Motivated by the increasingly powerful computing capabilities of end-user equipment, and by the growing privacy concerns over sharing sensitive raw data, a distributed machine learning paradigm known as federated learning (FL) has emerged. By training models locally at each client and aggregating learning models at a central server, FL has the capability to avoid sharing data directly, thereby reducing privacy leakage. However, the conventional FL framework relies heavily on a single central server, and it may fail if such a server behaves maliciously. To address this single point of failure, in this work, a blockchain-assisted decentralized FL framework is investigated, which can prevent malicious clients from poisoning the learning process, and thus provides a self-motivated and reliable learning environment for clients. In this framework, the model aggregation process is fully decentralized and the tasks of training for FL and mining for blockchain are integrated into each participant. Privacy and resource-allocation issues are further investigated in the proposed framework, and a critical and unique issue inherent in the proposed framework is disclosed. In particular, a lazy client can simply duplicate models shared by other clients to reap benefits without contributing its resources to FL. To address these issues, analytical and experimental results are provided to shed light on possible solutions, i.e., adding noise to achieve local differential privacy and using pseudo-noise (PN) sequences as watermarks to detect lazy clients.
Iñigo Querejeta-Azurmendi, David Arroyo, Jorge López Hernández-Ardieta, Luis Hernández Encinas
This paper proposes NetVote, an internet voting protocol where usability and ease in deployment are a priority. We introduce the notion of strict coercion resistance, to distinguish between vote-buying and coercion resistance. We propose a protocol with ballot secrecy, practical everlasting privacy, verifiability and strict coercion resistance in the re-voting setting. Coercion is mitigated via a random dummy vote padding strategy to hide voting patterns and make re-voting deniable. This allows us to build a filtering phase with linear complexity, based on zero knowledge proofs to ensure correctness while maintaining privacy of the process. Voting tokens are formed by anonymous credentials and pseudorandom identifiers, achieving practical everlasting privacy, where even if dealing with a future computationally unbounded adversary, vote intention is still hidden. It is not assumed for voters to own cryptographic keys prior to the election, nor store cryptographic material during the election. This property allows voters not only to vote multiple times, but also from different devices each time, granting the voter a vote-from-anywhere experience. This paper builds on top of the paper published in CISIS’19. In this version, we modify the filtering. Moreover, we formally define the padding technique, which allows us to perform the linear filtering scheme. Similarly we provide more details on the protocol itself and include a section of the security analysis, where we include the formal definitions of strict coercion resistance and a game based definition of practical everlasting privacy. Finally, we prove that NetVote satisfies them all.
We discuss the challenge of achieving an auditable key management for cryptographic access control to high-value sensitive data. In such settings it is important to be able to audit the key management process - and in particular to be able to provide verifiable proofs of key generation. The auditable key management has several possible use cases in both civilian and military world. In particular, the new regulations for protection of sensitive personal data, such as GDPR, introduce strict requirements for handling of personal data and apply a very restrictive definition of what can be considered a personal data. Cryptographic access control for personal data has a potential to become extremely important for preserving industrial ability to innovate, while protecting subject’s privacy, especially in the context of widely deployed modern monitoring, tracking and profiling capabilities, that are used by both governmental institutions and high-tech companies. However, in general, an encrypted data is still considered as personal under GDPR and therefore cannot be, e.g., stored or processed in a public cloud or distributed ledger. In our work we propose an identity-based cryptographic framework that ensures confidentiality, availability, integrity of data while potentially remaining compliant with the GDPR framework.
The insider threats have always been one of the most severe challenges to cybersecurity. It can lead to the destruction of the organisation's internal network system and information leakage, which seriously threaten the confidentiality, integrity and availability of data. To make matters worse, since the attacker has authorized access to the internal network, they can launch the attack from the inside and erase their attack trace, which makes it challenging to track and forensics. A blockchain traceability system for insider threats is proposed in this paper to mitigate the issue. First, this paper constructs an insider threat model of the internal network from a different perspective: insider attack forensics and prevent insider attacker from escaping. Then, we analyze why it is difficult to track attackers and obtain evidence when an insider threat has occurred. After that, the blockchain traceability system is designed in terms of data structure, transaction structure, block structure, consensus algorithm, data storage algorithm, and query algorithm, while using differential privacy to protect user privacy. We deployed this blockchain traceability system and conducted experiments, and the results show that it can achieve the goal of mitigating insider threats.
As a next-generation power system, the smart grid can implement fine-grained smart metering data collection to optimize energy utilization. Smart meters face serious security challenges, such as a trusted third party or a trusted authority being attacked, which leads to the disclosure of user privacy. Blockchain provides a viable solution that can use its key technologies to solve this problem. Blockchain is a new type of decentralized protocol that does not require a trusted third party or a central authority. Therefore, this paper proposes a decentralized privacy-preserving data aggregation (DPPDA) scheme for smart grid based on blockchain. In this scheme, the leader election algorithm is used to select a smart meter in the residential area as a mining node to build a block. The node adopts Paillier cryptosystem algorithm to aggregate the user's power consumption data. Boneh-Lynn-Shacham short signature and SHA-256 function are applied to ensure the confidentiality and integrity of user data, which is convenient for billing and power regulation. The scheme protects user privacy data while achieving decentralization, without relying on TTP or CA. Security analysis shows that our scheme meets the security and privacy requirements of smart grid data aggregation. The experimental results show that this scheme is more efficient than existing competing schemes in terms of computation and communication overhead.
Xi Lin, Jun Wu, Ali Kashif Bashir, Jianhua Li · 6 authors
Recently, edge artificial intelligence techniques (e.g., federated edge learning) are emerged to unleash the potential of big data from Internet of Things (IoT). By learning knowledge on local devices, data privacy preserving and Quality of Service (QoS) are guaranteed. Nevertheless, the dilemma between the limited on-device battery capacities and the high energy demands in learning is not resolved. When the on-device battery is exhausted, the edge learning process will have to be interrupted. In this article, we propose a novel wirelessly powered edge intelligence (WPEG) framework, which aims to achieve a stable, robust, and sustainable edge intelligence by energy harvesting (EH) methods. First, we build a permissioned edge blockchain to secure the peer-to-peer (P2P) energy and knowledge sharing in our framework. To maximize edge intelligence efficiency, we then investigate the wirelessly powered multiagent edge learning model and design the optimal edge learning strategy. Moreover, by constructing a two-stage Stackelberg game, the underlying energy-knowledge trading incentive mechanisms are also proposed with the optimal economic incentives and power transmission strategies. Finally, simulation results show that our incentive strategies could optimize the utilities of both parties compared with classic schemes, and our optimal learning design could realize the optimal learning efficiency.
Ilhaam A. Omar, Raja Jayaraman, Khaled Salah, Mecit Can Emre Simsekler · 6 authors
BACKGROUND: Clinical Trials (CTs) help in testing and validating the safety and efficacy of newly discovered drugs on specific patient population cohorts. However, these trials usually experience many challenges, such as extensive time frames, high financial cost, regulatory and administrative barriers, and insufficient workforce. In addition, CTs face several data management challenges pertaining to protocol compliance, patient enrollment, transparency, traceability, data integrity, and selective reporting. Blockchain can potentially address such challenges because of its intrinsic features and properties. Although existing literature broadly discusses the applicability of blockchain-based solutions for CTs, only a few studies present their working proof-of-concept. METHODS: We propose a blockchain-based framework for CT data management, using Ethereum smart contracts, which employs IPFS as the file storage system to automate processes and information exchange among CT stakeholders. CT documents stored in the IPFS are difficult to tamper with as they are given unique cryptographic hashes. We present algorithms that capture various stages of CT data management. We develop the Ethereum smart contract using Remix IDE that is validated under different scenarios. RESULTS: The proposed framework results are advantageous to all stakeholders ensuring transparency, data integrity, and protocol compliance. Although the proposed solution is tested on the Ethereum blockchain platform, it can be deployed in private blockchain networks using their native smart contract technologies. We make our smart contract code publicly available on Github. CONCLUSIONS: We conclude that the proposed framework can be highly effective in ensuring that the trial abides by the protocol and the functions are executed only by the stakeholders who are given permission. It also assures data integrity and promotes transparency and traceability of information among stakeholders.
Efficient and reliable access control in smart cities is critical for the protection of various resources for decision making and task execution. Existing centralized access control schemes suffer from the limitations of single point of failure, low reliability, and poor scalability. This article, therefore, proposes a distributed and reliable access control framework for smart cities by combining the blockchain smart contract technology and the attribute-based access control (ABAC) model. The framework consists of one policy management contract (PMC) for managing the ABAC policies, one subject attribute management contract (SAMC) for managing the attributes of subjects (i.e., entities accessing resources), one object attribute management contract (OAMC) for managing the attributes of objects (i.e., resources being accessed), and one access control contract (ACC) for performing the access control. To show the feasibility of the proposed framework, we construct a local private Ethereum blockchain system to implement the four smart contracts and also conduct experiments to evaluate the monetary cost as well as to compare the proposed framework with an existing access control list (ACL)-based scheme. The experimental results show that although the proposed scheme consumes more money than the ACL-based scheme at the deployment stage, it introduces less monetary cost during the system running especially for large-scale smart cities.
Device failure detection is one of most essential problems in Industrial Internet of Things (IIoT). However, in conventional IIoT device failure detection, client devices need to upload raw data to the central server for model training, which might lead to disclosure of sensitive business data. Therefore, in this article, to ensure client data privacy, we propose a blockchain-based federated learning approach for device failure detection in IIoT. First, we present a platform architecture of blockchain-based federated learning systems for failure detection in IIoT, which enables verifiable integrity of client data. In the architecture, each client periodically creates a Merkle tree in which each leaf node represents a client data record, and stores the tree root on a blockchain. Furthermore, to address the data heterogeneity issue in IIoT failure detection, we propose a novel centroid distance weighted federated averaging (CDW_FedAvg) algorithm taking into account the distance between positive class and negative class of each client data set. In addition, to motivate clients to participate in federated learning, a smart contact-based incentive mechanism is designed depending on the size and the centroid distance of client data used in local model training. A prototype of the proposed architecture is implemented with our industry partner, and evaluated in terms of feasibility, accuracy, and performance. The results show that the approach is feasible, and has satisfactory accuracy and performance.
The aim of this paper is to understand whether Distributed Ledger Technologies (DLTs) are ready to support complex services, such as those related to Intelligent Transportation Systems (ITS). In smart transportation services, a huge amount of sensed data is generated by a multitude of vehicles. While DLTs provide very interesting features, such as immutability, traceability and verifiability of data, some doubts on the scalability and responsiveness of these technologies appear to be well-founded. We propose an architecture for ITS that resorts to DLT features. Moreover, we provide experimental results of a real test-bed over IOTA, a promising DLT for IoT. Results clearly show that, while the viability of the proposal cannot be rejected, further work is needed on the responsiveness of DLT infrastructures.
N. Deepa, Quoc‐Viet Pham, Dinh C. Nguyen, Sweta Bhattacharya · 9 authors
Big data has generated strong interest in various scientific and engineering domains over the last few years. Despite many advantages and applications, there are many challenges in big data to be tackled for better quality of service, e.g., big data analytics, big data management, and big data privacy and security. Blockchain with its decentralization and security nature has the great potential to improve big data services and applications. In this article, we provide a comprehensive survey on blockchain for big data, focusing on up-to-date approaches, opportunities, and future directions. First, we present a brief overview of blockchain and big data as well as the motivation behind their integration. Next, we survey various blockchain services for big data, including blockchain for secure big data acquisition, data storage, data analytics, and data privacy preservation. Then, we review the state-of-the-art studies on the use of blockchain for big data applications in different vertical domains such as smart city, smart healthcare, smart transportation, and smart grid. For a better understanding, some representative blockchain-big data projects are also presented and analyzed. Finally, challenges and future directions are discussed to further drive research in this promising area.
Paulo Valente Klaine, Lei Zhang, Bingpeng Zhou, Yao Sun · 6 authors
Due to the number of confirmed cases and casualties of the new COVID-19 virus diminishing day after day, several countries around the world are discussing how to return to the new normal way of life. In order to keep the spread of the disease under control and avoid a second wave of infection, one alternative being considered is the utilization of contact tracing. However, despite several alternatives being available, contact tracing still faces issues in terms of maintaining user privacy and security, making its mass adoption quite difficult. Based on that, a novel framework for contact tracing using blockchain as its infrastructure is presented. By integrating blockchain with contact tracing applications, user privacy can be guaranteed, while also providing people and government bodies with a complete public view of all confirmed cases. Moreover, we also investigate how public locations can aid in the contact tracing process by measuring the risk of exposure to COVID-19 to the general public and advertising it in a blockchain. By doing so, these locations can effectively report potential infection risks, while also guaranteeing privacy and trustworthiness in the information. Lastly, numerical results are shown in different scenarios and conclusions are drawn.
Mohamed El Ghazouani, My Ahmed El Kiram, Latifa Er-Rajy, Yassine El Khanboubi
With the rapid development of cloud storage, more and more cloud clients can store and access their data anytime, from anywhere and using any device. Data deduplication may be considered an excellent choice to ensure data storage efficiency. Although cloud technology offers many advantages for storage service, it also introduces security challenges, especially with regards to data integrity, which is one of the most critical elements in any system. A data owner should thus enable data integrity auditing mechanisms. Much research has recently been undertaken to deal with these issues. In this paper, we propose a novel blockchain-based method, which can preserve cloud data integrity checking with data deduplication. In our method, a mediator performs data deduplication on the client side, which permits a reduction in the amount of outsourced data and a decrease in the computation time and the bandwidth used between the enterprise and the cloud service provider. This method supports private and public auditability. Our method also ensures the confidentiality of a client's data against auditors during the auditing process.
In most practical cloud computing applications such as e-voting, auctions, health, and financial applications or cloud services in common, to prove the exactness of outsourced data is one of the major needs today. Most of the time, third party auditing is employed for this task. This auditing work is controlled by assigning the secret inputs to an entity trusted third party, or worker, who is liable for performing computations and hand over the result of the computation to the cloud users or clients. To verify the integrity of computations using traditional cryptographic techniques, the time required to generate and validate the proof is a major computation issue. This paper proposes an improved public auditing technique for multi-party computation to check the integrity of outsourced data using a cryptographic solution. Many researchers have given auditing protocols that generate and verify proof using a cryptographic solution. Most of these scheme uses Non-Interactive Zero-Knowledge Proof (NIZK) which are basically built on bilinear map technology. The verification time using these existing technique is computationally expensive which affect the performance of the auditing system. We propose an efficient protocol that verifies the result correctness using modern cryptographic technique Indistinguishability Obfuscation. The proposed system works in two phases, (i) auction and (ii) audit. During the auction phase, multiple clients share their encrypted bid value to the worker. The worker generates auction result and proof using Pedersen Commitment Scheme. The audit phase starts only after the completion of the auction phase which results in reduced verification time. During the Audit phase, clients can verify the integrity of results using NIZK with the IO technique. The results for reduced verification time in auction system have been presented. It is found that the performance of the proposed system has improved compared to the pertinent NIZK Proof technique. In our setting, we assumed that a worker is one of the trusted entity. By this notion, our protocol also guarantees privacy to the clients during the audit phase.
In recent times, vehicular ad hoc networks (VANET) have become a core part of intelligent transportation systems (ITSs), which aim to achieve continual Internet connectivity among vehicles on the road. The VANET has been used to improve driving safety and construct an ITS in modern cities. However, owing to the wireless characteristics, the message transmitted through the network can be observed, altered, or forged. Since driving safety is a major part of VANET, the security and privacy of these messages must be preserved. Therefore, this paper introduces an efficient privacy-preserving data transmission architecture that makes use of blockchain technology in cluster-based VANET. The cluster-based VANET architecture is used to achieve load balancing and minimize overhead in the network, where the clustering process is performed using the rainfall optimization algorithm (ROA). The ROA-based clustering with blockchain-based data transmission, called a ROAC-B technique, initially clusters the vehicles, and communication takes place via blockchain technology. A sequence of experiments was conducted to ensure the superiority of the ROAC-B technique, and several aspects of the results were considered. The simulation outcome showed that the ROAC-B technique is superior to other techniques in terms of packet delivery ratio (PDR), end to end (ETE) delay, throughput, and cluster size.
With its decentralization, reliable database, security, and quasi anonymity, blockchain provides a new solution for data storage and sharing as well as privacy protection. This paper combines the advantages of blockchain and edge computing and constructs the key technology solutions of edge computing based on blockchain. On one hand, it achieves the security protection and integrity check of cloud data; and on the other hand, it also realizes more extensive secure multiparty computation. In order to assure the operating efficiency of blockchain and alleviate the computational burden of client, it also introduces the Paillier cryptosystem which supports additive homomorphism. The task execution side encrypts all data, while the edge node can process the ciphertext of the data received, acquire and return the ciphertext of the final result to the client. The simulation experiment proves that the proposed algorithm is effective and feasible.
Emerging technologies, such as digital twins and 6th generation (6G) mobile networks, have accelerated the realization of edge intelligence in industrial Internet of Things (IIoT). The integration of digital twin and 6G bridges the physical system with digital space and enables robust instant wireless connectivity. With increasing concerns on data privacy, federated learning has been regarded as a promising solution for deploying distributed data processing and learning in wireless networks. However, unreliable communication channels, limited resources, and lack of trust among users hinder the effective application of federated learning in IIoT. In this article, we introduce the digital twin wireless networks (DTWN) by incorporating digital twins into wireless networks, to migrate real-time data processing and computation to the edge plane. Then, we propose a blockchain empowered federated learning framework running in the DTWN for collaborative computing, which improves the reliability and security of the system and enhances data privacy. Moreover, to balance the learning accuracy and time cost of the proposed scheme, we formulate an optimization problem for edge association by jointly considering digital twin association, training data batch size, and bandwidth allocation. We exploit multiagent reinforcement learning to find an optimal solution to the problem. Numerical results on real-world dataset show that the proposed scheme yields improved efficiency and reduced cost compared to benchmark learning methods.
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Privacy-Preserving Technologies in Data
Artificial Intelligence in Healthcare and Education
In a distributed system, cross-domain access control is an important mechanism to realize secure data sharing among multiple domains. Most of the existing cross-domain access control mechanisms are generally based on a single-server architecture, which has limitations in terms of security and reliability (the access decision may be incorrect) and completeness and confidentiality (the access records can be modified). Blockchain technology with decentralization, verifiability, and immutability properties can solve these problems. Motivated by these facts, in this article, we construct a trusted and efficient cross-domain access control system based on blockchain. Consequently, we integrate blockchain and role mapping technology to provide reliable and verifiable cross-domain access process. We use blockchain to record user roles, role mapping rules, access policies, and audit records, realizing user self-validation, and access nonreputation. Considering the low throughput of the blockchain, we design an efficient smart contract to make the access decision based on the access history of users. Finally, a performance evaluation of the system is presented to demonstrate the feasibility of the proposed system.
Blockchain is a disruptive technology presented in 2008 that allows both scarcity and timestamps to be introduced to the digital world. Whereas many technological applications may benefit from this architecture, it involves direct conflict with both Privacy rights and Data Protection rules, as introduced by the General Data Protection Regulation (GDPR). This study first provides an overview of what blockchain is, how it works, and how it can affect privacy. It describes how this technology functions, thanks to binary ledgers distributed amongst the system nodes, and what role they play in validating the succession of blocks. The work analyses how blockchain can be applied to innovative fields and investigates related Privacy issues. Indeed, the chain can certify the time, the parties and the object included in a ‘block’ but cannot guarantee the legal validity, the veracity or correctness of the content. Furthermore, its immutability is in direct conflict with the right to be forgotten. In addition, due to the distributed nature of the system, it does not allow identification of data controllers and, consequentially, the accountable subject for the personal data processed within the digital ledger. The aim of the study is to highlight the characteristics of the proposed solution, i.e. supporting centralised governance of blockchain infrastructures to ensure control over the distributed nodes, as well as having the capability to intervene in modifying the chain when the law requires it. This set of interventions would also render publicly available the personal information within the blockchain with different levels of accessibility (‘Privacy by Layers’ (PbL)) and, therefore, provide log control that can ensure compliance with the Data Protection regulatory framework. To provide complete analysis on the matter, the study also addresses how Intelligent Systems running on a blockchain-based infrastructure that holds pieces of personal information can clash with Article 22 of the GDPR on automated decisions when it affects the fundamental rights of individuals. Finally, the conclusions crystallise the legal remarks by stressing the essential elements of the analysis that emerged during the study and framing them within the bigger picture of how the Law addresses social or technological phenomena.
Abstract Currently, attribute-based authentication provides a feasible solution for fine-grained access control in cloud environment. However, the existing schemes can not solve the following problems at the same time, that is, how to ensure that the computation cost of the client does not depend on the size of underlying access structure, and how to introduce distributed authorities to manage and maintain the attribute universe. To solve the above problems, an efficient multi-authority attribute-based authentication scheme is proposed. The new scheme uses the technique of distributed attribute-based encryption to realize the access control of anonymous users, and reduces users’ computation burden by optimizing the standard implementation zero-knowledge proof and outsourcing users’ computing tasks in the authentication stage. Under the new definition of security, it can be proved that the new scheme is secure and satisfies many attractive properties, such as introducing distributed authorities, supporting outsourcing computation, satisfying attribute anonymity.
Nowadays, blockchain is developing as a secure and trustworthy platform for secure information sharing in areas of application like banking, supply chain management, food industry, energy, the Internet, and medical services. Besides, the blockchain can be described in a decentralized manner as an immutable ledger for recording data entries. Furthermore, this new technology has been developed to interrupt a variety of data-driven fields, including the health sector. However, blockchain refers to the distributed ledger technology, which constitutes an innovation in the information recording and sharing without a trusted third party. In this paper, blockchain and Distributed Ledger-based Improved Biomedical Security system (BDL-IBS) has been proposed to enhance the privacy and data security across healthcare applications. Further, our goal is to make it possible for patients to use the data to support their care and to provide strong consent systems for sharing data among different organizations and applications, since this includes managing and accessing a high amount of medical information, and this technology can maintain data to ensure reliability. Finally, results show that new blockchain-based digital platforms allow for fast, easy, and seamless interactions between data suppliers to enhance privacy and data security, including for patients themselves.
Artificial intelligence (AI) has demonstrated huge potential in a variety of real-world applications. However, some significant considerations like fairness, transparency and trustworthiness are still challenging when applying AI to trust-oriented applications such as E-voting. E-voting plays a significant role in democratic societies, which requires voters and initiators have strong mutual trust. In this paper, we aim to facilitate the consolidation of AI ecosystems by developing a blockchain-based traceable self-tallying e-voting system. We take advantage of an event-oriented linkable group signature and a homomorphic time-lock puzzle to balance the anonymity and accountability, and the voting scale and efficiency of an e-voting system. The proposed e-voting protocol supports additional functions like multi-choice and self-tallying. We prove that the proposed protocol satisfies anonymity, time-bounded privacy, linkability and full-traceability. We also evaluate the time cost of off-chain operations and the gas cost of on-chain operations, which show the proposed e-voting protocol is practical and can be adopted in real-world applications.