Mobile crowdsensing (MCS) is a promising paradigm of large-scale sensing. A group of mobile users is recruited with their smart devices to accomplish various sensing tasks in specific areas. The mobility and intelligence of mobile users enable MCS to achieve a sufficient coverage ratio of sensing tasks or areas. Currently, MCS is generally proposed and implemented in a centralized way under a platformâs control. However, this centralized structure is vulnerable to a single point of failure. The platformâs failure leads to a shutdown of the entire system. In addition, there is a trust issue between the platform and mobile users because of computational transparency and financial security. It is possible that the platform manipulates the working process of MCS to obtain an improper gain. To overcome these problems, we propose a decentralized MCS framework, named ChainSensing, by leveraging blockchain. In ChainSensing, mobile users interact with blockchain via smart contracts to complete their operations, e.g., publishing sensing tasks and submitting collected data. Since there are computationally intensive problems in ChainSensing, e.g., path planning, path selection, and reward determination, it is significantly expensive to solve such problems in blockchain. Therefore, we propose to leverage smart devices and computing oracles to solve these problems. Specifically, we propose a heuristic algorithm to solve the path planning problem in smart devices of mobile users; we employ computing oracles to solve the path selection and reward determination problems. Finally, we conduct numerical simulations based on Ethereum to evaluate the performance of ChainSensing.
This perspectives article surveys the most promising privacy-preserving cryptographic technologies including secure multiparty computation, zero-knowledge proofs and fully homomorphic encryption, and their various real-world applications.
Oliver Stengele, Markus Raiber, JĂśrn MĂźllerâQuade, Hannes Hartenstein
We address the Threshold Information Disclosure (TID) problem on Ethereum: An arbitrary number of users commit to the scheduled disclosure of their individual messages recorded on the Ethereum blockchain if and only if all such messages are disclosed. Before a disclosure, only the original sender of each message should know its contents. To accomplish this, we task a small council with executing a distributed generation and threshold sharing of an asymmetric key pair. The public key can be used to encrypt messages which only become readable once the threshold-shared decryption key is reconstructed at a predefined point in time and recorded on-chain. With blockchains like Ethereum, it is possible to coordinate such procedures and attach economic stakes to the actions of participating individuals. In this paper, we present ETHTID, an Ethereum smart contract application to coordinate Threshold Information Disclosure. We base our implementation on ETHDKG [1], a smart contract application for distributed key generation and threshold sharing, and adapt it to fit our differing use case as well as add functionality to oversee a scheduled reconstruction of the decryption key. For our main cost saving optimisation, we show that the security of the underlying cryptographic scheme is maintained. We evaluate how the execution costs depend on the size of the council and the threshold and show that the presented protocol is deployable on Ethereum with a council of more than 200 members with gas savings of 20-40% compared to ETHDKG.
Shichang Xuan, Ming Jin, Xin Li, Zhaoyuan Yao ¡ 6 authors
The rapid development in network technology has resulted in the proliferation of Internet of Things (IoT). This trend has led to a widespread utilization of decentralized data and distributed computing power. While machine learning can benefit from the massive amount of IoT data, privacy concerns and communication costs have caused data silos. Although the adoption of blockchain and federated learning technologies addresses the security issues related to collusion attacks and privacy leakage in data sharing, the âfree-rider attacksâ and âmodel poisoning attacksâ in the federated learning process require auditing of the training models one by one. However, that increases the communication cost of the entire training process. Hence, to address the problem of increased communication cost due to node security verification in the blockchain-based federated learning process, we propose a communication cost optimization method based on security evaluation. By studying the verification mechanism for useless or malicious nodes, we also introduce a double-layer aggregation model into the federated learning process by combining the competing voting verification methods and aggregation algorithms. The experimental comparisons verify that the proposed model effectively reduces the communication cost of the node security verification in the blockchain-based federated learning process.
Abstract With the large-scale application of cloud storage systems, lots of attribute-based access control (ABAC) schemes have been introduced to protect data and user security in this insecure environment. ABAC can make data owners control their own data and protect data security and privacy. However, there are two serious privacy leakage problems, namely user attribute privacy and policy privacy, should be solved in the process of constructing ABAC schemes. In this paper, an ABAC scheme supporting privacy protection is constructed, which can solve privacy leakage problems of user attribute privacy and policy privacy in large universe attribute system. Furthermore, a framework is proposed. The framework combines our ABAC scheme, the Ethereum blockchain and blockchain-based storage system. In this framework, the security characteristics of blockchain technology are used to realize decentralization, tamper-resistant and avoiding single point of failure. Besides, the problems of attribute revocation and policy updating are solved by smart contract on the Ethereum blockchain. Finally, we established an initial implementation on Linux and Rinkeby test network, and the experimental results show that our scheme is feasible.
Victoria L. Lemieux, Artemij Voskobojnikov, Meng Kang
Self-sovereign identity (SSI) systems are novel blockchain-based solutions that are said to shift the control of data records from organizations to individuals. Contrary to conventional blockchains, such as Bitcoin or Ethereum, many SSI systems do not capture on ledger the exchange of transactional data between individuals. By not capturing the exchange of transaction data such SSI systems have the advantage of complying with privacy regulations such as the EUâs General Data Protection Regulations, but, at the same time, have the disadvantage of not capturing evidence that an exchange has happened. Such evidence, however, may be needed for audit and accountability purposes. To achieve these objectives and to preserve privacy, we leverage archival principles to introduce a novel concept of a proof registry, which we define as a set of technical components, data structures, and process flows, that assures that authoritative records offering evidence of transactions is captured, stored, and accessible. This solution solves the compliance and accountability problem while preserving the self-sovereignty and privacy of involved parties.
As the pioneer of blockchain technology, Bitcoin is the most famous cryptocurrency to date. Given its dramatic price swings along with constant news and events, the dynamics of cryptocurrencies are difficult to quantify. These dynamics are believed to be affected by various political, security, financial, and regulatory events. This paper presents the Impact-Score metric, an efficient framework that attempts to quantify the impact of events on the Bitcoin blockchain. We have applied our framework to 16 major worldwide events and the Bitcoin blockchain network (defined as Bitcoin transaction and users, blockchain data, and memory pool data) from 2016-2018. The results show that the majority of the events are correlated with quantifiable changes in the Bitcoin blockchain and network. We also observed correlations between event types (e.g. financial events) and event subtypes on certain Bitcoin blockchain network temporal features.
Newly emerging and evolving technologies such as Cloud, Fog and Edge Computing, as well as Internet of Things, Cyber-Physical Systems and Distributed Ledger Technology (such as blockchain) together with advances in Artificial Intelligence (AI) research are increasingly becoming a common and pervasive phenomenon in our everyday lives. Their co-evolution with society is driving the emergence of future socio-technical systems, which further promote ubiquitous entanglement between humans and machines. Fog, Edge and Dew computing as post-Cloud computing paradigms aim to relocate computing resources closer to end users in order to mitigate cloud-specific issues of highly centralized computation. Dew computing as the youngest of the post-cloud paradigms promotes human centered independence and collaboration between devices within scalable distributed computing infrastructures. Meanwhile, the field of artificial intelligence is adapting to recent challenges posed by user data privacy regulations as well as opportunities for applications on mobile devices based on their growing computational abilities. The usage of artificial intelligence in pervasive and scalable distributed computing systems is a natural step towards ubiquitous intelligent infrastructures and collaborative human and machine environments. Federated learning is an artificial intelligence technique enabling collaborative learning in distributed devices environment without sharing the training data sets, which are often private. This paper provides the overview of the federated learning paradigm showing that it inherently leverages both independence and collaboration, thus exemplifying implementation of dew intelligence within scalable distributed computing hierarchy.
Blockchain's immutability can resist unauthorized changes of ledgers, thus it can be used as a trust enhancement mechanism to a shared system. Indeed, blockchain has been considered to solve the security and privacy issues of the Internet of Things (IoT). In this regard, most researches currently focus on the realization of various access control models and architectures, and are working towards making full use of the blockchain to secure IoT systems. It is worth noting that there has been an increasingly heavy pressure on the blockchain storage caused by dealing with massive IoT data and handling malicious access behaviors in the system, and not many countermeasures have been seen to curb the increase. However, this problem has not been paid enough attention. In this paper, we implement an attribute-based access control scheme using smart contracts in Quorum blockchain. It provides basic access control functions and conserves storage by reducing the number of smart contracts. In addition, a reputation-based technique is introduced to cope with malicious behaviors. Certain illegal transactions can be blocked by the credit-assessment algorithm, which deters possibly malicious nodes and gives more chance to well-behaved nodes. The feasibility of our proposed scheme is demonstrated by doing experiment on a testbed and conducting a case study. Finally, the system performance is assessed based on experimental measurement.
Modern mobile applications often produce decentralized data, i.e., a huge amount of privacy-sensitive data distributed over a large number of mobile devices. Techniques for learning models from decentralized data must properly handle two natures of such data, namely privacy and massive engagement. Federated learning (FL) is a promising approach for such a learning task since the technique learns models from data without exposing privacy. However, traditional FL methods assume that the participating mobile devices are honest volunteers. This assumption makes traditional FL methods unsuitable for applications where two kinds of participants are engaged: 1) self-interested participants who, without economical stimulus, are reluctant to contribute their computing resources unconditionally, and 2) malicious participants who send corrupt updates to disrupt the learning process. This paper proposes Refiner, a reliable federated learning system for tackling the challenges introduced by massive engagements of self-interested and malicious participants. Refiner is built upon Ethereum, a public blockchain platform. To engage self-interested participants, we introduce an incentive mechanism which rewards each participant in terms of the amount of its training data and the performance of its local updates. To handle malicious participants, we propose an audit scheme which employs a committee of randomly chosen validators for punishing them with no reward and preclude corrupt updates from the global model. The proposed incentive and audit scheme is implemented with cryptocurrency and smart contract, two primitives offered by Ethereum. This paper demonstrates the main features of Refiner by training a digit classification model on the MNIST dataset.
Abstract This research work aims to investigate Blockchain technology and GDPR compliance studies. This will analyze the data privacy perspective with respect to distributed ledger technology. Blockchain has become one of the most frequently discussed technologies for its ability to allow for peer-to-peer transactions without a centralized intermediary. The GDPR was implemented in May 2018 for EU member states to maintain data privacy. DLT, the underlying technology of blockchain as is a decentralized system without any monetary authority. This research conducted a thorough literature review on prior conducted research to investigate the problems and determine the gaps of GDPR compliance with blockchain technologies and discuss the technical, use-case designs or solutions that make blockchain more compliant GDPR in terms of privacy. This systematic literature review addresses the gaps, feasibility, efficiency, and data privacy issues on compatibility problems that are primarily concerned with how a distributed ledger technology system in which recorded data or transaction cannot be changed or erased is challenging the GDPR data subject access rights (DSAR), where every data subjectâs personal data which is compliant to GDPR has a right to exercise their Rights to rectify, delete or limit the processing of your personal data at any time if necessary.
Sonia Alice George, Steffie Maria Stephen, Arunita Jaekel
A vehicular ad hoc network (VANET) consists of vehicles, roadside units, and other infrastructures that communicate with each other with the goal of improving road safety, reducing accidents, and alleviating traffic congestion. For safe and secure operation of critical applications in VANET, it is essential to ensure that only authenticated vehicles can participate in the network. Another important requirement for VANET communication is that the privacy of vehicles and their users must be protected. Privacy can be improved by using pseudonyms instead of actual vehicle identities during communication. However, it is also necessary to ensure that these pseudonyms can be linked to the real vehicle identities if needed, in order to maintain accountability. In this paper, we propose a new blockchain-based decentralized pseudonym management scheme for VANET. This allows the vehicles to maintain conditional anonymity in the network. The blockchain is used to maintain a record of each vehicle and all of its pseudo-IDs. The information in the blockchain can only be accessed by authorized entities and is not available to all vehicles. The proposed distributed framework maintains an immutable record of the vehicle data, which is not vulnerable to a single point of failure. We compared the performance of the proposed approach with a traditional PKI scheme and shown that it significantly reduces the authentication delay.
The rise of Internet of Things (IoT) technology promotes the rapid development of location services industry. The idea of smart connectivity also provides a new direction for Location-Based Social Networks (LBSNs). However, due to limited calculate ability and internal storage space of IoT devices, historical location data of users is generally stored in the central server, which is likely to cause the disclosure of usersâ private data. In this paper, we propose a Blockchain-enabled Privacy-Preserving Location Sharing (B-PPLS) scheme, which is a new framework that not only protects user location privacy but also provides effective location sharing services for users. For B-PPLS, location data owners can share the location area instead of location coordinates to Requesters, in order to realize the location privacy preserving. Also, the Merkle hash tree is utilized to divide the location area, so as to realize the multilevel privacy preserving. Furthermore, four algorithms are proposed to achieve the four stages of initialization, location record, location sharing, and location verification, respectively. Finally, we analyze the security of the proposed B-PPLS scheme and compare the performance with other related location privacy-preserving schemes by experimental evaluation.
Deep neural networks (DNNs) could be very useful in blockchain applications such as DeFi and NFT trading. However, training / running large-scale DNNs as part of a smart contract is infeasible on today's blockchain platforms, due to two fundamental design issues of these platforms. First, blockchains nowadays typically require that each node maintain the complete world state at any time, meaning that the node must execute all transactions in every block. This is prohibitively expensive for computationally intensive smart contracts involving DNNs. Second, existing blockchain platforms expect smart contract transactions to have deterministic, reproducible results and effects. In contrast, DNNs are usually trained / run lock-free on massively parallel computing devices such as GPUs, TPUs and / or computing clusters, which often do not yield deterministic results. This paper proposes novel platform designs, collectively called A New Hope (ANH), that address the above issues. The main ideas are (i) computing-intensive smart contract transactions are only executed by nodes who need their results, or by specialized serviced providers, and (ii) a non-deterministic smart contract transaction leads to uncertain results, which can still be validated, though at a relatively high cost; specifically for DNNs, the validation cost can often be reduced by verifying properties of the results instead of their exact values. In addition, we discuss various implications of ANH, including its effects on token fungibility, sharding, private transactions, and the fundamental meaning of a smart contract.
Amira Kchaou, Ryma Abassi, Samiha Ayed, Sihem Guemara El Fatmi
Recently, the Vehicular Ad-hoc network (VANET) has progressively gained attention from both industry and research with the rapid development of wireless communication technology and intelligent vehicles. The vehicles exchange messages with other entities but cannot share the resources between them. Therefore, security is required in some scenarios including integrity, traceability, confidentiality, notarization of exchanged information as well as access control. In order to provide a secure vehicle communication and access control, we propose an ABAC access control model using smart contract on the blockchain. The use of the blockchain facilitates the sharing of secure messages among vehicles. Moreover, vehicles are able to share the resources with each other exploiting the access control policy on the XACML standard. Then, we evaluate the access response time and the storage overhead of the proposal.
Jingwei Ye, Xin Kang, YingâChang Liang, Sumei Sun
Blockchain is a promising technology for future dynamic spectrum access (DSA) management due to its decentralization, immutability, and traceability. However, many challenges need to be addressed to integrate the blockchain to DSA, such as the trustworthiness of participating nodesâ spectrum sensing results, privacy protection of sensing nodesâ identities, and affordable lightweight consensus algorithms for IoT devices. In this article, we propose a trust-centric privacy-preserving blockchain for DSA in IoT networks. To be specific, we propose a trust evaluation mechanism to evaluate the trustworthiness of sensing nodes and design a Proof-of-Trust (PoT) consensus mechanism to build a scalable blockchain with high transaction-per-second (TPS). Moreover, a privacy protection scheme is proposed to protect sensorsâ real-time geolocation information when they upload sensing data to the blockchain. Two smart contracts are designed to make the whole procedure (spectrum sensing, spectrum auction, and spectrum allocation) run automatically. Simulation results demonstrate the expected computation cost of the PoT consensus algorithm for reliable nodes is low, and the cooperative sensing performance is improved with the help of the trust evaluation mechanism. In addition, incentivization and security are also analyzed, which show that our system can not only encourage nodesâ participation, but also resist many kinds of attacks which are frequently arise in the trust management mechanism and blockchain-based IoT systems.
Jiarui Zhang, Yaodong Huang, Fan Ye, Yuanyuan Yang
Edge computing guides the collaborative work of widely distributed nodes with different sensing, storage, and computing resources. For example, sensor nodes collect data and then store it in storage nodes so that computing nodes can access the data when needed. In this paper, we focus on the quality of service (QoS) in storage allocation in edge networks. We design a reputation mechanism for nodes in edge networks, which enables interactive nodes to evaluate the quality of service for reference. Each node publicly broadcasts a personal reputation list to evaluate all other nodes, and each node can calculate the global reputation of all nodes by aggregating personal reputations. We then propose a storage allocation algorithm that stores data to appropriate locations. The algorithm considers fairness, efficiency, and reliability which is derived from reputations. We build a novel Proof-of-Reputation (PoR) blockchain to support consensus on the reputation mechanism and storage allocation. The PoR blockchain ensures safety performance, saves computing resources, and avoids centralization. Extensive simulation results show our proposed algorithm is fair, efficient, and reliable. The results also show that in the presence of attackers, the success rate of honest nodes accessing data can reach 99.9%.
Three-dimensional (3D) data are easily collected in an unconscious way and are sensitive to lead biological characteristics exposure. Privacy and ownership have become important disputed issues for the 3D data application field. In this paper, we design a privacy-preserving computation system (SPPCS) for sensitive data protection, based on distributed storage, trusted execution environment (TEE) and blockchain technology. The SPPCS separates a storage and analysis calculation from consensus to build a hierarchical computation architecture. Based on a similarity computation of graph structures, the SPPCS finds data requirement matching lists to avoid invalid transactions. With TEE technology, the SPPCS implements a dual hybrid isolation model to restrict access to raw data and obscure the connections among transaction parties. To validate confidential performance, we implement a prototype of SPPCS with Ethereum and Intel Software Guard Extensions (SGX). The evaluation results derived from test datasets show that (1) the enhanced security and increased time consumption (490 ms in this paper) of multiple SGX nodes need to be balanced; (2) for a single SGX node to enhance data security and preserve privacy, an increased time consumption of about 260 ms is acceptable; (3) the transaction relationship cannot be inferred from records on-chain. The proposed SPPCS implements data privacy and security protection with high performance.
This paper introduces a blockchain-based federated learning (FL) framework with incentives for participating nodes to enhance the accuracy of classification problems. Machine learning technology has been rapidly developed and changed from a global perspective for the past few years. The FL framework is based on the Ethereum blockchain and creates an autonomous ecosystem, where nodes compete to improve the accuracy of classification problems. With privacy being one of the biggest concerns, FL makes use of the blockchain-based approach to ensure privacy and security. Another important technology that underlies the FL framework is zero-knowledge proofs (ZKPs), which ensure that data uploaded to the network are accurate and private. Basically, ZKPs allow nodes to compete fairly by only submitting accurate models to the parameter server and get rewarded for that. We have conducted an analysis and found that ZKPs can help improve the accuracy of models submitted to the parameter server and facilitate the honest participation of all nodes in FL.
Yuhao Bai, Qin Hu, Seung-Hyun Seo, Kyubyung Kang ¡ 5 authors
Smart cities have become a trend with improved efficiency, resilience, and sustainability, providing citizens with high quality of life. With the increasing demand for a more participatory and bottomâup governance approach, citizens play an active role in the process of policy making, revolutionizing the management of smart cities. In the example of urban infrastructure maintenance, the public participation demand is more remarkable as the infrastructure condition is closely related to their daily life. Although blockchain has been widely explored to benefit data collection and processing in smart city governance, public engagement remains a challenge. In this article, we propose a novel public participation consortium blockchain system for infrastructure maintenance that is expected to encourage citizens to actively participate in the decision-making process and enable them to witness all administrative procedures in a real-time manner. To that aim, we introduced a hybrid blockchain architecture to involve a verifier group, which is randomly and dynamically selected from the public citizens, to verify the transaction. In particular, we devised a private-prior peer-prediction-based truthful verification mechanism to tackle the collusion attacks from public verifiers. Then, we specified a Stackelberg-game-based incentive mechanism for encouraging public participation. Finally, we conducted extensive simulations to reveal the properties and performances of our proposed blockchain system, which indicates its superiority over other variations.
Navin V. Keizer, Fan Yang, Ioannis Psaras, George Pavlou
Initiatives such as blockchains and decentralized storage networks are pushing for a decentralized Web3 to replace the current architecture. At the core of Web3 are network resource sharing services, which allow anyone to sell spare network capacity in return for rewards. These services require a way to establish trust, as parties are potentially malicious. This can be achieved by reputation systems. In this paper we make the case for using deep reinforcement learning in Web3 reputation calculation. More specifically, we propose a model which allows for decentralized calculation of scores with high personalization for the user.
Sukrutha L. T. Vangipuram, Saraju P. Mohanty, Elias Kougianos
With the world facing the new virus SARS-CoV-2, many countries have introduced instant Internet applications to identify people carrying the infection. Internet-of-Medical-Things (IoMT) have proven useful in collecting medical data as well in tracing an individual carrying the virus. The data collected or traced belongs to an individual and should be revealed to themselves and hospital providers, but not to any third-party unauthorized agencies. In this paper we use an off-chain distributed storage solution for loading large medical data sets and a blockchain implementation to securely transfer the data from the infected person to the hospital system using the edge infrastructure, and call it CoviChain. The Coronavirus Disease (COVID-19) statistics are loaded on to the edge, and moved to InterPlanetary File Systems (IPFS) storage to retrieve the hash of the data file. Once the hash is obtained, it is moved to the blockchain by means of smart contracts. As the information is being hashed twice, CoviChain addresses the security and privacy issues and avoid exposing individuals' data while achieving larger data storage on the blockchain with reduced cost and time.