Moritz Tobias Bruckner, Adeline Frenzel, Daniel Veit
Amazon Mechanical Turk (MTurk) allows organizations and individuals to benefit from a collective source of intelligence, skills and insights from a global population, but MTurk withdraws from responsibilities and the obligation of overlooking payment transactions. Consequently, there is no contractual agreement between the crowd worker and the requester on when payments should be authorized. Requesters even obtain ownership of the work without having to pay for it. This situation of lock-up poses a serious issue for crowd workers. As a solution to this problem, the present study introduces smart contracts to transactions on crowd work platforms, arguing that smart contracts can mitigate risks from lock-up situations and support trust, through non-alterable terms and conditions imprinted into the code of a smart contract. This research conceptualizes an online experimental study to validate the trust building and risk mitigating effects of smart contracts in crowd work transactions on Amazon Mechanical Turk.
Aiming at the problem of privacy leakage during data sharing in the Internet of Things, a blockchain based secure data sharing platform with fine-grained access control(BSDS-FA) is proposed. First, this paper proposes a new hierarchical attribute-based encryption algorithm, which uses hierarchical attribute structure and multi-level authorization center. The algorithm implements flexible and fine-grained access control by distributing different user attributes to different authorization centers. Then, it combined with the Fabric blockchain technology to solve the problem of huge decryption cost for users in the Internet of things. Smart contract in blockchain executes high-complexity partial decryption algorithm to reduce the users' decryption overhead. Blockchain can also realize the traceability of historical operations to meet the security requirements of data restriction open and transparent supervision. Finally, the hierarchical attribute-based encryption algorithm is proved to be CPA-safe. The theoretical analysis and experimental results show that BDSS-FA provides more secure and reliable data sharing services for users in the Internet of Things.
Building trust relationships between different decentralized entities in the IoT ecosystem is essential. Hereof, the combination of blockchain technology and trust evaluation techniques is recently considered as an efficient measure. However, both technologies within the IoT are still facing some limitations which are addressed in this research. First, this publication reviews various blockchain-based trust approaches and depicts their strengths and limitations regarding their usage in decentralized IoT communities. Then, an optimized trust model with a multi-layer adaptive and trust-based weighting system is proposed. Additionally, different trust metric parameters and their mathematical models used for trust evaluation are presented. Moreover, this publication presents a novel approach for incentivization processes in the IoT marketplace using control loops and smart contracts. Thereby, participants are motivated to continuously improve their behavior. Finally, the proposed trust model is proved to be reliable. The experimental results conducted from different scenarios show that the presented approach provides more resiliency against various attacks than existing ones.
As use of data is becoming increasingly pervasive in all facets of society, many organizations and businesses are building their capabilities for better and more valuable uses for it. At the same time, infusing the use of data to various organizational practices has turned out to be a very complex task in practice. With this case study I aim to build on the understanding on how various organizational structures and processes impact the utilization of data in a large utilities company with highly autonomous business units. This study was motivated by two perspectives on data governance which as of yet have been relatively little researched. First, the literature review of this research indicates that data governance impacts the development of ordinary and dynamic organizational capabilities, even as there has been little joint research on these topics. Second, some research on data governance has suggested that decentralized, bottom-up approaches could be more suitable for large organizations than topdown approaches, which have mainly been at the focus of data governance research. In this single case study I conducted semi-structured interviews with various case company managers. Based on the findings from the interviews and synthesis with literature, I posit that data governance builds support for development of integrative capabilities, such as the ability to communicate efficiently in data related issues across the company. Additionally, dynamic integrative capabilities, such as communication practices aimed for changing the existing data processes, are a central enabler for further development of decentralized data governance. Further analysis also indicated that perception on value of data, top management support, data overview and business unit specific practices, competences and approach to collaboration also impacted the development of data governance in the case company. Based on these findings I present a framework for decentralized data governance. The goal of the framework is to help practitioners and researchers in understanding further the possible interconnections and dynamics of the various factors involved in development of common data governance practices in large, diverse organizations.
The article deals with the problems of cryptographic protection of data processing algorithms and techniques. They are novel techniques allowing to process private information without disclosing it to persons engaged in processing. One of the main applications of such security tools is the creation of personalized information services, which opens up new opportunities for business and reduces the risks of unauthorized access to personal data. We review important building blocks for cryptographic protection of data processing, such as zero-knowledge proofs, secure multi-party computations, and homomorphic encryption. Often, personalized information services are based on data mining and machine learning, so privacy-preserved machine learning is a very important building block for them. We analyze the concept of differential privacy which serves as the basis for privacy-preserving machine learning and some other cryptographic schemes. At the end of the paper, we forecast the perspectives of encrypted data processing.
Cryptocurrencies (e.g., Bitcoin and Ethereum), which promise to become the future of money transactions, are mainly implemented with blockchain technology. However, blockchain suffers from scalability issues. Sharding is the leading solution for blockchain scalability. Sharding splits the blockchain network into sub-chains called shards/committees. Each shard processes a sub-set of transactions, rather than the entire network processing all transactions. This raises security issues for sharding-based blockchain protocols. In this paper, we propose a novel methodology to analyze the security of these protocols (e.g., OmniLedger and RapidChain). In particular, this methodology estimates the failure probability of one sharding round taking into consideration the failure probabilities of all shards. To illustrate the effectiveness of the estimated failure probability, we conduct a numerical analysis of our methodology based on a huge number of trials. Finally, we compute confidence intervals to accurately estimate the failure probability and compare our methodology with existing approaches.
Cyber-Physical-Social System (CPSS) big data is specified as the global historical data which is usually stored in cloud, the local real-time data which is usually stored in the fog-edge server (FeS) of the mobile terminal devices or sensors, and the social data which is usually stored in the social data server (SdS), moreover adopts a centralized access control mechanism to offer users' access strategy which can easily cause CPSS big data to be tampered with and to be leaked. Therefore, a blockchain-based access control scheme called BacCPSS for CPSS big data is proposed. In BacCPSS, account address of the node in blockchain is used as the identity to access CPSS big data, the access control permission for CPSS big data is redefined and stored in blockchain, and processes of authorization, authorization revocation, access control and audit in BacCPSS are designed, and then a lightweight symmetric encryption algorithm is used to achieve privacy-preserving. Finally, a credible experimental model on EOS and Aliyun cloud is built. Results show that BacCPSS is feasible and effective, and can achieve secure access in CPSS while protecting privacy.
Jiawen Kang, Zehui Xiong, Chunxiao Jiang, Yi Liu · 9 authors
The emerging Federated Edge Learning (FEL) technique has drawn considerable attention, which not only ensures good machine learning performance but also solves "data island" problems caused by data privacy concerns. However, large-scale FEL still faces following crucial challenges: (i) there lacks a secure and communication-efficient model training scheme for FEL; (2) there is no scalable and flexible FEL framework for updating local models and global model sharing (trading) management. To bridge the gaps, we first propose a blockchain-empowered secure FEL system with a hierarchical blockchain framework consisting of a main chain and subchains. This framework can achieve scalable and flexible decentralized FEL by individually manage local model updates or model sharing records for performance isolation. A Proof-of-Verifying consensus scheme is then designed to remove low-quality model updates and manage qualified model updates in a decentralized and secure manner, thereby achieving secure FEL. To improve communication efficiency of the blockchain-empowered FEL, a gradient compression scheme is designed to generate sparse but important gradients to reduce communication overhead without compromising accuracy, and also further strengthen privacy preservation of training data. The security analysis and numerical results indicate that the proposed schemes can achieve secure, scalable, and communication-efficient decentralized FEL.
Crowdsourcing is a process wherein an individual or an organisation utilizes the talent pool present over the Internet to accomplish their task. The existing crowdsourcing platforms and their reputation computation are centralised and hence prone to various attacks or malicious manipulation of the data by the central entity. A few distributed crowdsourcing platforms have been proposed but they lack a robust reputation mechanism. So we propose a decentralised crowdsourcing platform having an immutable reputation mechanism to tackle these problems. It is built on top of Ethereum network and does not require the user to trust a third party for a non malicious experience. It also utilizes IOTAs consensus mechanism which reduces the cost for task evaluation significantly.
Federated learning (FL) is an emerging collaborative machine learning method to train models on distributed datasets with privacy concerns. To properly incentivize data owners to contribute their efforts, Shapley Value (SV) is often adopted to fairly assess their contribution. However, the calculation of SV is time-consuming and computationally costly. In this paper, we propose FedCoin, a blockchain-based peer-to-peer payment system for FL to enable a feasible SV based profit distribution. In FedCoin, blockchain consensus entities calculate SVs and a new block is created based on the proof of Shapley (PoSap) protocol. It is in contrast to the popular BitCoin network where consensus entities "mine" new blocks by solving meaningless puzzles. Based on the computed SVs, a scheme for dividing the incentive payoffs among FL clients with nonrepudiation and tamper-resistance properties is proposed. Experimental results based on real-world data show that FedCoin can promote high-quality data from FL clients through accurately computing SVs with an upper bound on the computational resources required for reaching consensus. It opens opportunities for non-data owners to play a role in FL.
Sarah Iqbal, Asad Waqar Malik, Anis Ur Rahman, Rafidah Md Noor
With the widespread adoption of the internet of things (IoT) technologies towards building a smart city, connected devices often offload computation tasks to nearby edge locations (base stations) to reduce overall computation and network delay. However, serving an ever-increasing number of end devices at these traditional edge locations is becoming impossible, subsequently making them fail to deliver the agreed quality of service to all requesting devices. However, the backend cloud data center is available to serve these requests but incurred additional communication delay, thus, unsuitable for delay-sensitive applications. Furthermore, the fact that the underlying network is inherently ad hoc which makes it prone to malicious nodes affecting its overall performance. In this work, we propose a secure fog computing paradigm where roadside units (RSUs) are used to offload tasks to nearby fog vehicles based on repute scores maintained at a distributed blockchain ledger. The experimental results demonstrate a significant performance gain in terms of queuing time, end-to-end delay, and task completion rate when compared to the baseline queuing-based task offloading scheme.
In this paper we continue to speak about the concept of Self-Sovereign Identity (SSI), but not in the cases of IoT devices as it was in previous works [1]. The main purpose of this research is the usage of digital identity in two cases: a) SSI of a single person in Knowledge based system “Experts Ledger” and b) SSI of a company and candidate in HR matching systems. Though these two systems are developed for different issues, the idea of SSI in both is similar. The overview of these systems is done, and the pros and cons of using SSI with the relation of Zero-Knowledge Proof (ZKP) in each of them is made.
Zongyang Zhang, Weihan Li, Haitao Liu, Jianwei Liu
With the continuous development and popularity of blockchain technology, anonymity of cryptocurrency has attracted wide attention. Zcash is an altcoin of Bitcoin aiming to protect blockchain anonymity. Its anonymity is highly guaranteed by zero-knowledge proofs. However, it is still practicable to decrease Zcash's anonymity. In this paper, we provide a refined empirical analysis of Zcash anonymity. We improve current address clustering methods and increase the clustering rate by 9%. We also analyze the whole process of distributing mining reward and identify 87.5% addresses and 25.7% transactions. Besides, we simplify Zcash transaction network and then pick out nodes (edges) which play important roles in network connectivity. We show that these nodes are mostly mining pools. In particular, users participating in shieldedpool are mostly founders, miners and mining pools, although shieldedpool itself is designed for protecting anonymity of users with high privacy requirements. Our results, to an extent, are opposite to the original intention of Zcash.
Kentaroh Toyoda, Jun Zhao, Allan N. Zhang, P. Takis Mathiopoulos
Federated learning (FL) is a promising decentralized deep learning technique that allows users to collaboratively update models without sharing their own data. However, due to its decentralized nature, no one can monitor workers' behavior, and they may thus deviate protocols (e.g., participating without updating any models). To solve this problem, many researchers have proposed blockchain-enabled FL to reward workers (or users) with cryptocurrencies to encourage workers to follow the protocols. However, there is a lack of theoretical discussions concerning how such rewards impact workers' behavior and how much should be given to workers. In this article, we propose a mechanism-design-oriented FL protocol on a public blockchain network. Mechanism design (MD) is often used to make a rule intended to achieve a specific goal. With MD in mind, we introduce the concept of competition into blockchain-based FL so that only workers who have contributed well can obtain rewards, which naturally prevents workers from deviating from the protocol. We then mathematically answer the following questions with contest theory, a novel field of study in economics: i) What behavior will workers take?; ii) how much effort should workers exert to maximize their profits?; iii) how many workers should be rewarded?; and iv) what is the best proportion for reward distribution?
Electronic medical data have significant advantages over paper-based patient records when it comes to storage and retrieval. However, most existing medical data sharing schemes have security risks, such as being prone to data tampering and forgery, and do not support the ability to verify the authenticity of the data source. To solve these problems, we propose a medical data sharing scheme based on attribute cryptosystem and blockchain technology in this paper. First, the encrypted medical data are stored in the cloud, and the storage address and medical-related information are written into the blockchain, which can ensure efficient storage and eliminate the possibility of irreversible modification of the data. Second, the proposed scheme combines attribute-based encryption (ABE) and attribute-based signature (ABS), which achieves the sharing of medical data in many-to-many communications. The ABE achieves data privacy and fine-grained access control, and the ABS verifies the authenticity of the source of the medical data while protecting the signer's identity. Moreover, the data user outsources most of the operations of medical data ciphertext decryption to the cloud service provider (CSP), which can greatly reduce the computational burden. Finally, results of the analysis show that our scheme satisfies the requirements for confidentiality and unforgeability in the random oracle model, and that the proposed scheme offers higher computational performance than other similar schemes.
Hui Li, Lishuang Pei, Dan Liao, Song Chen · 6 authors
Vehicular Ad Hoc Network (VANET) is an important foundation of intelligent transportation system and is widely used in traffic management, automatic driving, and road optimization. With the gradual popularization and further development of VANET, a large amount of VANET data has been produced. However, it poses huge challenges to the security and privacy when using VANET data provides services for users. In this paper, combining the technologies of blockchain and ciphertext-based attribute encryption (CP-ABE), we propose a fine-grained access control scheme for VANET data based on blockchain (FADB). In FADB, we employ the blockchain to replace the third-party service providers for user identity management and data storage. And different VANET data access rights can be established according to user attribute. By improving the CP-ABE, the lightweight VANET devices can outsource complex encryption and decryption operations to powerful RSUs and further improve the efficiency of data access. Final, we carry out a series of simulation tests and security analysis, proving that the FADB can provide effective data security and low performance overhead.