Victor R. Kebande, Feras M. Awaysheh, Richard A. Ikuesan, Sadi Alawadi · 5 authors
Continuous and emerging advances in Information and Communication Technology (ICT) have enabled IoT-to-Cloud applications to be induced by data pipelines coupled with Edge Intelligence-based architectures. Advanced vehicular networks greatly benefit from these architectures due to the implicit functionalities that are focused on realizing the Internet-of-Vehicle (IoV) vision. However, IoV is susceptible to attacks, where adversaries can easily exploit existing vulnerabilities. Several attacks may succeed due to inadequate or weaker authentication techniques. Hence, there is a timely need for hardening the authentication process through cutting-edge access control mechanisms. This paper proposes a Blockchain-based Multi-Factor authentication model that uses an embedded Digital Signature (MFBC_eDS) for vehicular clouds and Cloud-enabled IoV. Our proposed MFBC_eDS model consists of a scheme that integrates the Security Assertion Mark-up Language (SAML) to the Single Sign-On (SSO) capabilities for a connected Edge-to Cloud ecosystem. MFBC_eDS draws an essential comparison with the baseline authentication scheme suggested by Karla and Sood. Based on the foundations of Karla and Sood’s scheme, an embedded Probabilistic Polynomial-Time Algorithm (ePPTA) and an additional Hash function for the Pi generated during Karla and Sood’s authentication are proposed and discussed. The preliminary analysis of the proposition shows that the approach is more suitable to counter major adversarial attacks in an IoV-centered environment based on Dolev-Yao adversarial model while satisfying aspects of the CIA triad.
With the development of communication technologies in 5G networks and the Internet of things (IoT), a massive amount of generated data can improve machine learning (ML) inference through data sharing. However, security and privacy concerns are major obstacles in distributed and wireless networks. In addition, IoT has a limitation on system resources depending on the purpose of services. In addition, a blockchain technology enables secure transactions among participants through consensus algorithms and encryption without a centralized coordinator. In this paper, we first review the federated leaning (FL) and blockchain mechanisms, and then, present a survey on the integration of blockchain and FL for data sharing in industrial, vehicle, and healthcare applications.
Abstract The General Data Protection Regulation highlights the principle of data minimization, which means that only data required to successfully accomplish a given task should be processed. In this paper, we propose a Blockchain-based scheme that allows users to have control over the personal data revealed when accessing a service. The proposed solution does not rely on sophisticated cryptographic primitives, provides mechanisms for revoking the authorization to access a service and for guessing the identity of a user only in cases of need, and is compliant with the recent eIDAS Regulation. We prove that the proposed scheme is secure and reaches the expected goal, and we present an Ethereum-based implementation to show the effectiveness of the proposed solution.
Zainab Iftikhar, Yasir Javed, Syed Yawar Abbas Zaidi, Munam Ali Shah · 7 authors
With opportunities brought by Internet of Things (IoT), it is quite a challenge to assure privacy preservation when a huge number of resource-constrained distributed devices is involved. Blockchain has become popular for its benefits, including decentralization, persistence, immutability, auditability and consensus. With the implementation of blockchain in IoT, the benefits provided by blockchain can be derived in order to make IoT more efficient and maintain trust. In this paper, we discuss some applications of IoT in different fields and privacy-related issues faced by IoT in resource-constrained devices. We discuss some applications of blockchain in vast majority of areas, and the opportunities it brings to resolve IoT privacy limitations. We, then, survey different researches based on the implementation of blockchain in IoT. The goal of this paper is to survey recent researches based on the implementation of blockchain in IoT for privacy preservation. After analyzing the recent solutions, we see that the blockchain is an optimal way for preventing identity disclosure, monitoring, and providing tracking in IoT.
Monik Raj Behera, Sudhir K. Upadhyay, Suresh Shetty
Over the recent years, Federated machine learning continues to gain interest\nand momentum where there is a need to draw insights from data while preserving\nthe data provider's privacy. However, one among other existing challenges in\nthe adoption of federated learning has been the lack of fair, transparent and\nuniversally agreed incentivization schemes for rewarding the federated learning\ncontributors. Smart contracts on a blockchain network provide transparent,\nimmutable and independently verifiable proofs by all participants of the\nnetwork. We leverage this open and transparent nature of smart contracts on a\nblockchain to define incentivization rules for the contributors, which is based\non a novel scalar quantity - federated contribution. Such a smart contract\nbased reward-driven model has the potential to revolutionize the federated\nlearning adoption in enterprises. Our contribution is two-fold: first is to\nshow how smart contract based blockchain can be a very natural communication\nchannel for federated learning. Second, leveraging this infrastructure, we can\nshow how an intuitive measure of each agents' contribution can be built and\nintegrated with the life cycle of the training and reward process.\n
Self-sovereign identity is the next evolution of identity management models. This survey takes a journey through the origin of identity, defining digital identity and progressive iterations of digital identity models leading up to self-sovereign identity. It then states the relevant research initiatives, platforms, projects, and regulatory frameworks, as well as the building blocks including decentralized identifiers, verifiable credentials, distributed ledger, and various privacy engineering protocols. Finally, the survey provides an overview of the key challenges and research opportunities around self-sovereign identity.
Aiming at the privacy protection of lightweight nodes based on Bloom filters in blockchain, this paper proposes a new privacy protection method. Considering the superimposition effect of query information, node and Bloom filter are regarded as the two parties of the game. A privacy protection mechanism based on the mixed strategy Nash equilibrium is proposed to judge the information query. On this basis, a Bloom filter privacy protection algorithm is proposed when the probability of information query and privacy, not being leaked, is less than the node privacy protection. It is based on variable factor disturbance, adjusting the number of bits’ set to 1 in the Bloom filter to improve the privacy protection performance in different scenarios. The experiment uses Bitcoin transaction data from 2009 to 2019 as the test data to verify the effectiveness, reliability, and superiority of the method.
Federated learning (FL) has experienced a boom in recent years, which is jointly promoted by the prosperity of machine learning and Artificial Intelligence along with emerging privacy issues. In the FL paradigm, a central server and local end devices maintain the same model by exchanging model updates instead of raw data, with which the privacy of data stored on end devices is not directly revealed. In this way, the privacy violation caused by the growing collection of sensitive data can be mitigated. However, the performance of FL with a central server is reaching a bottleneck, while new threats are emerging simultaneously. There are various reasons, among which the most significant ones are centralized processing, data falsification, and lack of incentives. To accelerate the proliferation of FL, blockchain-enabled FL has attracted substantial attention from both academia and industry. A considerable number of novel solutions are devised to meet the emerging demands of diverse scenarios. Blockchain-enabled FL provides both theories and techniques to improve the performance of FL from various perspectives. In this survey, we will comprehensively summarize and evaluate existing variants of blockchain-enabled FL, identify the emerging challenges, and propose potentially promising research directions in this under-explored domain.
The major advances in body-mounted sensors and wireless technologies have been revolutionizing the healthcare industry, where patient’s conditions can be remotely monitored by medical staff. Such a model is gaining broad support due to its economic and social advantages. However, the wealth of sensor measurements pose major technical challenges on where to store the collected data, how to ensure its integrity, who control access permissions, and how to enable secure interaction between patients and medical facilities and professionals. This article aspires to provide a holistic solution based on blockchain technology. Our solution puts the patient in charge for granting and revoking access permissions and makes it easy for healthcare organizations and providers to meet privacy regulations. The sensor data are to reside on cloud storage, while access control and session logs are maintained on blockchain. In addition, a novel data-driven authentication and secure communication protocol is proposed to mitigate the risk of fraud and identity theft. In order to enforce such a protocol, all interactions between the cloud and patients and healthcare providers are regulated through smart contracts. The security properties of our solution are analyzed using AVISPA; it is also shown to be computationally efficient.
Federated learning enables the joint training of machine learning models by utilizing distributed data and computing resources while protecting local data privacy.The existing asynchronous federated learning can effectively solve the problems such as waste of computing resources and low training efficiency caused by synchronous learning.However, it aggregates local models from different nodes and updates the global model through the central server,which makes it endogenously subject to the centralized trust mode and suffers from some issues such as single point of failure andprivacy leakage. In this paper, we propose a blockchain-based privacy-preserving asynchronous federated learning,which ensures the trustability by storing local models into the blockchain and generating the global model through the consensus algorithm.In order to guarantee the privacy of federated learning and improve the model utility,the exponential mechanism of differential privacy is used to select model gradients with high contribution at high probability,and a lower privacy budget is allocated to ensure the model privacy.In addition, in order to solve the problem of clock desynchronization in asynchronous federated learning,we propose a two-factor adjustment mechanism to further improve the global model utility. Finally,theoretical analysis and experimental results demonstrate that our proposed scheme can effectively guarantee the trustability and privacy of the asynchronous federated learning while improving the model utility.
A flexible and effective identity system scheme has always been one of the core needs of the information age. Traditional centralized public key infrastructure has a number of flaws, and the present distributed public key infrastructure based on blockchain has a number of issues with performance, resilience, non-repudiation, identity flexibility, and other factors. This paper innovatively combines blockchain with decentralized undeniable attribute-based signatures and proposes a novel distributed public key infrastructure, which uses threshold algorithms and attribute-based signatures for fine-grained management of identities; the paper also introduces non-interactive zero-knowledge proof to make the certificate undeniable and uses the blockchain consensus mechanism to synchronize the certificate library to achieve distributed identity authentication. Through experimental modeling and analysis combined with specific scenarios' actual landing demand, this article indicates that the solution is adequate in terms of security and usability.
Forensic Science includes scientific methods to find out the actual cause of a crime and to bring justice to the victims. Forensic reports incorporate information regarding different crimes. These details are considered as extremely valuable and confidential as it helps the law enforcement agencies and prosecutors to ensure punishment to the blameworthy persons. These reports require security only to restrict access to the authorized persons. Blockchain stores every transaction occurring in the system and these transactions cannot be removed or modified because of their immutability. In this work, Inter-Planetary File System (IPFS) and Hyperledger based private blockchain are assembled to implement a secure forensic information storing system. Our system enables the tracing of any illegitimate en-trance or data tempering by the intruders. Our proposed hybrid approach surpasses the classical public blockchain systems i.e. Bitcoin and Ethereum in terms of transaction processing time achieving an average of 11.99 seconds per transaction. This system also facilitates the storing of heavyweight features which is not possible inside the existing blockchain frameworks.
Qi Zhang, Hai Lv, Junwei Ma, Jingye Li · 5 authors
Blockchain has the advantages of decentralization and distributed sharing of global ledger, but at the same time, it also has the risk of data privacy leakage. In order to prevent the privacy of blockchain users from being stolen by malicious attackers, there is still a large space for development of various protection mechanisms. At first, this paper analyzes the architecture of chain block, the Blockchain data privacy threat mainly summarized data for the application layer, network layer, data privacy threats to privacy, consensus layer data and transaction layer data privacy threats to privacy, and respectively summarized this paper introduces the principle of all kinds of privacy protection mechanism, characteristics, and different ways of implementation, Finally, it points out the future research direction of blockchain data privacy protection.
Kamilla Stevenson, Oda Skoglund, Mayank Raikwar, Danilo Gligoroski
Proof of Stake (PoS) emerged to replace and tackle the problem of vast energy consumption in Proof of Work (PoW) consensus. PoS is based on the assumption that the majority of the stake is owned by honest participants. Consequently, instead of solving a computationally hard puzzle to propose the next block in the blockchain, PoS selects a participant with probability proportional to its stake in the network. In contrast to the solution to the puzzle, the proof of selection in PoS has inherent privacy issues. The identity of the selected participant is revealed to other participants to verify the proof, and the stake of the selected can be deducted by frequency analysis. Therefore, Private Proof of Stake (PPoS) emerged to provide a valid alternative to PoW, aiming to tackle the energy consumption in PoW while preserving the privacy of the selected participant in a consensus round. Recent PPoS protocols by Baldimtsi et al. and Ganesh et al., rely on an anonymous broadcast channel and have a large proof size that hinders the practical implementation of the protocols.
Lukas Burkhalter, Hidde Lycklama, Alexander Viand, Nicolas Küchler · 5 authors
Federated Learning is an emerging decentralized machine learning paradigm that allows a large number of clients to train a joint model without the need to share their private data. Participants instead only share ephemeral updates necessary to train the model. To ensure the confidentiality of the client updates, Federated Learning systems employ secure aggregation; clients encrypt their gradient updates, and only the aggregated model is revealed to the server. Achieving this level of data protection, however, presents new challenges to the robustness of Federated Learning, i.e., the ability to tolerate failures and attacks. Unfortunately, in this setting, a malicious client can now easily exert influence on the model behavior without being detected. As Federated Learning is being deployed in practice in a range of sensitive applications, its robustness is growing in importance. In this paper, we take a step towards understanding and improving the robustness of secure Federated Learning. We start this paper with a systematic study that evaluates and analyzes existing attack vectors and discusses potential defenses and assesses their effectiveness. We then present RoFL, a secure Federated Learning system that improves robustness against malicious clients through input checks on the encrypted model updates. RoFL extends Federated Learning's secure aggregation protocol to allow expressing a variety of properties and constraints on model updates using zero-knowledge proofs. To enable RoFL to scale to typical Federated Learning settings, we introduce several ML and cryptographic optimizations specific to Federated Learning. We implement and evaluate a prototype of RoFL and show that realistic ML models can be trained in a reasonable time while improving robustness.
The traditional way of selling and buying land has lots of problems exists as its takes time, the verification process is lengthy and final processing is also time-consuming. With the development of Information and Communication Technologies (ICTs), the records are converted from file to digital. Still, there are several challenges are there to make the land management system effective and trustworthy. During the buying or selling process, different middlemen exist at various levels which makes the process complex and risky. There is a chance of duplication or forge of digital documents by the fraudulent person. To eliminate the above challenges in this paper Blockchain-based approach is applied to make the land record management system secure. The paper initially described the overall challenges that exist in the land record system in the India scenario. The authors in this paper proposed a Secure distributed architecture for land record management. Ethereum platform is used for the implementation of the land record digitization. The results analysis show that the system becomes faster, transparent, records are immutable by the use of Blockchain technology.
The past few years have seen a spate of cyber attacks targeting multinational companies, governments, institutions and even individuals' data. Most of these attacks target the vulnerabilities of the centralized system of data storage, emphasizing that a novel approach to data storage must be developed to tackle long-standing issues with centralized systems. Blockchain technology - a decentralized ledger based system provides a viable alternative to many of these issues. The objective of this paper was to develop and test a decentralized document storage system using a private Blockchain network, Ethereum coins and the InterPlanetary File System(IPFS). Interim results suggested that the proposed solution can be implemented at scale for certain use cases including confidential documents of both private and public entities.
Abel Binu Jacob, Pratiyush Prakash, Prashant Karhana, Pravati Swain
The information process of each and every individual is dependent on the Digital Identity Management (DIM) system. To decrease the requirement of remembering multiple passwords and authenticate digital Identity with improved security, we propose an optimized solution by using distributed ledger technology and smart contracts for the DIM system, i.e., Blockchain. The smart contract ensures that the user's registration process occurs only once and the required details are securely shared to the websites with the user's consent. Compared to traditional and existing digital identity management systems, the proposed Blockchain-based Web-Access through Biometrics verification ensures the user's identity in one-time registration. It provides secure and easy access to respective websites.