The fifth-generation (5G) wireless communication technology enables high-reliability and low-latency communications for the Intelligent Transportation System (ITS). However, the growingly sophisticated attacks against 5G-enabled ITS (5G-ITS) might cause serious damages to the valuable data generated by various ITS applications. Therefore, establishing a secure 5G-ITS through trust evaluation against potential threats has become a key objective. Furthermore, as a distributed shared ledger and database, Blockchain has the characteristics of non-tampering, traceability, openness and transparency, can support both trust storage and trust verification for trust evaluation. In this paper, we propose a heterogeneous Blockchain based Hierarchical Trust Evaluation strategy, named BHTE, utilizing the federated deep learning technology for 5G-ITS. Specifically, the trusts of ITS users and task distributers are evaluated using the federated deep learning and hierarchical incentive mechanisms are designed for reasonable and fair rewards and punishments. Moreover, the trusts of ITS users and task distributers are stored on heterogeneous and hierarchical blockchains for trust verification. The extensive experiment results show that: (i) the proposed BHTE can achieve reasonable and fair trust evaluations on both ITS users and task distributers; (ii) the BHTE performs excellently with high system throughput and low latency.
Vadim Lyubashevsky, Ngoc Khanh Nguyen, Gregor Seiler
In a set membership proof, the public information consists of a set of elements and a commitment. The prover then produces a zero-knowledge proof showing that the commitment is indeed to some element from the set. This primitive is closely related to concepts like ring signatures and “one-out-of-many” proofs that underlie many anonymity and privacy protocols. The main result of this work is a new succinct lattice-based set membership proof whose size is logarithmic in the size of the set.
Laura Ricci, Damiano Di Francesco Maesa, Alfredo Favenza, Enrico Ferro
Several blockchain projects to help against COVID-19 are emerging at a fast pace, showing the potential of this disruptive technology to mitigate the multi-systemic threats the pandemic is posing on all phases of the emergency management and generate value for the economy and society as a whole. This survey investigates how blockchain technology can be useful in the scope of supporting health actions that can reduce the spread of COVID-19 infections and allow a return to normality. Since the prominent use of blockchains to mitigate COVID-19 consequences are in the area of contact tracing and vaccine/immunity passport support, the survey mainly focuses on these two classes of applications. The aim of the survey is to show that only a proper combination of blockchain technology with advanced cryptographic techniques can guarantee a secure and privacy preserving support to fight COVID-19. In particular, this article first presents these techniques, i.e. zero-knowledge, Diffie Hellman, blind signatures, and proxy re-encryption, then describes how they are used in combination with blockchains to define robust and privacy-preserving solutions. Finally, a brief description of blockchain applications beyond contact tracing and vaccine certification is presented.
The IoT, or Internet of Things has been a major talking point amongst technology enthusiasts in recent years. The internet of thing (IoT) has been emerged and evolved rapidly, making the world's fabric around us smarter and more responsive. The smart home uses one such transformation of IoT, which seems to be the wave of the future. However, with the increasing wide adoption of IoT, data security, and privacy concerns about how our data is collected and shared with others, has also risen. To solve these challenges, an approach to data privacy and security in a smart home using blockchain technology is proposed in this paper. We propose authentication scheme that combines attribute-based access control with smart contracts and edge computing to create a secure framework for IoT devices in smart home systems. The edge server adds scalability to the system by offloading heavy processing activities and using a differential privacy method to aggregate data to the cloud securely and privately. We present several aspects of testing and implementing smart contracts, the differential private stochastic gradient descent algorithm, and system architecture and design. We demonstrate the efficacy of our proposed system by fully examining its security and privacy goals in terms of confidentiality, integrity, and availability. Our framework achieves desired security and privacy goals and is resilient against modification, DoS attacks, data mining and linkage attacks. Finally, we undertake a performance evaluation to demonstrate the proposed scheme's feasibility and efficiency.
Vehicular Ad-hoc Networks (VANETs) supporting the seamless operation of autonomous vehicles introduce various network-connected devices. The widespread devices are engaged in VANETs so that users can enjoy advantageous computing and reliable services. The combination brings in massive real-time message propagation and dissemination, which would be leveraged by the adversaries to perform data association, integration analysis and privacy mining. To address such challenges, existing authentication schemes use n pseudonym certificates for pre-defined k times and try to keep the vehicles anonymous. These schemes require fresh certificates for each authentication process, which cost more communication and storage resources. In this paper, we propose a novel privacy-preserving authentication protocol (P2BA) in bilinear groups, where a registered vehicle signs a traffic-related message and sends it to the nearby Road-side Unit (RSU) together with its blinded certificate. The RSU is able to independently check the message for validity based on a non-interactive zero-knowledge proof protocol. In this way, the computation time has been reduced fromO(n) toO(1) while the storage overhead fromO(nk) toO(n) compared to anonymous authentication protocols. Moreover, our scheme provides privacy properties such as anonymity and unlinkability. The simulations show that the message authentication can be processed by individual RSUs within 1 ms under the batch-enabled scheme, which outperforms the existing schemes in terms of computation overhead and latency.
We study the randomness complexity of interactive proofs and zero-knowledge proofs. In particular, we ask whether it is possible to reduce the randomness complexity, R, of the verifier to be comparable with the number of bits, C_V, that the verifier sends during the interaction. We show that such randomness sparsification is possible in several settings. Specifically, unconditional sparsification can be obtained in the non-uniform setting (where the verifier is modelled as a circuit), and in the uniform setting where the parties have access to a (reusable) common-random-string (CRS). We further show that constant-round uniform protocols can be sparsified without a CRS under a plausible worst-case complexity-theoretic assumption that was used previously in the context of derandomization. All the above sparsification results preserve statistical-zero knowledge provided that this property holds against a cheating verifier. We further show that randomness sparsification can be applied to honest-verifier statistical zero-knowledge (HVSZK) proofs at the expense of increasing the communication from the prover by R-F bits, or, in the case of honest-verifier perfect zero-knowledge (HVPZK) by slowing down the simulation by a factor of 2^{R-F}. Here F is a new measure of accessible bit complexity of an HVZK proof system that ranges from 0 to R, where a maximal grade of R is achieved when zero-knowledge holds against a "semi-malicious" verifier that maliciously selects its random tape and then plays honestly. Consequently, we show that some classical HVSZK proof systems, like the one for the complete Statistical-Distance problem (Sahai and Vadhan, JACM 2003) admit randomness sparsification with no penalty. Along the way we introduce new notions of pseudorandomness against interactive proof systems, and study their relations to existing notions of pseudorandomness.
Most of the existing sharing models adopt blockchain technology. With the increase of data volume and participating nodes, the storage performance and sharing security of data cannot be guaranteed, which is prone to data leakage. To solve this problem, we propose a double-link data storage and sharing model based on the alliance chain, which combines off-chain data storage and on-chain data sharing. The off-chain database uses the Hadoop Distributed File System (HDFS) distributed file system based on mean shift clustering to store data, which improves the storage and access rate of the data. The consensus mechanism combining Delegated Proof of Stake (DPOS)and Practical Byzantine Fault Tolerance (PBFT) with copy deletion scheme is adopted to realize the safe sharing of data. The proposed model has good efficiency, security, and accuracy, and can effectively improve the problems existing in smart city data security management.
Compared with traditional power systems, smart grid is designed to provide effective and secure energy services. Data aggregation is one of the key technologies in wireless sensor networks, which reduces the amount of data transmission between nodes by merging similar data and simplifying redundant data, thus significantly reducing the computation cost and communication overhead of the system. Many data aggregation schemes have been developed for the smart grid in the past years. However, most of the data aggregation schemes ignore the data security and privacy protection issues of the edge layer. To solve these problems, in this article, we propose an edge blockchain assisted lightweight privacy-preserving data aggregation for smart grid, named EBDA. In this work, we integrate edge computing and blockchain to design a three-layer architecture data aggregation scheme for smart grid. This new architecture supports a two-level data aggregation scheme, which is more efficient and secure. Through theoretical analysis and simulations, EBDA shows great superiority in terms of resisting network attacks, reducing system computation costs and communication overhead compared with existing schemes.
Access control is a technology that can guarantee the security of information in network transmission, in which role-based access control is to separate the subject from the permission, and to assign the permission by distributing the corresponding role of the user. However, the traditional role-based access control scheme is generally centralized, the allocation of user’s role lacks fine granularity, and there is static in the allocation of the role and permission, which is not consistent with the distributed and dynamic network architecture nowadays. Hence, we propose a dynamic and fine-grained role-based access control model DF-RBAC, which can realize the flexible assignment of roles by resource owners and security verification of assigned roles. Further, we take advantage of blockchain technology and cryptography technology to combine with the DF-RBAC framework to achieve access to the activity log security audit function, which can ensure the security of the overall architecture. By safety and experimental analysis, our framework has proved to be feasible.
The Bitcoin blockchain was the first publicly verifiable, and distributed ledger, where it is possible for everyone to download and check the full history of all data records from the genesis block. These properties lead to the emergence of new types of applications and the redesign of traditional systems that no longer respond to current business needs (e.g., transparency, protection against censorship, decentralization). One particular application is the use of blockchain technology to enable decentralized and self-sovereign identities including new mechanisms for creating, resolving, and revoking them. The public availability of data records has, in turn, paved the way for new kinds of attacks that combine sophisticated heuristics with auxiliary information to compromise users’ privacy and deanonymize their identities. In this paper, we review and categorize Bitcoin privacy attacks, investigate their impact on one of the Bitcoin-based identity methods namely did:btcr, and analyze and discuss its privacy properties.
We introduce and study a simple kind of proof system called line-point zero knowledge (LPZK). In an LPZK proof, the prover encodes the witness as an affine line 𝐯(t) : = at + 𝐛 in a vector space 𝔽ⁿ, and the verifier queries the line at a single random point t = α. LPZK is motivated by recent practical protocols for vector oblivious linear evaluation (VOLE), which can be used to compile LPZK proof systems into lightweight designated-verifier NIZK protocols. We construct LPZK systems for proving satisfiability of arithmetic circuits with attractive efficiency features. These give rise to designated-verifier NIZK protocols that require only 2-5 times the computation of evaluating the circuit in the clear (following an input-independent preprocessing phase), and where the prover communicates roughly 2 field elements per multiplication gate, or roughly 1 element in the random oracle model with a modestly higher computation cost. On the theoretical side, our LPZK systems give rise to the first linear interactive proofs (Bitansky et al., TCC 2013) that are zero knowledge against a malicious verifier. We then apply LPZK towards simplifying and improving recent constructions of reusable non-interactive secure computation (NISC) from VOLE (Chase et al., Crypto 2019). As an application, we give concretely efficient and reusable NISC protocols over VOLE for bounded inner product, where the sender’s input vector should have a bounded L₂-norm.
Blockchain, the foundation of Ethereum, has received greater attention on recent years as a decentralised and distributed public ledger technology that offers different properties like security, privacy, scalability and immutability during online data transactions, reduces cost of transactions, and enables verification and efficient exchange of personnel information across many computers without any trusted parties. The goal of this survey is to propel some methods in preserving user privacy during data transactions using Ethereum smart contract. We analysed the privacy challenges in blockchain and figured out the existing cryptographic mechanisms in preserving the transaction privacy. We also addressed some technique for simplifying claim settlement process in healthcare insurance. By using Ethereum blockchain via smart contract, current challenges in insurance such as being time-consuming, can be avoided thereby preventing insurance frauds and thus improving transparency to all members in-network. It also helps to avoid paper-based contracts, which are prone to errors, and removes the intermediaries. Furthermore, there is still a need to examine and consider future research in order to overcome privacy attacks when using blockchain.
Most IoT devices cannot afford to be a blockchain node due to the high computation and storage loads. Thus, the blockchain is usually deployed on one delegate node, e.g., the edge device or cloud, which may encounters three drawbacks: (1) The delegate node becomes the single failure point when the number of delegate notes are limited. (2) The delegate node replicating the blockchain data can lead to privacy information leak. (3) The delegate node is vulnerable to the Distributed Denial of Service (DDoS) attack. To tackle these drawbacks, we consider to minimize the redundant of blockchain to make the IoT devices as the specialized blockchain nodes. In this paper, we integrate a permissioned blockchain (HLF), an attribute-based access control (ABAC) and an identity-based signature (IBS) to build a security, lightweight, and cross-domain blockchain-based IoT access control system. Specifically, we divided the IoT system into different function domains, named IoT domains. Then, we establish a local blockchain ledger for each IoT domain to enable more IoT devices as blockchain nodes. The local blockchain ledger records the IoT domain entities' attributes, policy files' digests, and access decisions. Meanwhile, we use the channel technology of HLF to realize cross-domain access and use the IBS to filter the legal access requests for each IoT domain to prevent DDoS attacks. We also design a policy decision point (PDP) selection algorithm that select multiple IoT devices (blockchain nodes) to achieve the real-time distributed policy decisions (off-chain). Finally, we implement and evaluate the proposed system to demonstrate its practicality.
Xianjia Yu, Jorge Peña Queralta, Jukka Heikkonen, Tomi Westerlund
Autonomous systems are becoming inherently ubiquitous with the advancements of computing and communication solutions enabling low-latency offloading and real-time collaboration of distributed devices. Decentralized technologies with blockchain and distributed ledger technologies (DLTs) are playing a key role. At the same time, advances in deep learning (DL) have significantly raised the degree of autonomy and level of intelligence of robotic and autonomous systems. While these technological revolutions were taking place, raising concerns in terms of data security and end-user privacy has become an inescapable research consideration. Federated learning (FL) is a promising solution to privacy-preserving DL at the edge, with an inherently distributed nature by learning on isolated data islands and communicating only model updates. However, FL by itself does not provide the levels of security and robustness required by today’s standards in distributed autonomous systems. This survey covers applications of FL to autonomous robots, analyzes the role of DLT and FL for these systems, and introduces the key background concepts and considerations in current research.
The development of artificial intelligence and worldwide epidemic events has promoted the implementation of smart healthcare while bringing issues of data privacy, malicious attack, and service quality. The Medical Internet of Things (MIoT), along with the technologies of federated learning and blockchain, has become a feasible solution for these issues. In this paper, we present a blockchain-based federated learning method for smart healthcare in which the edge nodes maintain the blockchain to resist a single point of failure and MIoT devices implement the federated learning to make full of the distributed clinical data. In particular, we design an adaptive differential privacy algorithm to protect data privacy and gradient verification-based consensus protocol to detect poisoning attacks. We compare our method with two similar methods on a real-world diabetes dataset. Promising experimental results show that our method can achieve high model accuracy in acceptable running time while also showing good performance in reducing the privacy budget consumption and resisting poisoning attacks.