Aug 1, 2019·2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)
Nitin Sukhija, Elizabeth Bautista, Moon Moore, John-George Sample
In recent years, the crowdsourced data analytics have gained an unprecedented interest and adoption from the research communities. In data crowdsourcing, large scientific problems can be solved by utilizing collective human intelligence, where various researchers or groups work on solving various sub-problems. However, the main challenges that encompasses a crowdsourcing system are data access, management, and analysis along with user privacy and trust preservation. In this paper, we present a decentralized data access and control framework to handle data crowdsourcing using blockchain technology. Leveraging on blockchain, our permissioned system enables confidentiality, accountability and traceability of the Crowdsourced data while maintaining control of it via Proof of Stake consensus algorithm. The proposed framework will be employed to crowdsource operational data to the National Energy Research Scientific Computing Center (NERSC) users at Lawrence Berkeley National Laboratory.
By outsourcing most of storage and computational tasks to full nodes, Ethereum lightweight client is able to run on the resource-limited devices such as mobile phones. In the interactions with the lightweight client, the full node obtains considerable information about Ethereum addresses (Ethereum accounts), transactions, and transaction receipts associated with the lightweight client. This behavior obviously violates user privacy. Currently, there is no solution supporting the lightweight client to efficiently retrieve the account state, related transactions, and transaction receipts from the untrust full node without revealing privacy. In this paper, we propose to use private information retrieval (PIR) to design a privacy-preserving Ethereum lightweight client. Since directly using existing PIR scheme significantly increases communication overhead of the lightweight client, we tailor existing PIR schemes to adapt to the lightweight client, which allows the lightweight client to efficiently retrieve account state and transaction data without leaking sensitive query information. Security and performance analysis show that our scheme is capable of efficiently and privately downloading the data of interest to the lightweight client.
Xianglin Bao, Cheng Su, Yan Xiong, Wenchao Huang · 5 authors
Federated learning (shorted as FL) recently proposed by Google is a privacy-preserving method to integrate distributed data trainers. FL is extremely useful due to its ensuring privacy, lower latency, less power consumption and smarter models, but it could fail if multiple trainers abort training or send malformed messages to its partners. Such misbehavior are not auditable and parameter server may compute incorrectly due to single point failure. Furthermore, FL has no incentive to attract sufficient distributed training data and computation power. In this paper, we propose FLChain to build a decentralized, public auditable and healthy FL ecosystem with trust and incentive. FLChain replace traditional FL parameter server whose computation result must be consensual on-chain. Our work is not trivial when it is vital and hard to provide enough incentive and deterrence to distributed trainers. We achieve model commercialization by providing a healthy marketplace for collaborative-training models. Honest trainer can gain fairly partitioned profit from well-trained model according to its contribution and the malicious can be timely detected and heavily punished. To reduce the time cost of misbehavior detecting and model query, we design DDCBF for accelerating the query of blockchain-documented information. Finally, we implement a prototype of our work and measure the cost of various operations.
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.
Recently, the term ‘Internet of Things’ (IoT) has garnered great attention. As a trusted, dependable, and decentralized approach, blockchain has already been used in IoT. However, the existing blockchain has a number of drawbacks that prevent it from being used as a generic platform for IoT. The nodes in IoT are heavily resource-limited, especially computing and networking resources. Unfortunately, they are necessary for the blockchain to solve complicated puzzles and propagate blocks. In this paper, we propose agent mining and cloud mining approaches to solve the above problem in the blockchain-enabled IoT. To be specific, miners act as mining agents for nodes in IoT, offload mining tasks to cloud computing servers, and use networking resources dynamically. Furthermore, in order to enhance the performance, the access selection of users, computing resources allocation, and networking resources allocation are formulated as a joint optimization problem. We then propose a dueling deep reinforcement learning approach to address this problem. Numerical results justify the effectiveness of our proposed scheme.
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
Wattana Viriyasitavat, Li Da Xu, Zhuming Bi, Danupol Hoonsopon
Internet of Things (IoT) refers to networks with billions of physical devices for collecting, sharing, and utilizing data in the virtual world. Most of IoT applications centralize security assurance in creating, authenticating, transferring, or delating system components. However, the centralization exposes its limitations to meet security needs of a rapidly growing number of things world-widely. How to scale up the applications with assured security becomes a critical challenge. Blockchain technology (BCT) is a promising solution to provide security and protect privacy in a large scale; especially, smart contracts offer opportunities to improve the reliability of IoT applications. Smart contracts establish trusts for both of data and executed processes. Recently, many literature surveys and positioning articles have been published on the integration of BCT with IoT, but they are limited to superficial discussions of technical potentials, and very few of them have a thorough exploration of the challenges in developing BCT for IoT at technical levels. This paper uses the system design approach to scrutinize the state of the art of study on BCT-based applications and clarify critical research areas of enabling BCT for security assurance: 1) the relations of BCT and IoT are modeled and discussed; 2) the needs of eliminating threats in IoT-based applications are defined as functional requirements (FRs), existing works on enabling technologies of BCT are defined as the physical solutions (PSs); and 3) the mappings between FRs and PSs are established to identify the limitations and the critical areas for the applications of BCT in large-scale distributed environment.
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.
With the blockchain receiving extensive attention in recent years, many storage schemes based on the blockchain have been proposed as alternative means of cloud storage for data outsourcing. However, the conventional access control methods in the current sharing schemes require either individual permission granting via symmetric keys or a trusted central attribute authority for ciphertext-policy attribute-based encryption (CP-ABE). Moreover, due to the transparency of the blockchain, the data query logs are recorded in public ledgers. Nevertheless, the problem of privacy protection for data consumers has not been properly addressed. In this paper, we propose a fully decentralized data storage and sharing system on a blockchain by using multi-authority CP-ABE and decentralized multi-authority attribute-based signatures (DMA-ABSs). In our system, every party can securely store and share its data with a set of individuals satisfying a policy with no need to grant separate permissions individually. Additionally, the data owners can fully control their data, know how their data are accessed due to the nature of the blockchain and have the ability to opt-out at any time. The public ledger of the blockchain provides immutable logs of data address pointers, access policies, attribute public keys and data queries. In addition, data consumers' attributes are publicly verifiable through the DMA-ABS scheme without revealing more private information. Finally, the combination of the multi-authority CP-ABE with the blockchain guarantees the integrity, confidentiality, and accessibility of the data without the need for trusted third parties, such as a central authority or a data center.
Elli Androulaki, Angelo De, Matthias Neugschwandtner, Alessandro Sorniotti
Hyperledger Fabric, the popular open-source permissioned blockchain platform, contains a mechanism called endorsement for establishing the validity of a transaction. In this paper we take a comprehensive look at endorsements in Fabric by reviewing their definition, security model and role in Fabric. We then review motivation, architecture and design for the recently introduced state-based endorsement. This feature permits developers of smart contracts to set endorsement policies at the level of individual state variables, enabling more fine-grained security policies. We further introduce cryptographic schemes for privacy-preserving endorsements that hide the identity of an endorser and ensure unlinkability across multiple endorsements. We present security model and analysis for both approaches, as well as benchmarks to evaluate the practicality of the proposed schemes.
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.
Nowadays, massive healthcare data is collected for analysis and diagnosis from various applications and devices in mobile healthcare. To manage these data, existing work proposed centralized data management while this causes unexpected security threats, such as single point of failure and typical DDoS attack. For secure data management, we leverage blockchain technology and present a decentralized data management system based on consortium blockchain, called by HealChain. Specifically, network-wide healthcare data is processed by consortium blockchain nodes (CBNs) from collection, verification and recording in a strictly distributed manner. To realize HealChain, a hierarchical architecture with three layers is elaborately designed to facilitate network functionalities. After that, an operation procedure is put forward for ensuring considerate interactions between users and CBNs. Moreover, we formulate an optimization problem for a CBN to maximize the economic benefit in the mining procedure by optimizing the computing power. Genetic algorithm is utilized to solve the problem. Security analysis and numerical results demonstrate that HealChain is effective and efficient to achieve secure data management for mobile healthcare.
The crowdsourcing schemes which utilize the social network to solve complex tasks are an important part of open cooperation over the Internet. Although blockchain-based crowdsourcing schemes have considerable advantages in decentralization and data sharing, there is still a challenge to gurantee the security of crowdsourced-sensitive information and the fairness of crowdsourcing on the blockchain. To this end, this article investigates a crowdsourcing scheme based on blockchain. First, we define the basic requirements of blockchain-based crowdsourcing schemes including fairness, confidentiality, and integrity. And then, using secure hash, commitment, and homomorphic encryption, we propose a blockchain-based secure and fair crowdsourcing scheme, that is, BFC. The analysis results show that our scheme can satisfy the above requirements. Finally, the experimental results show that the computational overhead of the BFC scheme is acceptable to both the requester and the workers. In a word, our proposed crowdsourcing scheme has good expansibility in reality.
Recently, leading research communities have been investigating the use of blockchains for Artificial Intelligence (AI) applications, where multiple participants, or agents, collaborate to make consensus decisions. To achieve this, the data in the blockchain storage have to be transformed into blockchain knowledge. We refer to these types of blockchains as knowledge-based blockchains. Knowledge-based blockchains are potentially useful in building efficient risk assessment applications. An earlier work introduced probabilistic blockchain which facilitates knowledge-based blockchains. This paper proposes an extension for the probabilistic blockchain concept. The design of a reputation management framework, suitable for such blockchains, is proposed. The framework has been developed to suit the requirements of a wide range of applications. In particular, we apply it to the detection of malicious nodes and reduce their effect on the probabilistic blockchains' consensus process. We evaluate the framework by comparing it to a baseline using several adversarial strategies. Further, we analyze the collaborative decisions with and without the malicious node detection. Both results show a sustainable performance, where the proposed work outperforms others and achieves excellent results.
Access control technology is an important information security mechanism. At present, most of the database systems and enterprise information systems are role-based access control technologies, this rights management system has been running stably. However, due to the simple role access control, its flexibility and control granularity sometimes can't meet the requirements of actual access control. This paper proposes a secure access control model ARBACV1 based on RBACV1 combined with ABAC model, which is more flexible than RBACV1 and can perform fine-grained access control. The open transparency of data in the blockchain has caused people's high attention to data privacy protection issues[1]. A complete access control mechanism has not been provided in the Ethereum blockchain. To this end, according to the blockchain architecture, the proposed access control model ARBACV1 is applied to the blockchain through smart contracts, and the access of the blockchain users is controlled securely, and the code is written in Solidity language in Ethereum[2]. ARBACV1-based access control is implemented in blockchain.
Blockchain has been treated as one of the most promising technologies to promote crowdsourcing by providing new nice features, such as decentralization and accountability. Unfortunately, some inherent limitations of blockchain have been rarely addressed by the most existing works when applying blockchain into crowdsourcing, which becomes the performance bottleneck of crowdsourcing systems. In this paper, we propose a novel hybrid blockchain crowdsourcing platform to achieve decentralization and privacy preservation. Our platform integrates with a hybrid blockchain structure, dual-ledgers, and dual consensus algorithms to ensure secure communication between the requesters and the workers. Moreover, the smart contract and zero-knowledge proof are employed to ensure automatic operation of the tasks and the protection users' privacy, respectively. Finally, we conduct experiments to confirm the efficiency of the adopted consensus algorithm on our platform by comparing it with the state-of-the-art.
IoT devices produce a lot of valuable and sensitive data that is often shared with external parties to provide different kinds of useful services. Traditional IoT access control systems are centralized and do not include all the stakeholders in the access control decision making process. To fill this gap, we propose a permissioned blockchain based access control system for IoT where a different phase of access control like creating access policy and making the access control decision happens based on the consensus of all the stakeholders. To be more specific, we design and implement Attribute Based Access Control (ABAC) in a permissioned blockchain called Hyperledger Fabric and leverage its smartcontract and distributed consensus to enable a distributed access control for IoT. The effectiveness of our proposed system is demonstrated by the performance evaluation result in an IoT testbed.
Shan Jiang, Jiannong Cao, Julie A. McCann, Yanni Yang · 7 authors
Recent research has demonstrated searchable blockchains that not only provide reliable search over encrypted distributed storage systems but ensure privacy is preserved. Yet, current solutions focus on single-keyword search over encrypted data on the blockchain. To extend such approaches to multi-keyword scenarios, they essentially perform a single-keyword search for multiple times and take the intersection of the results. However, such extensions suffer from privacy and efficiency issues. In particular, the service peers, which process the search requests, will be aware of the intermediate results, which include the data associated with each of the encrypted keywords. Moreover, these multiple traversals incur long delays in performing the search requests one after another with an extra cost in calculating the intersection of multiple sets. Finally, the service peers will charge the data owner a lot for writing the vast intermediate results to the smart contract. In this paper, we propose a bloom filter-enabled multi-keyword search protocol with enhanced efficiency as well as privacy preservation. In the protocol, a low-frequency keyword selected by a bloom filter will be used to filter the database when performing a multi-keyword search operation. Because the keyword is of low frequency, the majority of the data will be excluded from the result, which reduces the computational cost significantly. Moreover, we propose to use pseudorandom tags to facilitate completing each search operation in only one round. In this way, no intermediate results are generated, and the privacy is preserved. Finally, we implement the protocol in a local simulated blockchain network and conduct extensive experiments. The results indicate that our multi-keyword search protocol outperforms the traditional method with an average of 14.67% less time delay and 59.96% less financial cost.
The sharing economy has made great inroads with services like Uber or Airbnb enabling people to share their unused resources with those needing them. The computing world, however, despite its abundance of excess computational resources has remained largely unaffected by this trend, save for few examples like SETI@home. We present DeCloud, a decentralized market framework bringing the sharing economy to on-demand computing where the offering of pay-as-you-go services will not be limited to large companies, but ad hoc clouds can be spontaneously formed on the edge of the network. We design incentive compatible double auction mechanism targeted specifically for distributed ledger trust model instead of relying on third-party auctioneer. DeCloud incorporates innovative matching heuristic capable of coping with the level of heterogeneity inherent for large-scale open systems. Evaluating DeCloud on Google cluster-usage data, we demonstrate that the system has a near-optimal performance from an economic point of view, additionally enhanced by the flexibility of matching.