The intelligent vehicle (IV) has become a promising technology that could revolutionize our life in smart cities sooner or later. However, it yet suffers from many security vulnerabilities. Traditional security methods are incapable to secure the IV data sharing against malicious attacks. Blockchain, as expected by both research and industry communities, has emerged as a good solution to address these issues. The major issues in IV data sharing are trust, data accuracy and reliability of data sharing in the communication channel. Blockchain technology, previously working for the cryptocurrency, has recently applied to build trust and reliability in peer-to-peer networks with similar topologies of IV data sharing. In this chapter, we present a new framework, namely biometric blockchain (BBC), for secure IV data sharing. In our new scheme, biometric information is exploited as a cue to record who is responsible in the data sharing activities, while the proposed BBC technology serves as the backbone of the IV data-sharing architecture. Hence, the proposed BBC technology provides a more reliable trust environment between the vehicles while personal identities are traceable in the proposed new scheme.
Machine learning and blockchain are two of the most notable technologies of recent years. The first is the foundation of artificial intelligence and big data analysis, and the second has significantly disrupted the financial industry. Both technologies are data‐driven, and thus there are rapidly growing interests in integrating both for more secure and efficient data sharing and analysis. In this article, we review existing research on combining machine learning and blockchain technologies and demonstrate that they can collaborate efficiently and effectively. In the end, we point out some future directions and expect more research on deeper integration of these two promising technologies.
Ajay Kumar Shrestha, Ralph Deters, Julita Vassileva
The tremendous technological advancement in the last few decades has brought many enterprises to collaborate in a better way while making intelligent decisions. The use of Information Technology tools in obtaining data of people's everyday life from various autonomous data sources allowing unrestricted access to user data has emerged as an important practical issue and has given rise to legal implications. Various innovative models for data sharing and management have privacy and centrality issues. To alleviate these limitations, we have incorporated blockchain in user modeling. In this paper, we constructed a decentralized data sharing architecture with MultiChain blockchain in the travel domain, which is also applicable to other similar domains including education, health, and sports. Businesses that operate in the tourism industries including travel and tour agencies, hotels and resorts, shopping malls are connected to the MultiChain and they share their user profile data via stream in the MultiChain. The paper presents the hotel booking service for an imaginary hotel as one of the enterprise nodes, which collects user profile data with proper validation and will allow users to decide which of their data to be shared thus ensuring user control over their data and the preservation of privacy. The data from the repository is converted into an open data format while sharing via stream in the blockchain so that other enterprise nodes, after receiving the data, can easily convert them and store into their own repositories. The paper presents an evaluation of the performance of the model by measuring the latency and memory consumption with three test scenarios that mostly affect the user experience. The node responded quickly in all of these cases.
Yue Zhang, Jian Weng, Jiasi Weng, Ming Li · 5 authors
With the popularity of Blockchain comes grave security-related concerns. Achieving privacy and traceability simultaneously remains an open question. Efforts have been made to address the issues, while they may subject to specific scenarios. This paper studies how to provide a more general solution for this open question. Concretely, we propose Onionchain, featuring a suite of protocols, offering both traceability and privacy. As the term implies, our Onionchain is inspired by Onion routing. We investigate the principles of Onion routing carefully and integrate its mechanism together with Blockchain technology. We advocate the Blockchain community to adopt Onionchain with the regards of privacy and traceability. To this end, a case-study of Onionchain, which runs in the context of Vehicular Ad Hoc Networks (VANETs), is proposed, providing the community a guideline to follow. Systematic security analysis and extensive experiments are also conducted to validate our secure and cost-effective Onionchain.
Payment channel networks (PCNs) are viewed as one of the most promising scalability solutions for cryptocurrencies today. Roughly, PCNs are networks where each node represents a user and each directed, weighted edge represents funds escrowed on a blockchain; these funds can be transacted only between the endpoints of the edge. Users efficiently transmit funds from node A to B by relaying them over a path connecting A to B, as long as each edge in the path contains enough balance (escrowed funds) to support the transaction. Whenever a transaction succeeds, the edge weights are updated accordingly. In deployed PCNs, channel balances (i.e., edge weights) are not revealed to users for privacy reasons; users know only the initial weights at time 0. Hence, when routing transactions, users typically first guess a path, then check if it supports the transaction. This guess-and-check process dramatically reduces the success rate of transactions. At the other extreme, knowing full channel balances can give substantial improvements in transaction success rate at the expense of privacy. In this work, we ask whether a network can reveal noisy channel balances to trade off privacy for utility. We show fundamental limits on such a tradeoff, and propose noise mechanisms that achieve the fundamental limit for a general class of graph topologies. Our results suggest that in practice, PCNs should operate either in the low-privacy or low-utility regime; it is not possible to get large gains in utility by giving up a little privacy, or large gains in privacy by sacrificing a little utility.
Fan Zhang, Deepak Maram, Harjasleen Malvai, Steven Goldfeder · 5 authors
Thanks to the widespread deployment of TLS, users can access private data over channels with end-to-end confidentiality and integrity. What they cannot do, however, is prove to third parties the {\em provenance} of such data, i.e., that it genuinely came from a particular website. Existing approaches either introduce undesirable trust assumptions or require server-side modifications. As a result, the value of users' private data is locked up in its point of origin. Users cannot export their data with preserved integrity to other applications without help and permission from the current data holder. We propose DECO (short for \underline{dec}entralized \underline{o}racle) to address the above problems. DECO allows users to prove that a piece of data accessed via TLS came from a particular website and optionally prove statements about such data in zero-knowledge, keeping the data itself secret. DECO is the first such system that works without trusted hardware or server-side modifications. DECO can liberate data from centralized web-service silos, making it accessible to a rich spectrum of applications. To demonstrate the power of DECO, we implement three applications that are hard to achieve without it: a private financial instrument using smart contracts, converting legacy credentials to anonymous credentials, and verifiable claims against price discrimination.
Jawaid Iqbal, Arif Iqbal Umar, Noorul Amin, Abdul Waheed
In body sensor networks, both wearable and implantable biosensors are deployed in a patient body to monitor and collect patient health record information. The health record information is then transmitted toward the medical server via a base station for analysis, diagnosis, and treatment by medical experts. Advancement in wireless technology although improves the patient health–monitoring mechanism, but still there are some limitations regarding security, privacy, and efficiency due to open wireless channel and limited resources of body sensor networks. To overcome these limitations, we have proposed an efficient and secure heterogeneous scheme for body sensor networks, in which biosensor nodes use a certificate-less cryptography environment to resolve the key escrow and certificate-management problems, while MS uses a public key infrastructure environment to enhance the scalability of the networks. Furthermore, we design an online/offline signcryption method to overcome the burden on biosensor nodes. We split the signcryption process into two phases: offline phase and online phase. In the offline phase, the major operations are computed without prior knowledge of patient data. While in online phase, the minor operations are computed when patient data are known. Besides, we have used a new hybrid blockchain technology approach for the secure transmission of patient information along with attributes stored in the medical server toward the cloud that provides ease of patient data access remotely from anywhere by the authorized users and data backup in case of medical server failure. Moreover, hybrid blockchain provides advantages of interoperability, transparency traceability, and universal access. The formal security analysis of the proposed scheme is proved in the standard model, and informal security assures that our scheme provides resistance against possible attacks. As compared to other existing schemes, our proposed scheme consumes fewer resources and efficient in terms of processing cost, transmission overhead, and energy consumption.
In recent years, multi-party computation (MPC) frameworks based on replicated secret sharing schemes (RSSS) have attracted the attention as a method to achieve high efficiency among known MPCs. However, the RSSS-based MPCs are still inefficient for several heavy computations like algebraic operations, as they require a large amount and number of communication proportional to the number of multiplications in the operations (which is not the case with other secret sharing-based MPCs). In this paper, we propose RSSS-based three-party computation protocols for modular exponentiation, which is one of the most popular algebraic operations, on the case where the base is public and the exponent is private. Our proposed schemes are simple and efficient in both of the asymptotic and practical sense. On the asymptotic efficiency, the proposed schemes require O(n)-bit communication and O(1) rounds,where n is the secret-value size, in the best setting, whereas the previous scheme requires O(n2)-bit communication and O(n) rounds. On the practical efficiency, we show the performance of our protocol by experiments on the scenario for distributed signatures, which is useful for secure key management on the distributed environment (e.g., distributed ledgers). As one of the cases, our implementation performs a modular exponentiation on a 3,072-bit discrete-log group and 256-bit exponent with roughly 300ms, which is an acceptable parameter for 128-bit security, even in the WAN setting.
The emerging blockchain technology has enabled various decentralised applications in a trustless environment without relying on a trusted intermediary. It is expected as a promising solution to tackle sophisticated challenges on personal data management, thanks to its advanced features such as immutability, decentralisation and transparency. Although certain approaches have been proposed to address technical difficulties in personal data management; most of them only provided preliminary methodological exploration. Alarmingly, when utilising Blockchain for developing a personal data management system, fictions have occurred in existing approaches and been promulgated in the literature. Such fictions are theoretically doable; however, by thoroughly breaking down consensus protocols and transaction validation processes, we clarify that such existing approaches are either impractical or highly inefficient due to the natural limitations of the blockchain and Smart Contracts technologies. This encourages us to propose a feasible solution in which such fictions are reduced by designing a novel system architecture with a blockchain-based "proof of permission" protocol. We demonstrate the feasibility and efficiency of the proposed models by implementing a clinical data sharing service built on top of a public blockchain platform. We believe that our research resolves existing ambiguity and take a step further on providing a practically feasible solution for decentralised personal data management.
Democratic principles demand that every voter should be able to individually verify that their vote is recorded as intended and counted as recorded, without having to trust any authorities. However, most end-to-end (E2E) verifiable voting protocols that provide universal verifiability and voter secrecy implicitly require to trust some authorities or auditors for the correctness guarantees that they provide.
In this paper, we explore the notion of individual verifiability. We evaluate the existing E2E voting protocols and propose a new protocol that guarantees such verifiability without any trust requirements. Our construction depends on a novel vote commitment scheme to capture voter intent that allows voters to obtain a direct zero-knowledge proof of their vote being recorded as intended. We also ensure protection against spurious vote injection or deletion post eligibility verification, and polling-booth level community profiling.
Dinh C. Nguyen, Pubudu N. Pathirana, Ming Ding, Aruna Seneviratne
\n\t\t\t\t\tBlockchain technology with its secure, transparent and decentralized nature has been recently employed in many mobile applications. However, the process of executing extensive tasks such as computation-intensive data applications and blockchain mining requires high computational and storage capability of mobile devices, which would hinder blockchain applications in mobile systems. To meet this challenge, we propose a mobile edge computing (MEC) based blockchain network where multi-mobile users (MUs) act as miners to offload their data processing tasks and mining tasks to a nearby MEC server via wireless channels. Specially, we formulate task offloading, user privacy preservation and mining profit as a joint optimization problem which is modelled as a Markov decision process, where our objective is to minimize the long-term system offloading utility and maximize the privacy levels for all blockchain users. We first propose a reinforcement learning (RL)-based offloading scheme which enables MUs to make optimal offloading decisions based on blockchain transaction states, wireless channel qualities between MUs and MEC server and user’s power hash states. To further improve the offloading performances for larger-scale blockchain scenarios, we then develop a deep RL algorithm by using deep Q-network which can efficiently solve large state space without any prior knowledge of the system dynamics. Experiment and simulation results show that the proposed RL-based offloading schemes significantly enhance user privacy, and reduce the energy consumption as well as computation latency with minimum offloading costs in comparison with the benchmark offloading schemes.\n\t\t\t\t
Dinh C. Nguyen, Pubudu N. Pathirana, Ming Ding, Aruna Seneviratne
For current and future Internet of Things (IoT) networks, mobile edge-cloud computation offloading (MECCO) has been regarded as a promising means to support delay-sensitive IoT applications. However, offloading mobile tasks to the cloud gives rise to new security issues due to malicious mobile devices (MDs). How to implement offloading to alleviate computation burdens at MDs while guaranteeing high security in mobile edge cloud is a challenging problem. In this paper, we investigate simultaneously the security and computation offloading problems in a multi-user MECCO system with blockchain. First, to improve the offloading security, we propose a trustworthy access control mechanism using blockchain, which can protect cloud resources against illegal offloading behaviours. Then, to tackle the computation management of the authorized MDs, we formulate a computation offloading problem by jointly optimizing the offloading decisions, the allocation of computing resource and radio bandwidth, and smart contract usage. This optimization problem aims to minimize the long-term system costs of latency, energy consumption and smart contract fee among all MDs. To solve the proposed offloading problem, we develop an advanced deep reinforcement learning algorithm using a double-dueling Q-network. Evaluation results from real experiments and numerical simulations demonstrate the significant advantages of our scheme over the existing approaches.
Dinh C. Nguyen, Pubudu N. Pathirana, Ming Ding, Aruna Seneviratne
For current and future Internet of Things (IoT) networks, mobile edge-cloud\ncomputation offloading (MECCO) has been regarded as a promising means to\nsupport delay-sensitive IoT applications. However, offloading mobile tasks to\nthe cloud is vulnerable to security issues due to malicious mobile devices\n(MDs). How to implement offloading to alleviate computation burdens at MDs\nwhile guaranteeing high security in mobile edge cloud is a challenging problem.\nIn this paper, we investigate simultaneously the security and computation\noffloading problems in a multi-user MECCO system with blockchain. First, to\nimprove the offloading security, we propose a trustworthy access control using\nblockchain, which can protect cloud resources against illegal offloading\nbehaviours. Then, to tackle the computation management of authorized MDs, we\nformulate a computation offloading problem by jointly optimizing the offloading\ndecisions, the allocation of computing resource and radio bandwidth, and smart\ncontract usage. This optimization problem aims to minimize the long-term system\ncosts of latency, energy consumption and smart contract fee among all MDs. To\nsolve the proposed offloading problem, we develop an advanced deep\nreinforcement learning algorithm using a double-dueling Q-network. Evaluation\nresults from real experiments and numerical simulations demonstrate the\nsignificant advantages of our scheme over existing approaches.\n
Domenico Rotondi, Marco Saltarella, Gaetano Giordano, Francesco Pellecchia
Abstract Compliance of distributed ledger technology (DLT) based solutions with the European Union General Data Protection Regulation (EU GDPR) is currently a hot topic both from a technical point of view, as well as from a legal and normative one. Flexible working is a new working model where employees can perform their work activity without specific time and space constraints. In this context privacy and information trustworthiness are relevant and, therefore, on the one hand it can gain benefit from using DLT based solutions, on the other hand it must comply with the GDPR. This paper describes a solution that combines DLTs and the new Ciphertext‐Policy Attribute Based Encryption cryptographic techniques to support compliance with the EU GDPR in the context of flexible working. The devised solution is being concretely experimented, in the context of the EU H2020 CITADEL project, by the Municipality of Bari (Italy).
Loïc Lesavre, Priam Varin, Peter Mell, Michael L. Davidson · 5 authors
Identity management systems (IDMSs) are widely used to provision user identities while managing authentication, authorization, and data sharing within organizations and on the web. Traditional identity systems typically suffer from single points of failure, lack of interoperability, and privacy issues, such as enabling mass data collection and user tracking. Blockchain technology has the potential to alleviate these concerns: it can support the ability for users to control the custody of their own identifiers and credentials, enabling novel data ownership and governance models with built-in control and consent mechanisms. Hence, blockchain-based IDMSs, which could benefit both users and businesses, are beginning to proliferate. This work categorizes these systems into a taxonomy based on differences in blockchain architectures, governance models, and other salient features. Context is provided for the taxonomy through the description of related terms, emerging standards, and use cases while highlighting relevant security and privacy considerations.
An exemplary paradigm of how an AI can be a disruptive technological paragon via the utilization of blockchain comes straight from the world of deep learning. Data scientists have long struggled to maintain the quality of a dataset for machine learning by an AI entity. Datasets can be very expensive to purchase, as, depending on both the proper selection of the elements and the homogeneity of the data contained within, constructing and maintaining the integrity of a dataset is difficult. Blockchain as a highly secure storage medium presents a technological quantum leap in maintaining data integrity. Furthermore, blockchain’s immutability constructs a fruitful environment for creating high quality, permanent and growing datasets for deep learning. The combination of AI and blockchain could impact fields like Internet of things (IoT), identity, financial markets, civil governance, smart cities, small communities, supply chains, personalized medicine and other fields, and thereby deliver benefits to many people.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Baraka William Nyamtiga, Jose Costa Sapalo Sicato, Shailendra Rathore, Yunsick Sung · 5 authors
As a core technology to manage decentralized systems, blockchain is gaining much popularity to deploy such applications as smart grid and healthcare systems. However, its utilization in resource-constrained mobile devices is limited due to high demands of resources and poor scalability with frequent-intensive transactions. Edge computing can be integrated to facilitate mobile devices in offloading their mining tasks to cloud resources. This integration ensures reliable access, distributed computation and untampered storage for scalable and secure transactions. It is imperative therefore that crucial issues of security, scalability and resources management be addressed to achieve successful integration. Studies have been conducted to explore suitable architectural requirements, and some researchers have applied the integration to deploy some specific applications. Despite these efforts, however, issues of anonymity, adaptability and integrity still need to be investigated further to attain a practical, secure decentralized data storage. We based our study on peer-to-peer and blockchain to achieve an Internet of Things (IoT) design supported by edge computing to acquire security and scalability levels needed for the integration. We investigated existing blockchain and associated technologies to discover solutions that address anonymity, integrity and adaptability issues for successful integration of blockchain in IoT systems. The discovered solutions were then incorporated in our conceptual design of the decentralized application prototype presented for secure storage of IoT data and transactions.
Jernej Mihelj, Yuan Zhang, Andrej Kos, Urban Sedlar
Real-time data about various traffic events and conditions-offences, accidents, dangerous driving, or dangerous road conditions-is crucial for safe and efficient transportation. Unlike roadside infrastructure data which are often limited in scope and quantity, crowdsensing approaches promise much broader and comprehensive coverage of traffic events. However, to ensure safe and efficient traffic operation, assessing trustworthiness of crowdsourced data is of crucial importance; this also includes detection of intentional or unintentional manipulation, deception, and spamming. In this paper, we design and demonstrate a road traffic event detection and source reputation assessment system for unreliable data sources. Special care is taken to adapt the system for operation in decentralized mode, using smart contracts on a Turing-complete blockchain platform, eliminating single authority over such systems and increasing resilience to institutional data manipulation. The proposed solution was evaluated using both a synthetic traffic event dataset and a dataset gathered from real users, using a traffic event reporting mobile application in a professional driving simulator used for driver training. The results show the proposed system can accurately detect a range of manipulative and misreporting behaviors, and quickly converges to the final trust score even in a resource-constrained environment of a blockchain platform virtual machine.
Identity management (IdM) is a method used to determine user identities. The centralized aspect of IdM introduces a serious concern with the growing value of personal information, as well as with the General Data Protection Regulation (GDPR). The problem with currently-deployed systems and their dominating approach, with identity providers (IdP) and single-point services, is that a third party is in charge of maintaining and controlling the personal data. The main challenge to manage data securely lies in trusting humans and institutes who are responsible for controlling the entire activity. Identities are not owned by the rightful owners or the user him/herself, but by the mentioned providers. With the rise of blockchain technology, self-sovereign identities are in place utilizing decentralization; unfortunately, the flaws still exist. In this research, we propose DNS-IdM, a smart contract-based identity management system that enables users to maintain their identities associated with certain attributes, accomplishing the self-sovereign concept. DNS-IdM has promising outcomes in terms of security and privacy. Due to the decentralized nature, DNS-IdM is able to avoid not only the conventional security threats, but also the limitations of the current decentralized identity management systems.
The recent development in IoT and 5G translates into a significant growth of Big data in 5G—envisioned industrial automation. To support big data analysis, Deep Learning (DL) has been considered the most promising approach in recent years. Note, however, that designing an effective DL paradigm for IoT has certain challenges such as single point of failure, privacy leak of IoT devices, lack of valuable data for DL, and data poisoning attacks. To this end, we present BlockDeepNet, a Blockchain-based secure DL that combines DL and blockchain to support secure collaborative DL in IoT. In BlockDeepNet, collaborative DL is performed at the device level to overcome privacy leak and obtain enough data for DL, whereas blockchain is employed to ensure the confidentiality and integrity of collaborative DL in IoT. The experimental evaluation shows that BlockDeepNet can achieve higher accuracy for DL with acceptable latency and computational overhead of blockchain operation.
Due to little attention given to anonymous protection against eavesdropping attacks in Bitcoin network, this paper initiatively proposes a solution to Bitcoin anonymization based on network structure. We first present a general adversarial network model for formulizing deanonymization attack, then present a novel propagation method APRP(Adaptive PageRank Propagation) that adopts PageRank as propagation delay factor and constantly adjusts PR-value of nodes to adapt to network dynamics. Experiments on both simulated and real Bitcoin networks confirm the superiority of APRP in terms of 20-50% performance enhancement under various deanonymization attacks.
Open access
Internet Traffic Analysis and Secure E-voting
Privacy-Preserving Technologies in Data
Advanced Steganography and Watermarking Techniques
Mohammad Saidur Rahman, Ibrahim Khalil, Pathum Chamikara Mahawaga Arachchige, Abdelaziz Bouras · 5 authors
In this paper, we present a novel architecture of blockchain-based tamper-proof electronic health record (EHR) management system. Recording electronic health data in cloud-based storage systems always pose a threat to information security. Intruders can delete or tamper EHR of patients, giving benefits to insurance companies or hiding medical malpractices (e.g. misdiagnosis and delayed diagnosis). A tamper-proof EHR management system is required that would essentially solve such issues. The blockchain is an emerging technology that can be adapted to develop a tamper-proof data management system. However, establishing a new blockchain based system replacing the existing system is expensive. In our proposed architecture, we introduce a wrapper layer integration mechanism, named as the blockchain handshaker, between the existing cloud-based EHR management system and public blockchain network to develop a tamper-proof health record management system. We implement a prototype to provide evidence on the feasibility of the proposed concept.
Abstract In the traditional medical system, individual medical data is managed by hospitals rather than individual patients. It is difficult to exchange effectively with fragmented storage, and large amounts of data are difficult to realize their potential value. With the rapid development of medical informatization, centralized storage of fragmented medical data has been unable to meet the relevant needs of the medical industry. To solve the difficulty of sharing and the complexity of confirming rights in the medical system, this paper proposes a medical data sharing model based on blockchain. The model provides reliable storage with IPFS file system, uses Proxy re-encryption to realize data sharing and ensure data proprietary rights, and uses Token economic system to measure the contribution in the sharing process, which stimulates the enthusiasm of sharing. At last, based on the existing sharing problem of medical data, the paper shows the potential solution.