The amount of information generated grows as more and more sensor and IoT devices are deployed in smart cities. It is of utmost importance for us to consider the privacy data leakage and compromised identity from both outside adversaries and inside abuse of data access privilege. The security assumption of the system should not solely rely on the fact that permission and access control were being implemented correctly. Quite the contrary, a system can be designed in a way that user's identity data and usage traces are not leaked even if the system had been compromised. Based upon our previous on-street parking system utilizing Bluetooth Low Energy (BLE) beacons, we applied a cryptographic primitive called zero-knowledge proof to our authentication system. A commitment scheme and Merkle tree is combined in the setup to achieve zero-knowledge set membership proof. Doing so, the user is anonymous to the server between authentication sessions, while the server's still able to verify the legitimacy of such user. The on-street parking system is therefore immune to privacy data leakage, as for now one cannot mass-query and profile certain user's traces within the system.
In recent years, blockchain has triggered an upsurge in the application of decentralized models and has received more and more attention. For convenience and security considerations, in blockchain applications, users usually use wallets to manage digital assets. The most important data stored in the wallet is the user’s private key, which is also the only identification of the ownership of the encrypted digital assets. Once the private key is lost or stolen, it will bring irreparable losses. We proposed a consortium blockchain wallet scheme based on dual-threshold key protection secret-sharing. By splitting and storing the user’s wallet private key using a secret-sharing method, we can protect our private keys safely and effectively. Our scheme is based on the application scenario of the consortium blockchain. The peers preset by the consortium blockchain store the user’s wallet private key shadow shares, reasonably integrate storage resources, and enhance the solution’s anti-attack ability by setting double thresholds.
Runze Cheng, Yao Sun, Yi‐Jing Liu, Le Xia · 6 authors
Cache-enabled device-to-device (D2D) communication is a potential approach to tackle the resource shortage problem. However, public concerns of data privacy and system security still remain, which thus arises an urgent need for a reliable caching scheme. Fortunately, federated learning (FL) with a distributed paradigm provides an effective way to privacy issue by training a high-quality global model without any raw data exchanges. Besides the privacy issue, blockchain can be further introduced into the FL framework to resist the malicious attacks occurred in D2D caching networks. In this study, we propose a double-layer blockchain-based deep reinforcement FL (BDRFL) scheme to ensure privacy-preserved and caching-efficient D2D networks. In BDRFL, a double-layer blockchain is utilized to further enhance data security. Simulation results first verify the convergence of the BDRFL-based algorithm, and then demonstrate that the download latency of the BDRFL-based caching scheme can be significantly reduced under different types of attacks when compared to some existing caching policies.
Yueqiang Xu, Heli Zhang, Hong Ji, Lichao Yang · 6 authors
The integration of blockchain and mobile edge computing (MEC), as a secure, efficient, and reliable edge computing paradigm, has been widely applied in many applications, such as large-scale Internet of Things (IoT), Internet of Vehicles (IoV), and smart grid. However, due to the restricted transaction throughput of blockchain, the combination of blockchain and MEC in most existing works cannot support applications with frequent transaction requirements. In this paper, we propose an integrated blockchain and MEC (IBM) framework based on a space-structured ledger to meet the transaction demands for IoT applications. In the framework, a collaborative mining process is designed, where we consider the cooperation between mobile devices (MDs) and MEC servers. To promote mining efficiency, we further develop a high-performance consensus mechanism called reputation-based proof of work (Re-PoW), in which differentiated mining targets are assigned according to the reputation of MDs. In the Re-PoW consensus mechanism, heterogeneous capabilities and historical behaviors of MDs are all considered for accurately evaluating their reputation. In addition, we present an alternating optimization algorithm by jointly optimizing bandwidth allocation and computation resource allocation to further enhance the performance of the proposed scheme. Simulation results show that the proposed approach can achieve significant throughput improvement.
Zhenquan Qin, Ye Jin, Jie Meng, Bingxian Lu · 5 authors
The marine Internet of things (MIoT) is the application of the Internet of things technology in the marine field. Nowadays, with the arrival of the era of big data, the MIoT architecture has been transformed from cloud computing architecture to edge computing architecture. However, due to the lack of trust among edge computing participants, new solutions with higher security need to be proposed. In the current solutions, some use blockchain technology to solve data security problems while some use federated learning technology to solve privacy problems, but these methods neither combine with the special environment of the ocean nor consider the security of task publishers. In this article, we propose a secure sharing method of MIoT data under an edge computing framework based on federated learning and blockchain technology. Combining its special distributed architecture with the MIoT edge computing architecture, federated learning ensures the privacy of nodes. The blockchain serves as a decentralized way, which stores federated learning workers to achieve nontampering and security. We propose a concept of quality and reputation as the metrics of selection for federated learning workers. Meanwhile, we design a quality proof mechanism [proof of quality (PoQ)] and apply it to the blockchain, making the edge nodes recorded in the blockchain more high-quality. In addition, a marine environment model is built in this article, and the analysis based on this model makes the method proposed in this article more applicable to the marine environment. The numerical results obtained from the simulation experiments clearly show that the proposed scheme can significantly improve the learning accuracy under the premise of ensuring the safety and reliability of the marine environment.
Policy-based chameleon hash (PCH) is one of the techniques used for rewriting transaction-level data stored in blockchains. This technique integrates the access policy of the attribute-based encryption (ABE) in the transactions and only allows users with attributes set satisfying the access policy to modify the transactions. However, some operations in the PCH-based rewritable blockchain solution require high computational cost which may impact the performance of user systems, especially on resource-constrained devices. To solve this problem, we propose an online/offline rewritable blockchain with auditable outsourced computation (OO-RB-AOC) scheme. We utilize the ring signature to ensure the credibility of multiple attribute authorities, and adopt the online/offline technique for generating the hash of the rewritable transaction. In addition, expensive computations (e.g., pairings) required for rewriting the transactions can be outsourced to the clouds. The users can rest assured that the computations from the clouds are correct with the audit mechanism of the proposed scheme. On the other hand, the proposed scheme offers a desirable feature for commercial application where the clouds can limit the number of outsourced requests according to the subscription of the users. We also prove the security of the proposed OO-RB-AOC scheme. Finally, we present a theoretical comparison and experimental analysis of the proposed scheme.
Cybertwin-driven-based network architecture for sixth generation (6G) is a new cloud-centric network architecture, which was put forward to address challenges of 6G, such as scalability, security, mobility, and availability. As more and more users’ data are obtained and converted into other digital asset by the cybertwin, relevant techniques of data management to enhance privacy and security becomes a key challenge for Cybertwin-driven 6G. The double authentication preventing ring signature (DAPRS) is a cryptographic primitive that meets requirements of authenticity, anonymity of users’ private data, and linkability of users’ misbehavior, so it is suitable for monitoring behaviors of risky users. In this article, we propose a post-quantum secure ring signature to enhance security and privacy in Cybertwin-driven 6G (PRSG). In the PRSG, we first put forward an accumulator based on a chameleon hash function that allows to efficiently prove knowledge of an accumulated value. Then, we construct a DAPRS based on the accumulator and the zero-knowledge argument of knowledge. Finally, we present how to use the DAPRS to build a secure and efficient privacy-preserving scheme in the Cybertwin-driven 6G. Security proof shows that this scheme achieves anonymity, unforgeability, as well as double signature extractability. Also, we make some security analysis and performance evaluation to demonstrate that the PRSG has low communication complexity, high performance, and privacy preservation in the cybertwin-driven 6G.
Liang Tan, Keping Yu, Na Shi, Caixia Yang · 6 authors
COVID-19 is currently a major global public health challenge. In the battle against the outbreak of COVID-19, how to manage and share the COVID-19 Electric Medical Records (CEMRs) safely and effectively in the world, prevent malicious users from tampering with CEMRs, and protect the privacy of patients are very worthy of attention. In particular, the semi-trusted medical cloud platform has become the primary means of hospital medical data management and information services. Security and privacy issues in the medical cloud platform are more prominent and should be addressed with priority. To address these issues, on the basis of ciphertext policy attribute-based encryption, we propose a blockchain-empowered security and privacy protection scheme with traceable and direct revocation for COVID-19 medical records. In this scheme, we perform the blockchain for uniform identity authentication and all public keys, revocation lists, etc are stored on a blockchain. The system manager server is responsible for generating the system parameters and publishes the private keys for the COVID-19 medical practitioners and users. The cloud service provider (CSP) stores the CEMRs and generates the intermediate decryption parameters using policy matching. The user can calculate the decryption key if the user has private keys and intermediate decrypt parameters. Only when attributes are satisfied access policy and the user's identity is out of the revocation list, the user can get the intermediate parameters by CSP. The malicious users may track according to the tracking list and can be directly revoked. The security analysis demonstrates that the proposed scheme is indicated to be safe under the Decision Bilinear Diffie-Hellman (DBDH) assumption and can resist many attacks. The simulation experiment demonstrates that the communication and storage overhead is less than other schemes in the public-private key generation, CEMRs encryption, and decryption stages. Besides, we also verify that the proposed scheme works well in the blockchain in terms of both throughput and delay.
Fariba Ghaffari, Komal Gilani, E. Bertin, Noël Crespi
Summary As the basic building block of any information security system, identity and access management (IAM) solutions play vital role in enterprise's security programmes. Providing centric solutions for IAM is inefficient in terms of having single point of failure, high cost, duplication and complexity to the users. Recently, emerging the distributed ledger technology (DLT) has attracted significant scientific interests in research areas like identity management, authentication and access control processes. In these contexts, Blockchain can offer greater data and rule confidentiality and integrity, as well as increasing the availability of the system by removing the single point of failure in the procedure. In this paper, we provide a comprehensive overview of the IAM solutions based on their basic components including identity management, authentication and access control. In the identity concept, we discuss about self‐sovereign identity which enhances privacy and security of distributed digital identities by providing individual's consolidated digital identity and verified attributes for enabling them to utilize their ownership. To offer a clearer understanding of the state of the art, we propose taxonomy to categorize them based on their features. For the conclusion of the paper, we compare the existing methods based on proposed taxonomy. Also, considering the advantages and disadvantages of existing methods, we discussed about the possible future directions.
The emergence of Intelligent Connected Vehicles (ICVs) shows great potential for future intelligent traffic systems, enhancing both traffic safety and road efficiency. However, the ICVs relying on data driven perception and driving models face many challenges, including the lack of comprehensive knowledge to deal with complicated driving context. In this paper, we are motivated to investigate cooperative knowledge sharing for ICVs. We propose a secure and efficient directed acyclic graph (DAG) blockchain based knowledge sharing framework, aiming to cater for the micro-transaction based vehicular networks. The framework can realize both local and cross-regional knowledge sharing. Then, the framework is applied to autonomous driving applications, wherein machine learning based models for autonomous driving control can be shared. A lightweight tip selection algorithm (TSA) is proposed for the DAG based knowledge sharing framework to achieve consensus and identity verification for cross-regional vehicles. To enhance model accuracy as well as minimizing bandwidth consumption, an adaptive asynchronous distributed learning (ADL) based scheme is proposed for model uploading and downloading. Experiment results show that the blockchain based knowledge sharing is secure, and it can resist attacks from malicious users. In addition, the proposed adaptive ADL scheme can enhance driving safety related performance compared to several existing algorithms.
Abstract Blockchain has decentralization characteristics and requires more targeted security schemes to protect user privacy. In contrast, existing signature schemes have many high‐complexity operations and impose an enormous computational burden on wireless nodes. This article proposes a light‐weighted identity privacy scheme for blockchain‐based on edge computing. We construct linkable identity privacy and non‐linkable identity privacy, which can resist collusion attacks while virtually guaranteeing blockchain nodes' identity privacy. Since edge computing offloads heavily, the proposed scheme has lower computational complexity than the existing techniques.
Qiliang Yang, Mingrui Zhang, Yanwei Zhou, Tao Wang · 6 authors
As an important method of protecting data confidentiality in the Internet of Things (IoT), access control has been widely concerned. Because attribute-based access control mechanisms are dynamic, it is not only suitable to solve the dynamic access problem in IoT, but also to deal with the dynamic caused by node movement and access data change. The traditional centralized attribute-based access control mechanism has some problems: due to the large number of devices in IoT, the central trusted entity may become the bottleneck of the whole system. Moreover, when a central trusted entity is under distributed denial-of-service (DDoS) attack, the entire system may crash. Blockchain is a good way to solve the above problems. Therefore, we developed a non-interactive, attribute-based access control scheme that applies blockchain technology in IoT scenarios by using PSI technology. In addition, the attributes of data user and data holder are hidden, which protects the privacy of both parties’ attributes and access policy. Furthermore, the experimental results indicate that our scheme has high efficiency.
Smart contracts can be used for the fair exchange of digital goods. A smart contract can escrow the exchange where the receiver deposits the payment, and the sender claims it by providing the goods. In the case of misbehavior, the parties provide proof on whether the received goods match the pre-agreed description or not. In general, the description is assumed to be the hash of the goods, and it is publicly known. However, without trusting the description provided by the sender, this assumption is not plausible for the scenarios where the goods are uniquely created for a specific receiver. To overcome the trust issue, sampling-based exchange protocols have been introduced where the parties use a sample of the goods as the description. Yet, the existing sampling-based proposals suffer from high on- and off-chain computational and storage costs. In this paper, we present FairDEx: an efficient sampling- based protocol that is suitable for the exchange of unique goods. Our description protocol allows us to achieve low on- and off-chain costs, which are independent of the size of the goods. The off-chain part of the protocol only utilizes highly efficient algorithms, namely hashing and symmetric key encryption. To illustrate the feasibility of FairDEx, we evaluate a research prototype on the Ethereum test network. Our results show that the cost of running FairDEx is around 0.6M gas for reasonably large sample sets, which is 30% cheaper than the state-of-the-art Ethereum-based proposals.
Deep learning has achieved the high-accuracy of state-of-the-art algorithms in long-standing AI tasks. Due to the obvious privacy issues of deep learning, Google proposes Federal Deep Learning (FDL), in which distributed participants only upload local gradients and and a centralized server updates parameters based on the collected gradients. But few users are willing to participate in federated learning due to the lack of contribution evaluation and reward mechanisms. So a decentralized federated deep learning, called DFDL, has been proposed by introducing blockchain to form an effective incentive mechanism for participants. However, DFDL still faces serious privacy issues as blockchain does not guarantee the privacy of training data and model. In this paper, in order to address the aforementioned issues, we propose a new Privacy-preserving DFDL scheme, called PDFDL. With PDFDL, parties can securely learn a global model with their local gradients in the assistance of blockchain, and the parties’ sensitive data and the global model are well protected. Specifically, with a secure multi-party aggregation computing, all local gradients are encrypted by their owners before being sent to the smart contract, and can be directly aggregated without decryption. Detailed security analysis shows that PDFDL can resist various known security threats. Moreover, we give an implementation prototype by integrating deep learning module with a Blockchain development platform (Ethereum V1.6.4). We demonstrate the encryption performance and the training accuracy of our PDFDL on benchmark datasets.
A wider coverage and a better solution to latency reduction in 5G necessitates its combination with mobile edge computing (MEC) technology. Decentralized deep learning (DDL) as a promising solution to privacy-preserving data processing for millions of edge smart devices, it leverages federated learning within the networking of local models, without disclosing a client's raw data. Especially, in industries such as finance and healthcare where sensitive data of transactions and personal medical records is cautiously maintained, DDL facilitates the collaboration among these institutes to improve the performance of local models, while protecting data privacy of participating clients. In this survey paper, we demonstrate technical fundamentals of DDL for benefiting many walks of society through decentralized learning. Furthermore, we offer a comprehensive overview of recent challenges of DDL and the most relevant solutions from novel perspectives of communication efficiency and trustworthiness.
Wider coverage and a better solution to a latency reduction in 5G necessitate its combination with multi-access edge computing (MEC) technology. Decentralized deep learning (DDL) such as federated learning and swarm learning as a promising solution to privacy-preserving data processing for millions of smart edge devices, leverages distributed computing of multi-layer neural networks within the networking of local clients, whereas, without disclosing the original local training data. Notably, in industries such as finance and healthcare where sensitive data of transactions and personal medical records is cautiously maintained, DDL can facilitate the collaboration among these institutes to improve the performance of trained models while protecting the data privacy of participating clients. In this survey paper, we demonstrate the technical fundamentals of DDL that benefit many walks of society through decentralized learning. Furthermore, we offer a comprehensive overview of the current state-of-the-art in the field by outlining the challenges of DDL and the most relevant solutions from novel perspectives of communication efficiency and trustworthiness.
Aisha Zahid Junejo, Manzoor Ahmed Hashmani, Mehak Maqbool Memon
With the widespread of blockchain technology, preserving the anonymity and confidentiality of transactions have become crucial. An enormous portion of blockchain research is dedicated to the design and development of privacy protocols but not much has been achieved for proper assessment of these solutions. To mitigate the gap, we have first comprehensively classified the existing solutions based on blockchain fundamental building blocks (i.e., smart contracts, cryptography, and hashing). Next, we investigated the evaluation criteria used for validating these techniques. The findings depict that the majority of privacy solutions are validated based on computing resources i.e., memory, time, storage, throughput, etc., only, which is not sufficient. Hence, we have additionally identified and presented various other factors that strengthen or weaken blockchain privacy. Based on those factors, we have formulated an evaluation framework to analyze the efficiency of blockchain privacy solutions. Further, we have introduced a concept of privacy precision that is a quantifiable measure to empirically assess privacy efficiency in blockchains. The calculation of privacy precision will be based on the effectiveness and strength of various privacy protecting attributes of a solution and the associated risks. Finally, we conclude the paper with some open research challenges and future directions. Our study can serve as a benchmark for empirical assessment of blockchain privacy.
Internet of Vehicles (IoV) has been sought as a solution to realize an Intelligent Transportation System (ITS) for efficient traffic management. Data driven ITS requires learning from vehicular data and provide vehicles with timely information to support a wide range of safety and infotainment ITS applications. IoV is vulnerable to multitude of cyber-attacks and privacy concerns. Federated learning (FL) is on the verge of delivering the collaborative learning by exchanging learning model parameters instead of actual data, which is expected to provide privacy in IoV. However, despite featuring an inherently secure and privacy-preserving framework, FL is still vulnerable to poisoning and reverse engineering attacks. Blockchain technology (BC) has already demonstrated a zero-trust, fully secure, distributed, and auditable information recording and sharing paradigm. In this article, we present a practical prospect of blockchain empowered federated learning to realize fully secure, privacy preserving, and verifiable FL for the IoV that is capable of providing secure and trustworthy ITS services.
Emerging multitenant cloud computing ecosystems allow multiple applications to share virtualized pool of computing and networking resources. As a result, such ecosystems are becoming increasingly prone to data privacy concerns (personal data leakages and unauthorized access). While cloud computing providers support robust security and privacy mechanisms (e.g., public key cryptography, firewalls, and virtual private networks, among many others), they lack mechanisms and frameworks to monitor, audit, and verify these data privacy concerns. The emergence of data protection regulations around the world, such as General Data Protection Regulation in Europe and the Data Protection Act in the U.K., further emphasizes the need to overcome these privacy limitations. In this article, a novel technique for monitoring, auditing, and verifying the operations carried out on a user’s personal data in cloud computing ecosystems is proposed. Our research methodology leverages distributed ledger technologies (e.g., blockchain and smart contracts) for developing an immutable recording technique, which transparently logs, monitors, and verifies the operations carried out on user data. Using a healthcare pharmacy scenario and extensive real-world experiments, we validate the feasibility of the proposed technique. The proposed work handles a large pool of requests ($>$13K) ensuring minimal latency ($\approx$50–60 ms) and overheads for three different service packages varied with respect to the number of actors and operations.
Alexandre San Pedro Siqueira, Arlindo Flávio da Conceição, Vladimir Rocha
This article presents the potential use of the Self-Sovereign Identities (SSI), combining with Distributed Ledger Technologies (DLT), to improve the privacy and control of health data. The paper presents the SSI technology, lists the prominent use cases of decentralized identities in the health area, and discusses an effective blockchain-based architecture. The main contributions of the article are: (i) mapping SSI general and abstract concepts, e.g., issuers and holders, to the health domain concepts, e.g., physicians and patients; (ii) creating a correspondence between the SSI interactions, e.g., issue and verify a credential, and the US standardized set of health use cases; (iii) presenting and instantiating an architecture to deal with the use cases mentioned, effectively organizing the data in a user-centric way, that uses well-known SSI and Blockchain technologies.
In 2019, the National Archives of Korea (NAK) developed a blockchain recordkeeping platform to conduct R&D on recordkeeping approaches. This paper introduces two types of R&D studies that have been conducted thus far. The first is the use of blockchain transaction audit trail technology to ensure the authenticity of audiovisual archives, i.e., the application of blockchain to a new system. The second uses blockchain technology to verify whether the datasets of numerous information systems built by government agencies are managed without forgery or tampering, i.e., the application of blockchain to an existing system. Government work environments globally are rapidly shifting from paper records to digital. However, the traditional recordkeeping methodology has not adequately kept up with these digital changes. Despite the importance of responding to digital changes by incorporating innovative technologies such as blockchain in recordkeeping practices, it is not easy for most archives to invest funds in experiments on future technologies. Owing to the Korean government’s policy of investing in digital transformation, NAK’s blockchain recordkeeping platform has been developed, and several R&D tasks are underway. Hopefully, the findings of this study will be shared with archivists around the world who are focusing on the future of recordkeeping.