In recent years, Artificial intelligence (AI) have been applied in many fields, including driverless cars, smart cities, healthcare, finance, etc. However, data island and data privacy security are still two major challenges for AI, federated learning is proposed as a solution. In federated learning, many clients collaborate to train a common model under the coordination of a central server, while keeping the training data decentralized. Each client's data does not leave the local area, and a global shared model is jointly built by means of parameter exchange under the encryption mechanism, the built model serves only the local target in their respective regions. In this paper, we present the definition, classification, and learning process of the federated learning, and discuss the key challenges faced by federated learning and the solutions that are currently available.
Manuel Valentin, Claus Pahl, Nabil El Ioini, Hamid R. Barzegar
Recent developments in distributed ledger technologies have created a whole new set of possibilities in the way of managing trust, security, privacy and traceability in computer-based transactions, principles which are increasingly gaining importance in the world of IoT. Currently, IoT devices are generally based on centralized, client-server systems, where digital service providers have complete control over user data and information generated by their devices. In this paper we present the development of a decentralized access and management system for IoT devices, where operations on these devices, such as the installation and management of apps are handled by a blockchain-based identification and record system. The system prototype consists of a smartphone application acting as a management hub for the whole system, an IoT device API implementation for allowing secure access to management and data functionalities, and a set of smart contracts on the Ethereum blockchain, where all necessary information for the functioning of the system is stored. The system allows app developers to provide their apps as Docker containers for IoT devices without the need to publish them on a centralized app store, and a regular user can subscribe to and access the available applications by paying in cryptocurrency without revealing any private information to the system. A cost and performance evaluation have been performed to assess the feasibility of the proposed solution.
This systematic review investigates consumer trust in blockchain applications and makes recommendations for future research. The review targeted papers focusing on blockchain applications, collecting data from a consumer perspective, and examining trust as an outcome variable. We excluded non-peer-reviewed papers written before 2008 or in languages other than English. Our search in 5 databases yielded 704 studies. 29 studies were retained for the full-text review and 5 studies were included after conflict resolution. The small number of studies retained for analysis highlights the need for further empirical research on consumer trust in blockchain technology. While blockchain remained a mysterious term for most consumers, trust was the main factor determining the use of blockchain applications. Additionally, interface design, service, and information quality, together with the platform's ability to allow consumers to investigate products using blockchain applications. Trust remains the central issue for blockchain as trust in this “trustless” system appears to be a prerequisite for actual use, creating a “trust paradox”. This systematic review on consumer trust in blockchain technology is the first to provide a preliminary synthesis of consumers' needs and expectations for blockchain developers and provides important directions for future research on blockchain applications.
Mpyana Mwamba Merlec, Youn Kyu Lee, Seng-Phil Hong, Hoh Peter In
A massive amount of sensitive personal data is being collected and used by scientists, businesses, and governments. This has led to unprecedented threats to privacy rights and the security of personal data. There are few solutions that empower individuals to provide systematic consent agreements on distinct personal information and control who can collect, access, and use their data for specific purposes and periods. Individuals should be able to delegate consent rights, access consent-related information, and withdraw their given consent at any time. We propose a smart-contract-based dynamic consent management system, backed by blockchain technology, targeting personal data usage under the general data protection regulation. Our user-centric dynamic consent management system allows users to control their personal data collection and consent to its usage throughout the data lifecycle. Transaction history and logs are recorded in a blockchain that provides trusted tamper-proof data provenance, accountability, and traceability. A prototype of our system was designed and implemented to demonstrate its feasibility. The acceptability and reliability of the system were assessed by experimental testing and validation processes. We also analyzed the security and privacy of the system and evaluated its performance.
Haider Hussain Al Oliwi, Zainab Al Husain, Reza Rafeh
One of the most popular applications of the Internet of Things (IoT) is smart home which consists of several IoT devices for collecting data and controlling home appliances. Since IoT devices communicate with external entities for service provision smart homes are vulnerable to security threats. Blockchain is a public distributed ledger which is immutable and can securely store data. One solution for security issues with smart homes can be integrating blockchain and smart homes. This paper proposes a new framework for integrating blockchain and smart home in which a smart contract controls the interaction among IoT devices with external entities. Smart contract of a home is associated with the public key of the householder and can have several subcontracts. Subcontracts are used to govern the interaction between one or more IoT devices and one or more external entities. The successful results of the first prototype of the proposed framework show its potential to appropriately address the security and data privacy issues with smart homes.
Many countries have been using online voting systems instead of ballot papers. In such voting systems, voters can cast votes online using their cell phones, laptops, and other internet-connected devices. Online voting systems have centralized databases where a malicious attacker can hack, change or manipulate the database. Therefore, current online voting systems present a security concern for any government. Blockchain is a peer-to-peer technology in which data is stored in a tamper-proof ledger and maintained by participating peers. In this paper, we propose a secure online voting system using Ethereum Blockchain and Ethereum Smart Contracts. A voter can cast her vote securely after scanning her fingerprint and iris recognition. Our proposed methodology uses Paillier homomor-phic encryption and the private key shifting technique to ensure voter privacy and identity security.
Internet Traffic Analysis and Secure E-voting
Advanced Steganography and Watermarking Techniques
Institutions in highly regulated domains such as finance and healthcare often have restrictive rules around data sharing. Federated learning is a distributed learning framework that enables multi-institutional collaborations on decentralized data with improved protection for each collaborator's data privacy. In this paper, we propose a communication-efficient scheme for decentralized federated learning called ProxyFL, or proxy-based federated learning. Each participant in ProxyFL maintains two models, a private model, and a publicly shared proxy model designed to protect the participant's privacy. Proxy models allow efficient information exchange among participants without the need of a centralized server. The proposed method eliminates a significant limitation of canonical federated learning by allowing model heterogeneity; each participant can have a private model with any architecture. Furthermore, our protocol for communication by proxy leads to stronger privacy guarantees using differential privacy analysis. Experiments on popular image datasets, and a cancer diagnostic problem using high-quality gigapixel histology whole slide images, show that ProxyFL can outperform existing alternatives with much less communication overhead and stronger privacy.
Internet of Things (IoT) applications bring evolved and intelligent services that can help improve users' daily lives. These applications include home automation, health care, and smart agriculture. However, IoT development and adoption face various security and privacy challenges that need to be overcome. As a promising security paradigm, context-aware security enables one to enforce security and privacy mechanisms adaptively. Moreover, with the advancements in edge computing, context-aware security services can dynamically be placed close to a user's location and enable the support of low latency communication and mobility. Therefore, the design of an adaptive and decentralized access control mechanism becomes a necessity. In this paper, we propose a decentralized context-aware authorization management as a service based on the blockchain. The proposed architecture extends the Authentication and Authorization for Constrained Environments (ACE) framework with blockchain technology and context-awareness capabilities. Instead of a classic Open Authorization 2.0 (OAuth) access token, it uses a new contextual access token. The evaluation results show our proposition's effectiveness and advantages in terms of usability, security, low latency, and energy consumption.
Ioan-Mihail Stan, Ilie-Constantin Barac, Daniel E. Rosner
The ability to vote in state elections represents a basic right and expressing the vote is a civic obligation. However, the pandemics of COVID-19 showed us how such a simple process for a citizen may generate indirect barriers in expressing the electoral options. The risks of getting in contact with the virus made the classical voting method infeasible in times of crisis. Thus, a new opportunity arises, anticipated since 2016 by the Eurobarometer - the need to provide methods to vote over the Internet. Currently, in the European Union, only Estonia can provide citizens with such services. To overcome the current situation, we propose an architecture of a highly scalable e-election system implemented over the blockchain technology. The aim is to provide a general viable solution and to reduce the time to market, as a quick response to the current situation. The latter aspect is ensured by the Hyperledger Fabric, a permissioned, highly customizable, and integration-focused blockchain implementation. The paper provides a qualitative solution evaluation covering essential security aspects (e.g., resilience to Sybil Attack) and performance measurements (block sizing, state database performance). The contrast established with other platforms, is the simple but tamper-proof architectural view, focused on isolation and distribution of functions.
The digitization of Electronic Medical Record (EMR) provides potential access to a wealth of medical information, but also presents new challenges in privacy-preserved EMR exchanging and sharing. In this paper, we propose a blockchain-based smart healthcare system with fine-grained privacy protection for reliable data exchanging and sharing among different users. We design a blockchain-enabled dynamic access control framework combined with Local Differential Privacy (LDP) strategies to provide the attribute-based privacy protection in transaction workflow. We design four types of smart contracts in the framework to meet the requirements of anonymous transaction, dynamic access control, beneficial matching decision, and evaluation of published data in an open network. To satisfy fine-grained privacy protection, we classify sensitive attributes of EMRs into different levels and set differential privacy budgets to randomize attributes before data publishing. Also, we design data quality function to depict the disturbance incurred by LDP-based privacy preferences at the requester view, and present appropriate many-to-many matching decisions among participants for beneficial transactions. Finally, we develop a prototype system and test our approach using 200,000 real-world EMRs. Experimental results show that the proposed privacy-preserved scheme can make stable and reliable transactions between EMR publishers and requesters. The prototype system achieves individual-centric privacy configuration at the patient site, while providing error-guaranteed statistics at the requester site. Additionally, the access control policies, logs of anonymous transaction are kept in the blockchain to provide system-level traceability.
Vincent Schlatt, Johannes Sedlmeir, Simon Feulner, Nils Urbach
Know your customer (KYC) processes place a great burden on banks, because they are costly, inefficient, and inconvenient for customers. While blockchain technology is often mentioned as a potential solution, it is not clear how to use the technology's advantages without violating data protection regulations and customer privacy. We demonstrate how blockchain-based self-sovereign identity (SSI) can solve the challenges of KYC. We follow a rigorous design science research approach to create a framework that utilizes SSI in the KYC process, deriving nascent design principles that theorize on blockchain's role for SSI.
Purpose: The study aimed to determine why Turkish participants are voluntary or hesitant to use cryptocurrencies and to assess which factors affected cryptocurrencies usage. Method: Technology acceptance model (TAM) was used in the study to analyze Turkish individuals’ cryptocurrency usage behavior. Structural equation (regression) modeling (SEM) was used to test the research model. AMOS and SPSS programs were used for analysis. Findings: The study model showed that cryptocurrency usage was exposed to the most effect by perceived benefit. Contrary to expectations, perceived risk did not make any significant effect on usage behavior. A causality relation between perceived benefit and decentralization factor was found more than other benefit sub-factors. Perceived ease of use had no positive direct relation with usage behavior in the study. Perceived ease of use had a positive relation with perceived benefit in the study.
Dae-Geun Yoon, Sung-Jin Moon, Kisung Park, Sungkee Noh
As the needs for personal data increase due to the advent of the AI era, many companies are collecting their users' data and using it to advance the service. As the use of personal data increases, the value of personal data also increases. Although these valuable personal data are generated by individuals, only centralized service providers get profit from the data. In this paper, we propose a blockchain-based personal data trading system using DID (Decentralized Identifiers) and VC (Verifiable Credentials). Our proposed system allows users to collect personal data in their own data storage provided by the system. DID and VC are used to authenticate the user's identity and to prove ownership of the data without any centralized systems, respectively. The integrity of the traded data and the history of the transactions are ensured by Hyperledger Fabric, which is a decentralized infrastructure composed of consortium blockchain nodes. We show how our system works by implementing the monitoring system that provides the current status of the user's data and trading. We verify that two end entities including a seller and a buyer can complete personal data trading by using our proposed system without centralized service providers.
Abstract With the popularity and development of sensors‐containing intelligent terminals, mobile crowdsensing system (MCS) based on the Internet of Things (IoT) has become a new paradigm of application. By the MCS, the pervasive smart device users are enabled to collect large‐scale data cost‐effectively, for crowd intelligent extraction and human‐centric service delivery. However, most of the existing MCSs are based on a centralized structure vulnerable to attacks and intrusions. Moreover, the data collected through crowdsensing are diverse and difficult to guarantee user privacy, especially during the payment and data upload stages. In this article, we propose a blockchain‐based privacy‐preserving crowdsensing (BPPC) scheme based on the distributed structure, to protect user privacy. First, we combine the multiblockchain technology and K‐anonymity to construct anonymity groups for the confusion. Second, we present the random algorithm HashProof to select candidates from the anonymity groups to avoid deployment of Trusted Third Party (TTP) or agent server. Ultimately, we design encryption‐based algorithms building trust and authentication mechanisms in the system to guarantee the confidentiality of user data and achieve the accurate distribution of rewards. To verify the effectiveness and efficiency of the BPPC scheme, extensive experiments were conducted.
The wealth of user data acts as a fuel for network intelligence toward the sixth generation wireless networks (6G). Due to data heterogeneity and dynamics, decentralized data management (DM) is desirable for achieving transparent data operations across network domains, and blockchain can be a promising solution. However, the increasing data volume and stringent data privacy-preservation requirements in 6G bring significantly technical challenge to balance transparency, efficiency, and privacy requirements in decentralized blockchain-based DM. In this paper, we investigate blockchain solutions to address the challenge. First, we explore the consensus protocols and scalability mechanisms in blockchains and discuss the roles of DM stakeholders in blockchain architectures. Second, we investigate the authentication and authorization requirements for DM stakeholders. Third, we categorize DM privacy requirements and study blockchain-based mechanisms for collaborative data processing. Subsequently, we present research issues and potential solutions for blockchain-based DM toward 6G from these three perspectives. Finally, we conclude this paper and discuss future research directions.
Purpose This study aims to understand the benefits and challenges associated with the adoption of a blockchain-based identity management system in public services by conducting an academic literature review, and to explore the design of such a system that can be applied to the Korean government. Design/methodology/approach This study explores the adoption of a blockchain-based identity management system using a literature review and an actual design case intended for use by the government sector. Findings Blockchain-based identity management systems can significantly improve transparency, accountability, and reliability in the user control of one's own data while reducing the time and cost needed to deliver public services, as well as increasing administrative efficiency. However, it is not always easy to implement such systems, and introducing new technologies in the government field requires a complicated, time-consuming process. There is currently an appetite for research extending beyond the typical technology-driven approach to elucidate the government adoption of new technologies and explore its implications. Practical implications The idea behind this system is that by storing and managing personal information on the blockchain and providing mobile apps to customers, users can log in or retrieve previously authenticated personal information without having to go through an authentication process. Since users do not need to go through the verification process every time, it is expected that they will be able to access only the necessary personal information more quickly and conveniently without having to deal with unnecessary details. In addition, the blockchain-based operation of a public service effectively increases the transparency and reliability of that service and reduces the social costs caused by personal information leakage. Originality/value This study introduces the design of a blockchain-based identity management system that can be used in public services, specifically in the Korean government sector for the first time. Along with a literature review, the implications that this study gleans from these real-world use cases can contribute to this field of research.
Crowdsourcing Data Trading is a novel paradigm, where a platform can aggregate data collected by a group of mobile users. Traditional crowdsourcing data trading systems rely on trusted data trading brokers, which increase costs and cannot prevent collusion. What’s more, they don’t take of data quality and truthfulness into consideration con-currently. The emergence of blockchain and smart contracts brings in a decentralized scheme. However, the selection of the most effective providers remains challenging. To tackle these problems, we propose a dynamic-game-with-complete-information-and-Blockchain-based Crowdsourcing data Trading system (CBCT), which mainly includes a smart contracts called CBCToken. Firstly, we replace the data trading broker and adopt the Stackelberg Game, a Dynamic Game with complete information (DGC), to manage the selection of providers. Moreover, homomorphic watermarking technology is applied to protect the data copyright. Lastly, we deploy BCDToken on an Ethereum test network to demonstrate its practicability and significant performances.
In the process of using smart contracts, users need to provide privacy data such as personal account information to the contract for transactions. However, as the number of users continues to increase, privacy data leakage has become more and more serious. Also, the current model checking methods do not involve verifying the privacy data in smart contracts. For these reasons, we introduce a privacy-preserving framework based on stochastic model checking called VeriPrivData. It formally expresses privacy data by data sensitivity. The smart contract is defined as the Commitments tuple with data sensitivity. Then the Commitments tuple is used to model as DTMC (Discrete Time Markov Chains). We extend PCTL (Probabilistic Computation Tree Logic) to ds-PCTL (Probabilistic Computation Tree Logic with data sensitivity) for describing the privacy requirements. Finally, we verify whether DTMC satisfies the ds-PCTL formula. In this paper, the VeriPrivData framework uses the stochastic model checking tool PRISM, and experiments are carried out. Experimental results show that this method can effectively avoid illegal disclosure of privacy data and enhance the protection of privacy data in the contract.
Junaid Ahmed Khan, Kavyashree Umesh Bangalore, Kaan Özbay
Privacy preservation in contact tracing for COVID-19 is challenging as such applications tend to reveal users sensitive data which is shared together with their location. This paper proposes COVERT-Blockchain, a novel distributed ledger based platform for contact tracing without revealing users privacy where infected users only share their anonymized location traces on the Blockchain with a sliding window. To further reduce the chances of revealing the corresponding users' trajectories, in COVERT-Blockchain we employ an adaptive logging mechanism to store trajectory data for contact tracing only if the users stayed in a location for longer time duration. COVERT-Blockchain is evaluated for scalability and robustness in terms of overhead and delays in storing and retrieving data, results show it to be efficiently achieving contact tracing without privacy leakage.