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Blockchain-based Data Sharing System for Sensing-as-a-Service in Smart Cities

Chao Lin, Debiao He, Sherali Zeadally, Xinyi Huang, Zhe Liu

2020ACM Transactions on Internet Technology17 citationsDOI

Abstract

The sensing-as-a-service (SaaS) model has been explored to address the challenge of intractability of managing a large number of sensors faced by future smart cities. However, how to effectively share sensor data without compromising confidentiality, privacy protection, and fair trading without third parties is one of critical issues that must be solved in the SaaS in smart cities. While blockchain shows promise in solving these issues, the existing blockchain-based data sharing (BBDS) systems are difficult to apply to SaaS in smart cities because of many unresolved issues such as requiring a customized blockchain, huge storage, communication and computation costs, and dependence on a third party to achieve fair trading. We propose a BBDS system model with its security requirements before we present a concrete construction by combining -protocol, Paillier encryption scheme, and any secure symmetrical encryption and signature schemes. To demonstrate the utility of our proposed BBDS system, we present a security analysis and compare our system with other solutions. We implement the prototype in Remix to analyze the gas cost, and we conduct experiments to evaluate the communication and computation costs of the BBDS system using symmetric encryption (advanced encryption standard (AES)) and a signature scheme (elliptic curve digital signature algorithm (ECDSA)).

Topics & Concepts

Computer scienceEncryptionComputer securityDigital signatureBlockchainPaillier cryptosystemData sharingCloud computingElliptic Curve Digital Signature AlgorithmScheme (mathematics)Public-key cryptographyElliptic curve cryptographyHybrid cryptosystemOperating systemMedicineHash functionMathematical analysisAlternative medicinePathologyMathematicsBlockchain Technology Applications and SecurityCryptography and Data SecurityPrivacy-Preserving Technologies in Data
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