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.
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.
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.
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.
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.
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.