operations records, which can improve the data sharing efficiency by a more lightweight ledger. When medical workers want to view some medical data, they can derive the original data from the storage address by obtaining access control permission. Meanwhile, they will form a hinged record ledger with time stamps and establish permanent, immutable records. This ledger can provide evidence and traceability for verifying medical data when a medical dispute arises. Every valid user can check the validity of medical data without losing any private information.
To protect the sensitive information security, a GBS scheme based on lattice cryptography has been designed for anonymity authentication. This GBS scheme is based on lattice assumption , where is a uniform distribution. Meanwhile, the algorithms for is performed by the same work way in rings . Although the work way becomes simple, the hardness of this lattice assumption has not decreased. Here, the GM represents the group manager, the GG represents group guest (Signer), the GU represents group user (Message owner), the SV represents signature verifier, and the SO represents signature opener. To generate a valid group signature, these four parties perform the following six algorithms, such as KeyGen., Join, Blind Sign, Verify, Open, and Revoke. This GBS scheme can also realize that the group members free join and revoke.
Moreover, the bimodal Gaussian distribution has been applied into the GBS scheme, which can improve the efficiency of reject sampling  and make the GBS scheme more efficient. Following are the detailed step descriptions of the GBS scheme.
Key Gen.: According to the rules in Ref. , parameters are defined, where is security parameter, and . Then, the system parameters are generated by the following steps:
• Choose a short matrix , as ;
• Generate matrix such that ;
• Choose a short matrix ;
• Generate the matrix which satisfies ;
• Derive according the former principle;
• Output as group public key, as group master secret key, as tracing manager’s (opener’s) secret key, as guest ’s key pair.
Join algorithm: This algorithm contains two parts: one is that GG sends a registration message to GM, the other is that GM generates a member certificate to GG. GG also sets a leaving date and time for registration to prevent pretending by some malicious adversaries.
(1) GG performs:
• Selects vectors , as is the bimodal Gaussian distribution;
• Computes and ;
• Computes ;
• Sets a leaving date and time ;
• Sends to group manager.
(2) GM performs:
• Samples ;
• Sets the revocation token ;
• Computes with the received ;
• Selects randomly;
• Computes ;
• Derives with probability ; otherwise, restart;
• Records GG ’s registration , here “1” represents this GG is active;
• Outputs the member certificate for GG .
Blind sign algorithm: This algorithm contains four parts: GG verifies the date and sends the commitment to GU, GU blind the message to be signed and sends it to GG, GG signs the blind message and returns it back to GU, and GU recover the signature for original message. GG first verifies the validation of his member certificate . Here, with the conditions of , and can be verified with probability for the security parameter (in practice ). Next is the detailed steps of this blind sign algorithm.
1) GG performs:
• Verifies the validity of member certificate ;
• If or , terminates and restarts Join algorithm;
• Continues iff ;
• Sends to the user.
2) GU performs:
• Selects a blind vector ;
• Computes with the former computed ;
• Selects randomly;
• Computes ;
• Drives with probability , and sends it to GG; otherwise, restart.
3) GG performs:
• Confirms the signature expiration date , Otherwise restart;
• GG computes with and the former selected vector ;
• Derives the blind signature of with probability , and sends it to GU; otherwise, restart;
4) GU performs:
• GU computes with the former selected blind vector .
• Recovers signature with probability ; Otherwise, restart;
Verify algorithm: SV performs verification process and gives Accept or Reject.
• SV checks the date and time and , otherwise restart;
• If or , Reject
• Iff , Accept.
Open algorithm: Though this algorithm, it can confirm who is the real signer in the group.
• Samples ;
• If r′i=ri, returns GG’s index i; Otherwise, restart.
Revoke algorithm: GM executes the following steps to change group member and records the revocation information to a list .
• Queries on to get ;
• Updates state (1) to inactive (0), and records into ;
(1) Member certificate correctness: The member certificate is valid when it satisfies three conditions. First is, as is defined in the Sign algorithm. Second is , as this condition is restricted system security reason. Third is , as that it is based on the following Eq. (1) holds.
(2) Signature correctness: The signature is valid for message which also needs those three conditions holds. As in the Verify algorithm, the former two conditions are and , and the third is as the following Eq. (2) holds.
Meanwhile, the signature is correct, which also needs the following conditions to hold. Firstly, a user with a fake expiration date cannot pass verification as the new guest has set a leaving date and time for the member certificate. Secondly, the signature generation time is verified at step 1 of Sign algorithm, only the no expired group member can perform the following signing steps. Thirdly, the verification time is compared with signature generation time . If the user does not have a correct signature expiration date, he will be rejected. Moreover, the signature cannot be accepted, created by the revoked group member whose revocation token is in list .
(3) Opening correctness: On inputting , the signature is generated by guest if , where . The correctness of that open algorithm is based on the following Eq. (3).
This section provides the security proof of the proposed GBS scheme to show it can achieve dynamical-almost-full anonymity, blindness, traceability and non-frameability.
(1) Dynamical-almost-full anonymity: With the free joining and revoking mechanism, one signature cannot be confirmed which group member is the signer, and two different signatures cannot be distinguished who is the real signer between two different signers without the information of group muster’s secret key.
Theorem 1: In random oracle model, this GBS scheme can capture dynamical-almost-full anonymity with the hardness of .
Proof: A query-respond game has been established between the adversary and challenger , and challenger can utilize the forged signature created by adversary to solve instance with non-negligible probability. Detailed proving processes are shown as follows.
Game 0: Suppose adversary can get some information of . Based on the hardness of , cannot distinguish that one signature’s real signer is user or user by the query-respond game with . Meanwhile, can add new user into the group, and perform queries on the opened signatures and member’s revocation token. If asks to add a new user, first confirms whether this user’s identity is registered or not. performs registration process to generate the registration information when this identity does not exist and returns it back. also establish an empty registration list to store the registration information. Here, the returned registration information does not contain the revocation token and key-expiration time. When asks to reveal the revocation token for user , checks and finds out user ’s registration information. He returns member certificate back, and updates . Then, gives two challenge indices with relate to , and derives signature with a random when he confirms that are newly recorded in . After that, gives his guess . It will derive 1 when , and 0 when . Moreover, needs to give two different expiration dates for indices and . cannot pass validation process if he does not provide a right key-expiration date which should satisfy . Even worse, provides two correct expiration dates, will derive challenging signature for verification. will attack the anonymity of signer in this GBS scheme with time-bound keys.
Game 1: executes KeyGen. and derives key pair for the challenging signature. If performs query on an opened signature , will quit and select a random bit as response. Note that it cannot distinguish this game with Game 0. Next, performs the following games.
Game 2: executes Join algorithm to answer for the query on random oracle . If performs query on an opened signature , calculates . Meanwhile, executes Join algorithm and calculates to get . Next, derives signature as response. Here, cannot get anything from this collision-resistant random oracle as he has nothing about the newly registered user. Note that it cannot distinguish this game with the former two games.
Game 3: As is related with and . randomly chooses a blind vector for , and executes the Blind sign algorithm to generate . Meanwhile, can compute from the following Game 4, and derives with probability . Note that it cannot distinguish this game with the former games.
Game 4: randomly selects a vector and calculates . Then, calculates with and , here is derived with from Game 2. The generation probability of is .
Game 5: As generates with challenging bit , is derived uniformly. chooses a random vector and sets . Here, is a proper instance. If can find a solution for this hard problem, he can make a correct distinction between and . Note that it cannot distinguish this game with the former games.
Game 6: is generated independently with . Note that it cannot distinguish this game with the former games. Therefore, it is impossible for to forge a valid signature as is hard.
Now, it can say that this GBS scheme can capture dynamical-almost-full anonymity.
Blindness: The signer cannot deny his signature inserted in one message which he does not know what it is, here this signature can be verified to be true.
Theorem 2: In random oracle model, this GBS scheme can capture statistically blind with the hardness of .
Proof: Suppose adversary can interact with two different users and , it can prove that signatures and generated by two users cannot be distinguished. The blind message and are generated with probability probability . As , it tailors and to be distributed according to the same distribution by rejection sampling lemma . Therefore, it can derive , and these two blind messages are distributed independently from the original message. Then, from the blind sign and revoke steps, the blind signature is generated by with probability , and the signature generated by with probability . It can derive two signatures and which also satisfy . Meanwhile, they are independent from the original message to be signed. Now, it can say that this GBS scheme can capture statistically blind to the attacks from adversary .
Traceability: The signer with relate to one valid signature can be confirmed by the open algorithm, and he cannot deny this signature.
Theorem 3: In random oracle model, this GBS scheme is traceable with the hardness of .
Proof: Suppose adversary can forge a valid signature, and he has ability to add new group user by replacing user’s . Meanwhile, the revocation token can also be queried by . Next, challenger utilizes a pseudo polynomial time (PPT) algorithm to perform a query-answer game with to solve instance. can execute queries on the KeyGen., Join, and Blind sign algorithms with enough times, and responds them one by one. Then, can forge a signature for with enough queried information. With the former hypothesis, this signature derived by is valid. Now, can create a new valid signature with by Forking Lemma . Hence, obtains two equations and , and then generates two vectors and () respectively. There also has . Here, it has as , and is a solution for instance as . However, this is impossible with current computation. So the hypothesis for adversary fails, and the GBS scheme is traceable.
Non-frameability: It cannot generate a legitimate signature by impersonating other people, no matter the group manager and other members.
Theorem 4: In random oracle model, this GBS scheme can capture non-frameability with the hardness of .
Proof: Suppose adversary can forge a valid signature. This signature also opens to user , but this user has never generated the signature. Then, there exists a challenge PPT algorithm which can solve problem from the query-answer game with . signs up a new user by working as the corrupted group member, and makes some queries to the main algorithms in the GBS scheme. answers the queries with the system keys , and . When asks for a signature with relate to message , generates and sends back the corresponding signature . makes these queries for many times, and obtains enough information for the system keys. Next, forges a signature for the target message which opens to user . Based on the former assumption for , is a legitimate signature with overwhelming probability. As also can generate a legitimate signature , it can derive . Then, there will exist because it does exist hash collision. Meanwhile, it also can derive as and . Until now, finds out a solution for the instance.
This section provides the efficiency comparison between the proposed GBS with the other three similar schemes. Key size is an essential index to reflect the scheme performance as that more small key size leads to more efficient scheme performance. Here, the parameters in [22,23], and the proposed GBS are unified and the parameter and in  are converted with the principle of and . Then, the key comparison results with some other similar schemes are shown in Table 1. As for the and , the proposed GBS is a little bigger than those in the other two schemes. But the keys’ distribution with bimodal Gaussian by modeling can make the scheme more secure. As for the , its size in the proposed GBS is nearly half of that in Ref. . As for the signature size, the proposed GBS has a great advantage in the signature verification efficiency with more small size.
Then, to clearly view the difference between the proposed GBS scheme with similar three schemes. The performance environment is a Windows 10 desktop with Intel(R) Core (TM) i7 CPU 3.2 GHz and 16G RAM. The key size is performed on Matlab R2016a with two security levels of 80-bit and 192-bit, where the parameters and are setting as and respectively. The parameter for 80-bit and for 192-bit as it satisfies , the message length is preset to , and is set according the principle in . From the performance results in Fig. 3, is almost the same, and in GBS scheme is smaller than . Furthermore, the signature size comparison is shown independently in Fig. 4, and the signature size in the GBS scheme has more advantages than the other two similar schemes. As the , , and can be pre-generated to save algorithm time, the signature size makes essential effects on the signature generation and verification. Therefore, the small signature size in the GBS scheme can improve the efficiency.
This paper solves the problems of authentication difficulty and privacy leakage in the BIoMT system. A four-ledger architecture for the BIoMT system is introduced first, which contains the perceptual, network, platform, and application. Meanwhile, an anonymous authentication model has been designed to strengthen the security of the data-sharing process in the network ledger. This model integrates the anonymous authentication scheme and untampered blockchain ledger, which can guarantee the traceability of the signature and the anonymity of the signer’s identity privacy at the same time. Then, a GBS scheme has been proposed to achieve anonymous authentication. It keeps the signer’s anonymity through the group representative signing mechanism and protects data privacy with the message binding mechanism. Moreover, the security proof and analysis show that this GBS scheme can capture the needed security properties of dynamical-almost-full anonymity, blindness, traceability, and non-frameability. The efficiency comparison and performance evaluation of key size show that the proposed anonymous authentication model and signature scheme are efficient and practical.
With the number of smart medical devices increasing, the security problems of privacy leakage, data loss, and unauthorized access have become more and more serious. To maximize the value of medical data, privacy security, identity authentication, and access control are the main security issues that should be put into continuous efforts. Therefore, there still exist some exciting research directions in BIoMT, such as the data fine-grained access control, anonymous authentication, and lightweight storage in the cross-institutional sharing process.
Funding Statement: This work was supported by the National Natural Science Foundation of China under Grant 61962009, the Doctor Scientific Research Fund of Zhengzhou University of Light Industry under Grant 2021BSJJ033, the Key Scientific Research Project of Colleges and Universities in Henan Province (CN) under Grant No.22A413010, the Foundation and Cutting-Edge Technologies Research Program of Henan Province (CN) under Grant No. 222102210161, the Natural Science Foundation of Henan Province (CN) under Grant No. 222300420582.
Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the present study.
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