key: cord-0817402-xc7nfkgs authors: Zhang, Can; Xu, Chang; Sharif, Kashif; Zhu, Liehuang title: Privacy-Preserving Contact Tracing in 5G-Integrated and Blockchain-Based Medical Applications date: 2021-02-09 journal: Comput Stand Interfaces DOI: 10.1016/j.csi.2021.103520 sha: b34fb56d45a7051822256d2bdc738e1807d4304b doc_id: 817402 cord_uid: xc7nfkgs The current pandemic situation due to COVID-19 is seriously affecting our daily work and life. To block the propagation of infectious diseases, an effective contact tracing mechanism needs to be implemented. Unfortunately, existing schemes have severe privacy issues that jeopardize the identity-privacy and location-privacy for both users and patients. Although some privacy-preserving systems have been proposed, there remain several issues caused by centralization. To mitigate this issues, we propose a Privacy-preserving contact Tracing scheme in 5G-integrated and Blockchain-based Medical applications, named PTBM. In PTBM, the 5G-integrated network is leveraged as the underlying infrastructure where everyone can perform location checking with his mobile phones or even wearable devices connected to 5G network to find whether they have been in possible contact with a diagnosed patient without violating their privacy. A trusted medical center can effectively trace the patients and their corresponding close contacts. Thorough security and performance analysis show that the proposed PTBM scheme achieves privacy protection, traceability, reliability, and authentication, with high computation & communication efficiency and low latency. We are currently in a critical period of fighting the corona virus (COVID- 19 ) epidemic, where everyone hopes that the whole society will restore to the original order as soon as possible. To achieve this, both commercial healthcare/pharmaceutical organizations and academic researchers in related fields are 5 actively contributing. Until now, hundreds of COVID-19-related papers have been published that have enabled researchers and the general public to know more about overcoming the spread of disease. For example, researchers in [21] used natural language processing techniques to find the propagation characteristics of situation information related to COVID-19 in social media such as Sina 10 Weibo 1 . Besides, more novel technicians have been used to help people fight for this aggressive epidemic. The 5G technology has been considered as one of the promising technicians during the COVID-19 pandemic, which enables higher communication resources with low latency. A report of the World Economic 15 Forum (WEF) [14] shows that COVID-19 rises 5G to the 2nd rank of most discussed tech topics (before COVID-19 is 7th rank). Another report [13] figures that COVID-19 is accelerating 5G demand in some industries such as healthcare. Under this circumstance, more research papers related to 5G and healthcare [23, 1, 31, 42, 9] have been published, which indicates that it is valuable to research 20 5G-based medical applications especially COVID-19-related applications. During those medical applications, contact tracing can be considered as an important step in the prevention and control of infectious diseases such as COVID- 19 . In a typical contact tracing scenario, the close contacts of a patient will be traced and advised to seek proactive treatment from medical 25 organizations as they have a higher probability to be infected compared to noncontacts. A recent study published in Lancet [5] shows that aggressive close contact tracking and isolation is the key measure to Shenzhen city's control of 1 https://weibo.com/ COVID-19 transmission. Hence, it will be helpful to stop the infectious virus from spreading further, if an effective solution of contact tracing is established. 30 In the current pandemic situation, the privacy of information has become an extremely sensitive issue, as, on one hand, immediate information sharing related to contacts is necessary, while on the flip side medical records (and identity) have to be kept confidential. Because users medical data seems to be more sensitive, medical-related privacy issues appear to be more serious and will cause 35 greater losses compared with privacy issues in scenarios like IoT [38, 35] , vehicular network [40] , smart grid [36] , and cloud computing [34, 37, 39, 33] . A report of IBM in 2019 [18] shows that the average cost of data loss per leakage has risen to $3.92 million, where healthcare suffers from the highest loss of $6.45 million per security breach. To mitigate these severe issues caused by privacy leakage, 40 research in both academic and industrial areas has tried to propose new medical applications with the privacy-preserving properties. For example, MIT [27] uses Bluetooth signals from smartphones to perform automated contact tracing privately. Besides, Apple and Google jointly announced a contact tracing system that also makes use of Bluetooth-based proximity-detection technology to find 45 close contacts without compromising location privacy [3] . Unfortunately, there are still some issues with existing contact tracing solutions that need to be solved. More specifically: • Centralized Architecture: All the existing schemes are based on a centralized architecture. In that case, the system may suffer from single-50 point failure (data breach) that will affect the availability of the whole system. • Unreliable Data Storage: Data stored on a centralized server can be easily deleted or tampered with, by malicious system administrators. Besides, malicious users may upload fake information to the system to evade 55 supervision. • Limited Privacy Protection: Privacy protection cannot be fully guaranteed. Existing privacy-preserving solutions can prevent privacy from violating by external adversaries. However, different companies, entities, and agencies who possess the user's private data have unlimited access 60 privileges to them, which may also lead to privacy abuse. In light of these issues that limit the practical use of existing contact tracing systems, we propose a novel privacy-preserving contact tracing scheme in blockchain-based medical applications, named PTBM. More specifically, the 5G-integrated network architecture is leveraged to achieve high communication 65 bandwidth with low propagation & response latency. Blockchain is used to achieve reliable data storage & verification, and secure cryptographic primitives are also leveraged to protect identity privacy and location privacy for both users and patients. To the best of our knowledge, this is the first privacy-preserving contact tracing scheme with a decentralized architecture that achieves reliability 70 in large scale emergency systems. Following are the major contributions of this work. • We design a novel decentralized architecture of contact tracing applications, where the 5G-integrated blockchain network is leveraged as underlying infrastructures. Both permissionless and permissioned blockchains 75 are used to achieve public location checking and supervised data storage & tracing, respectively. • We propose PTBM, a decentralized contact tracing scheme based on the hierarchy of blockchain and secure cryptographic primitives to protect the identity privacy and location privacy for both public users and patients 80 without losing the tracing ability of trusted medical center. • We make thorough security analysis and performance evaluation to prove that the proposed PTBM scheme achieves privacy protection, reliability, authentication, and traceability with high computation & communication efficiency and low latency. The rest of the paper is organized into seven sections. Section 2 gives the background and motivation of the scheme including the related works. Section 3 introduces the fundamentals of blockchain, PCSD cryptosystem, and the bloom filter. Section 4 presents the formalized system model, security model, and design goals of PTBM. In section 5, we elaborate on the complete working of the 90 proposed scheme, followed by the security analysis and performance evaluation in sections 6 and 7, respectively. Finally, section 8 concludes the paper. In this section, some related works about blockchain with 5G, existing contact tracing mechanism and blockchain-based medical applications are intro-95 duced, which prove the novelty of the proposed scheme because to the best of our knowledge, none of the existing schemes supports reliable and privacypreserving contact tracing with low latency. Both the blockchain and 5G can be considered as the next-generations tech-100 nology, hence their integration also attracts the attention of researchers, which covers a variety of scenarios including Internet-of-Things (IoT) [41, 17, 4] , and edge computing [28, 19, 15] . In the IoT scenario, Zhang et al. [41] proposed an edge intelligence and blockchain empowered IIoT framework under the 5G environment. The pro-105 posed scheme includes a cross-domain resource scheduling mechanism and a credit-differentiated edge transaction approval mechanism. It also achieves secure service management and low latency with the help of blockchain and 5G. Hewa et al. [17] presented an automatic certificate revocation scheme, where Elliptic Curve Qu Vanstone (ECQV) certificates are used to achieve lightweight 110 authentication for the resource-restricted IoT devices, and smart contracts are used to achieve reliable and automatic certificate revocation. The proposed scheme realizes a lightweight certificate store, update, and revocation, hence it is suitable for the 5G-based wireless network that interconnects millions of IoT devices. Bera et al. [4] introduced the issues and challenges of apply-115 ing blockchain in the 5G-based IoT environment. They also presented a secure framework for blockchain-based data management that can resist several potential attacks and achieve reliability. Detailed analysis shows that the proposed scheme provides less communication and computation overheads as compared to other relevant works. In the edge computing scenario, Nkenyereye et al. [28] adopted a 5G cellular architecture of Emergency Driven Message (EDM) Protocol which provides higher scalability with lower latency. They also constructed a blockchain-based system where each node is seen as an edge node to reliably store EDM records of which the privacy is protected by a lightweight signcryption scheme. Jangi-125 rala et al. [19] designed a lightweight RFID-based authentication protocol for supply chains, named LBRAPS. Their proposed LBRAPS protocol uses several lightweight cryptographic primitives such as bitwise XOR and one-way hash functions, hence it is suitable for the 5G mobile edge computing environment. Gao et al. [15] incorporated another novel technique, software-defined networks 130 (SDNs) to ensure effective management for blockchain-enabled fog computing in 5G networks. The article also shows that the combination of blockchain and the SDN relieves the pressure of the centralized controller. Besides, some privacy-preserving schemes that try to leverage 5G technology in blockchain-oriented solutions have been presented [26, 16, 7, 22, 10, 11] . We 135 can conclude that more and more researchers are paying attention to solve the privacy issues in 5G-integrated blockchain networks. The current outbreak of coronavirus in the world has attracted extensive attention from both academic institutions and researchers in various fields. In the contact tracing scenario, Ahmed et al. [2] summarizes existing mobile contact tracing APPs which can be divided into three architectures: centralizing, decentralizing, and hybrid. It also concludes that security and privacy issues are 150 one of the most concerns that these APPs are still facing. Fitzsimons et al. [12] designed a privacy-preserving contact tracing mechanism by introducing secure two-party computation (2PC). More specifically, 2PC is used to securely calculate the distance between the location trajectory for a patient and a user. If the calculated distance is below the given threshold, the user will be considered 155 as a close contact of the patient. However, the computation and communication costs of verification are relatively high because the 2PC protocol should be followed for each user who wants to perform the contact tracing operation. Xu et al. presented BeepTrace [32] , a blockchain-based privacy-preserving contact tracing scheme where pseudonyms are used to achieve the unlinkability 160 between the on-chain data and the real-world identity, and the uploaded onchain data are also encrypted by the corresponding user. Compared with other existing technicians, BeepTrace achieves privacy protection with a decentralized architecture. Unfortunately, the user uses Geo public key to encrypt the location information, and the location privacy cannot be well guaranteed if the Geo 165 Solver who holds the decryption private key is malicious. Besides, it cannot actively trace the contact of a given patient. Because of the decentralization and immutability of the blockchain, several works have introduced blockchain in medical applications to achieve reliable sharing. The proposed scheme also used CP-ABE-based access control and the 175 content extraction signature scheme. Li et al. [20] proposed a blockchain-based medical data preservation system to realize privacy-preserving and verifiable medical data storage. Shen et al. [29] presented a privacy-preserving image retrieval mechanism for medical IoT systems that used customized blockchain transaction structures to store the feature vector for each image. Smart con- Based on the above analysis, we make three conclusions that form the major motivation of this work: 1. The integration of blockchain achieves reliable data storage and program execution in a decentralized manner, which also mitigates the compu- In PTBM, we use the blockchain-based 5G network and lightweight cryptographic primitives as the underlying infrastructure and higher construction respectively to achieve reliable and privacy-preserving contact tracing with high 205 efficiency and low latency. More specifically, the reliability of blockchain and enhanced communication bandwidth with low latency of 5G network will be utilized, whereas the deficiencies bring from using either of them alone will be eliminated. The privacy of the user's identity and the patient route can be well protected, whereas the traceability can be also guaranteed. In this section, some basic primitives used in the proposed scheme are introduced. These include the blockchain system, public-key cryptosystem with strong key decryption (PCSD), and the bloom filter (BF). The concept of blockchain originates from the Bitcoin cryptocurrency that achieves reliable transactions under decentralized and trust-less circumstances. Blockchain can be seen as a distributed and append-only ledger with immutability, hence the data stored on the blockchain cannot be deleted or tampered with. Blockchain can be primarily categorized into two types: permissioned and 220 permissionless blockchain. Before participating in the permissioned blockchain, all users must register with the Trusted Authority (TA), hence only the authorized (registered) users can get access to the data block stored on the ledger. The permissionless blockchain does not need access control, and anyone can participate in and query the data stored on the blockchain, however, this leads 225 to scalability and public accountability as well as some privacy issues. For example, external attackers can perform inference attacks that use statistical analysis to shrink the data privacy and identity privacy of the blockchain users with the help of background knowledge. For the purpose of mitigating these privacy issues, we use the hierarchical ar-230 chitecture of blockchain with secure cryptographic primitives to achieve privacy protection without losing the decentralization & accountability properties. The concept of PCSD derives from the Public-Key Cryptosystem with Distributed Decryption (PCDD) presented in [25] . PCSD consists of the following 235 algorithms: • (msk, (pk 1 , sk 1 ), · · · , (pk n , sk n )) ← KeyGen(λ, n) is a probabilistic algorithm that receives a security parameter λ and a number of key-pairs n as input, and outputs a strong private key msk and n key-pairs (pk 1 , sk 1 ), · · · , (pk n , sk n ). Let p, q be two large prime numbers that satisfy |p| = selected. The public key and private key for i is pk i = (N, g, h i ) and is a probabilistic algorithm that receives i's public key pk i and a message m i , and outputs a ciphertext c i associated with m. Given i's public key pk i and a message m i ∈ Z N , choose r i ∈ [1, N/4] randomly and compute the ciphertext • m ← SDec(msk, c) is a deterministic algorithm that receives a strong 255 private key msk and a ciphertext c as input, and outputs the message m. For any ciphertext c, the corresponding message m can be recovered by Assume c i is encrypted by i's public key pk i , note that for all 1 ≤ i ≤ n, the corresponding message m i can be recovered by using msk. Hence we call 260 msk a strong private key. Entities (other users and attackers) that do not know msk or sk i , the cyphertext c i cannot be decrypted. We leverage this property of PCSD to achieve privacy-preserving tracing in the proposed PTBM scheme. The BF can be considered as an indexed structure used for membership 265 queries. The idea of BF is to choose o independent secure hash functions a n } and range R = Z m . The BF consists of three algorithms described as follows: • Init: Choose an m-bit vector B that will be used to store the membership information. All the bits in B are set to 0. • Insert: All the members in D will be inserted into the vector B. More specifically, to insert a membership a i , o hashed values o k=1 H k (a i ) will be computed, and each H k (a i ) position of B will be set to 1. Note that B can also be implemented using a hash In the proposed scheme, we 280 use this implementation to store the location state information to protect the user's location privacy as well as realizing efficient location checking operations. In this section, we formalize the system model, threat model, and design goals of the proposed PTBM scheme. The MC can be considered as a trusted authority controlled by the govern-290 ment. It performs the registration operation to generate & distribute the system parameter and key-pairs for each user, MO, and fog node. MC will stay offline after registration unless it needs to trace the patient information or MO's misbehavior. In that case, it will trace the data stored on the permissioned blockchain. Hence, the introduction of MC cannot violate the decentralization property of 295 the blockchain. Furthermore, this allows that any MC (public or private sector entity) can be easily made part of the existing healthcare infrastructure. The fog nodes are deployed beside the checkpoints that are also controlled by the government. These checkpoints are specially set up to check body temper-300 ature and/or other parameters for public health monitoring. Because we utilize the 5G-integrated network as the underlying infrastructure, which means all the fog nodes are connected by 5G wireless network via 5G base station. Besides, then can also connect other 5G terminals such as the user's mobile phone or other 5G-supported wireless devices. It uploads the user's encrypted location 305 checking information and periodically uploads its current state to the permissioned blockchain. When a user wants to cross a checkpoint, they will use their mobile phone to upload their identity and location information encrypted by their respective 310 public key via 5G wireless channel. Besides, they can execute location checking on the permissionless blockchain to check whether their historical route includes dangerous areas where infected patients have been found. The MOs can be seen as primary healthcare institutions (e.g., community 315 clinics) that treat infectious patients. MO will maintain several nodes in both permissioned and permissionless blockchain for patient route query and location publication respectively. The patients' activity areas will be published to the permissionless blockchain so that every user can check whether they have been to these areas. If they have, then they can go to a nearby MO for examination 320 and treatment in time. In In our threat model, we assume that MC cannot be compromised, and the data stored in MC cannot be leaked. Moreover, MC distributes the system parameter and key-pairs via secure channels. Both the fog nodes and MOs are honest-but-curious, which means that they 335 exactly perform the presented algorithms and protocols, however, they try to infer both users' and patients' private information from the data stored on the blockchain. Note that the collusion between fog nodes and MOs is not allowed because fog nodes are controlled by the government as mentioned earlier. Most of the users are honest, however, there exist several malicious users 340 that try to impersonate other legitimate users to upload fake information to evade supervision. Here we introduce the design goals of PTBM. Note that in Section 6, we prove that PTBM realizes all of these design goals. Besides, MC can also trace the misbehavior of MO. Efficiency: Due to the limited computation and storage capacity of the devices in the 5G-integrated network, the computation complexity should be as 360 low as possible, especially for users and fog nodes that make use of mobile and IoT devices, respectively. Besides, the communication costs should also below to save the channel resource and bring out low latency. In this section, a detailed description of the proposed PTBM scheme is given. The notations used in this work are illustrated in Table 1 . uid i , nid j The pseudonym of user i and fog node j. (upk i , usk i ) Signature key-pair for user i. (npk j , nsk j ) Encryption key-pair for fog node j. Secret key and strong private key for MC. A secure pseudo-random function. H, H 1 , · · · , H o o + 1 secure hash functions. In this process, MC first generates a secret key K ← {0, 1} λ held by MC itself, and the strong private key & signature key-pair (msk, n j=1 (npk j , nsk j )) ← PCSD.KeyGen(λ, n), where the key-pairs will be distributed to the corresponding For each fog node j associated with the real identity n j , MC also generates the pseudonym nid j = F (K, n j ). Then is sends (nid j , npk j , nsk j ) to fog 385 node j through secure channels. Besides, it will initialize a dictionary S on the permissioned blockchain that will be used to store the encrypted state information. Note that each nid is associated with a geographic location about the checkpoint, of which the correspondence is held by MC secretly. When a user i wants to cross a checkpoint associated with fog node j, it first sends a request R i = (uid i , t i , σ i ) to j, where t i is the current timestamp and σ i = DS.Sig(usk i , H(uid i ||t i )) is the signature generated by i's private key. After receiving R i , j first check whether the timestamp t i is obsolete. If the difference between current time t and t i exceeds the threshold t θ , R i will Note that s j is periodically updated, and j will upload the current state information (E j , U sj , B sj ) to S[s j ] stored on the permissioned blockchain before the update operation, where E j = PCSD.Enc(npk j , nid j ) is the encrypted 410 pseudonym of j, and U sj represents the users that passed-by in this time period. For each u i ∈ U sj , u i = PCSD.Enc(npk j , uid i ). When a medical organization MO receives a patient p, it uses the address addr M to execute route query contract of the corresponding uid p given by p with It is obvious to conclude that the user i was in close contact with the patient with a high probability. Therefore, i should go to an MO nearby to determine whether they are infected or not. Note that not all patients are active in executing the location checking op- Besides, for an MO M , it uses its address addr M to invoke the contract of route query and location publication, and all the operations will be recorded to the immutable blockchain. During the Registration process, M uses the real-455 world identity mid M to register from MC and obtains addr M . Hence, each address addr is associated with the corresponding MO's real-world identity, which means that the misbehavior of MO (e.g., publishing fake location information, abusing the route query operation) can be traced by MC. To show that the proposed PTBM scheme achieves the aforementioned design goals of privacy protection, reliability, authentication, and traceability, here we present a thorough analysis. In the proposed scheme, both the privacy of patients and other users should 465 be protected, which includes identity privacy and route privacy. In PTBM, each user will be assigned a pseudonym generated by MC by computing uid i = F (K, u i ). If the pseudo-random function F is secure, the user i's pseudonym uid i and the corresponding real-world identity u i is unlinkable 470 without knowing the secret key K held by MC. The patient p can be considered as a special kind of user. During the Route Query process, their pseudonym uid p will be used. As mentioned above, the proposed PTBM scheme achieves unlinkability between u p and uid p . Hence, only the query requester MO knows the real identity of p, which is inevitable 475 because the MO is responsible for p's examination and treatment. For external adversaries, the inference attack cannot be performed because they can only access the permissionless blockchain that only stores the published location information. Therefore, both honest-and-curious MO and external adversaries cannot in-480 fer the real-world identity of both patients and other users, which means the proposed PTBM scheme achieves identity privacy protection. In the permissioned blockchain, given a pseudonym uid i , the encrypted route In the permissionless blockchain, only the bloom filter B associated with a 495 patient p's activity area is published. As mentioned above, external adversaries cannot find which user has been to this area associated with B. Therefore, both honest-and-curious MO and external adversaries cannot recover the exact route of any user i, which means the proposed PTBM scheme achieves location privacy protection. 500 Due to the reliability of blockchain which has been well established in the literature, all the data stored on the permissionless and permissioned blockchain cannot be modified or deleted. Hence, the correctness of query/tracing results on the blockchain can also be guaranteed. In PTBM, each user is assigned a key-pair for generating digital signatures. For a malicious user, it cannot impersonate another legitimated user to upload the information without knowing the private key. Besides, we use timestamp t i to resist replay attacks, which means the malicious users cannot use the obsolete request of others. Therefore, PTBM achieves effective user authentication. To evaluate the computational performance of PTBM, we set the number of 540 users m ranging from 1000 to 10000 with increments of 1000, the number of fog nodes n = 100, the number of patients m/1000, and the number of users that perform location checking operation to m * 20%. We randomly generate the route of each user, and the patients are also uniformly & randomly selected. All the evaluations are executed 5 times, and we calculate the average execution time 545 for each process to remove relative errors. The results are shown in Figure 2 . During the Registration process, each user i is assigned as a pseudonym uid i and a signature key-pair (upk i , usk i ), each fog node j is assigned as a pseudonym nid j and an encryption/decryption key-pair (npk j , nsk j ). Assume Besides, the registration operation only performs once, and in the real world situation, it will be done immediately when a new user joins. As is illustrated in Figure 2 (b), the execution time is not related to the number of users pass by. We also notice that the execution time slightly increases with the number of users increases. That is because a larger number of users 580 bring out a larger size of state information for node j that will be uploaded to the permissioned blockchain, which results in higher communication delay. During the Route Query process, the MO queries the patient p's route During the Location Publication process, the MO queries the permissioned blockchain to obtain p's recent activity areas, and uploads the associated bloom filter to the permissionless blockchain. Assume the number of p's recent activity 590 area is n p , the time cost of Location Publication process for MO is τ (Query) + τ (Update), the corresponding time complexity is O(1). As can be seen in Figure 2 As is illustrated in Figure 2 As is seen in Figure 2 (e), the execution time is irrelevant to the number of 610 users and ranges from 35ms to 45ms, which is efficient. During the Contact tracing process, MC recovers each contact's pseudonym by executing PCDD.SDec operations for each state s obtained by Patient Tracing process. Hence, it takes m j · n p · τ (PCSD.SDec) to recover all the pseudonym 615 for all states, and the corresponding time complexity is O(m j · n p ). As is seen in Figure 2 (f), the execution time is irregular, that is because, in our experimental setting, each user's route is randomly generated which results in random m j and n p . We evaluate the communication cost for users, fog nodes, and MOs in our experiment, as shown in Table 2 , where we set the number of users m = 1000. It can be observed that the communication costs for users, fog nodes, and MOs are all less than 10KB, which is quite efficient. Therefore, PTBM achieves high communication efficiency and can well save the bandwidth usage and reduce the 635 delay of the 5G-integrated infrastructure. For future work, we will consider leveraging this blockchain-based solution for more comprehensive patient management, vaccination protocols, and protected information sharing with the international organizations in a global pandemic scenario. Besides, how to integrate the emerging 5G technology to other 665 relevant medical application that demands high communication reliability and low latency is also a promising research direction. Technologies trend towards 5g network for smart health-care using iot: A review A survey of COVID-19 contact tracing apps How apple and google are enabling covid-19 contacttracing Blockchain-envisioned secure data delivery and collection scheme for 5g-based iot-enabled internet of drones environment Epidemiology and transmission of covid-19 in 391 cases and 1286 of their close contacts in shenzhen, china: a retrospective cohort study Blockchain for e-healthcare systems: Easier said than done A comprehensive review of the COVID-19 pandemic and the role of iot, drones, ai, blockchain, and 5g in managing its impact How can blockchain help people in the event of pandemics such as the covid-19? 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