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.
With the rapid development of the Internet of Things (IoT), more and more data are generated by smart devices to support various edge services. Since these data may contain sensitive information, security and privacy of data aggregation has become a key challenge in IoT. To tackle this problem, a blockchain-based secure data aggregation strategy, namely (BSDA), is proposed for edge computing empowered IoT. Specifically, in order to restrict task receivers [i.e., mobile data collectors (MDCs)] to search and accept tasks, the block header is intergraded with a security label including task security level (SL) and task completion requirement. Accordingly, new block generation rules are developed to improve system performance in throughput and transaction latency. Furthermore, BSDA decomposes both sensitive tasks and task receivers into groups against privacy disclosure. On the other hand, a deep reinforcement learning method, the improved self-adaptive double bootstrapped deep deterministic policy gradient (IDDPG), is developed to design energy-efficient MDC routes under the constrains that the SLs of MDCs should be higher than the SLs of data aggregation tasks. Simulation results indicate that 1) as a privacy-preserving strategy, BSDA obtains high throughput and low transaction latency and 2) BSDA outperforms certain contemporary strategies in aggregation ratio and energy cost.
Vehicular ad hoc networks (VANETs) has become an important part of modern intelligent transportation systems (ITS). However, under the influence of malicious mobile vehicles, offloading vehicle tasks to the cloud server is threatened by security attacks. Edge cloud offloading (ECCO) has considered a promising approach to enable latency-sensitive VANET. How to solve the complex computation offloading of vehicles while ensuring the high security of the cloud server is an issue that needs urgent research. In this paper, we studied the safety and offloading of multi-vehicle ECCO system based on cloud blockchain. First, to achieve consensus in the vehicular environment, we propose a distributed hierarchical software-defined VANET (SDVs) framework to establish a security architecture. Secondly, to improve the security of offloading, we propose to use blockchain-based access control, which protects the cloud from illegal offloading actions. Finally, to solve the intensive computing problem of authorized vehicles, we determine task offloading via jointly optimizing offloading decisions, consensus mechanism decisions, allocation of computation resources and channel bandwidth. The optimization method is designed to minimize long-term system of delays, energy consumption, and flow costs for all vehicles. To better resolve the proposed offloading method, we develop a new deep reinforcement learning (DRL) algorithm via utilizing extended deep Q-networks. We evaluate the performance of our framework on access control and offloading through numerical simulations, which have significant advantages over existing solutions.
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.
Lelio Campanile, Mauro Iacono, Alexander H. Levis, Fiammetta Marulli · 5 authors
Smart streets promise widely available traffic information to help improve people's safety. Unfortunately, gathering that data may threaten privacy. We describe an architecture that exploits a blockchain and the Internet of Vehicles and show its compliance with the General Data Protection Regulation.
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.
Swaraj Kumar, Sandipan Dutta, Shaurya Chatturvedi, M. P. S. Bhatia
Several recent advances in Federated Learning have made it possible for researchers to train their models on private data present on contributing devices without compromising their privacy. In this paradigm, each contributor’s local updates are aggregated and averaged to update the global model. In this paper, we introduce a secure and decentralized training for distributed data. In order to develop an efficient decentralized system, blockchain technology is introduced via Ethereum, which enables us to create a value-driven incentive mechanism. This is done to encourage the contributors to positively affect the learning of the global model. We provide an enhanced security mechanism by implementing differential privacy and homomorphic encryption. The performance of the global model has been significantly boosted by implementing Elastic Weight Consolidation, which prevents Catastrophic forgetting, a scenario where the model learns only on new data and forgets its previous learnings. It proves essential in distributed training since the model is being trained on a spectrum of data, often present in clusters on each contributor’s device. We introduce an innovative way of using hyperparameter optimization in federated learning with the help of Hyperopt and deposit based reward mechanism. Experiments verify the capability of the novel strategies incorporated in our system.
Many supply chain finance problems can be fundamentally solved by using blockchain. The current technical challenge of applying blockchain in supply chain finance is the lack of systematic privacy protection architecture. Based on the architecture of Julongchain, through the nonlinear principal component analysis in private data feature extraction, this paper proposed a new type of privacy protection architecture based on specific supply chain financial system. This privacy protection solution split privacy data by decision machine, then mixed data using mixed mechanism, finally determined the characteristic weight of privacy data by applying the decentralized decision institution.
Komal Gilani, E. Bertin, Julien Hatin, Noël Crespi
In the digital revolution, even secure communication between individuals, services and devices through centralized digital entities presents considerable risks. Having service providers continue to offer their centric solutions is inefficient in terms of duplication, has serious security lacunae and is cumbersome to the users. The self-sovereign Identity concept, which includes the individual’s consolidated digital identity and verified attributes, enables the users of data to exert their ownership and gain insights from their data’s usage. The authentication and verification of digital identity is essential to achieve the privacy and security of distributed digital identities. In this paper, we provide a coherent view of the central concepts of self-sovereign Identity, including the components of identity proofing and authentication solutions for different self-sovereign Identity solutions. We discussed an overview of Identity management approaches, introducing an architecture overview as well as the relevant actors in such a system and blockchain technology as solution for distributed user-centric identity. Finally, we discuss the existing solutions and point out the research gaps and elaborate challenges and trade-offs towards building a complete identity management system.
: With the increasing digitization of financial transactions, ensuring privacy and security has become a critical challenge. Traditional cryptographic techniques such as RSA and AES provide strong security guarantees but do not always protect user privacy. Privacy-preserving cryptographic solutions, including zero-knowledge proofs, homomorphic encryption, and secure multi-party computation, offer enhanced privacy for financial transactions while maintaining security and efficiency. This paper explores modern cryptographic techniques that preserve financial privacy, their advantages, implementation challenges, and future research directions
Technological advancements in blockchain solutions have allowed for the development of applications that were not previously viable. Many of those applications will require querying potentially vast amounts of transaction data. With a potential transaction throughput orders of magnitude higher than was possible until recently, such applications would benefit from delegating those queries to third party services with the computing power and infrastructures allowing the quick return of accurate results. The problem, however, with delegating queries, is that it creates a need for trust in a third party that blockchain technology was explicitly designed to eliminate. Such a third party query service should then be able to provide proof of the authenticity of the results it returns to its users. In this project, we build an API that provides fast and scalable random access to transaction data on the Algorand blockchain and allows queries according to a number of transaction parameters. A mathematical proof based on the principle of Zero-Knowledge Proof allows the user of the API to verify the authenticity and completeness of the queried transaction data. This work is based on the vChain framework described in the paper "vChain: Enabling Verifiable Boolean Range Queries over Blockchain Databases" by Cheng Xu, Ce Zhang, and Jianliang Xu.
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.
Hasventhran Baskaran, Salman Yussof, Fiza Abdul Rahim, Asmidar Abu Bakar
Blockchain is a time stamped ledger that is used to keep immutable records. This technology has gained immense popularity due to the use of its decentralized architecture in cryptocurrency platforms. Blockchain has been increasingly adopted in other sectors due to its ability to ensure data integrity. The increasing use of blockchain by the public has made it become a subject to data privacy laws. Based on the study conducted by other researchers, there are features of blockchain that conflict with certain elements in data privacy laws. The European Union's General Data Protection Regulation (GDPR), which becomes a model for data privacy act of many other countries, has been identified to be incompatible with blockchain. One of the research works in the area of blockchain is to figure out how blockchain can be made to comply with such privacy laws. In Malaysia, blockchain is still relatively new and a study needs to be done to evaluate its compatibility with Personal Data Protection Act 2010 (PDPA). Hence, the aim of this paper is to identify the gaps between blockchain and PDP A in terms of their compatibility and to propose solutions to bridge the gaps. Based on the gaps identified, the paper proposed the use permissioned or private blockchain, off-chain storage and stealth address to enable a blockchain application to be compliant with PDPA.