Differential privacy preserving based framework using blockchain for internet-of-things

被引:4
作者
Kashif, Muhammad [1 ,2 ]
Kalkan, Kubra [1 ]
机构
[1] Engn Fac Ozyegin Univ, Dept Comp Sci, Istanbul, Turkiye
[2] Natl Univ Sci & Technol, Coll Adv Technol, Muscat, Oman
关键词
Blockchain; Consensus mechanism; Distributed system; Internet of things; Security; Authentication; MIoT; SCHEME;
D O I
10.1007/s12083-024-01858-w
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The Internet of Things (IoT) has enabled the collection of vast amounts of data that can be used to improve various aspects of our lives. However, the astronomical volume of data generated by these IoT devices has raised significant concerns pertaining to privacy preservation. The amalgamation of the Internet of Things (IoT) with blockchain technology has engendered a promising solution for securing and managing IoT data, but it is still susceptible to privacy breaches. Recently, differential privacy (DP) has been proposed as a promising technique to alleviate these issues. In this paper, we design and propound a complete end-to-end blockchain-based architecture by implementing differential privacy at the stream level generated by IoT devices by deploying Laplace noise and Gaussian noise utilizing low complex cryptography mechanism and fast convergence consensus protocol to surmount the privacy preservation issues in IoT based blockchain network. Our novel DP-based framework introduces the concept of privacy levels as low, medium, and high as set by the data owner and also analyzes the impact of different parameters on the effectiveness of the approach and provides recommendations for tuning them. The workflow of our proposed framework consists of three phases: Data generation phase, Data Sharing phase, and Data Analysis phase. During the Data generation phase, the data owner will first determine the desired level of privacy protection (low, medium, high) and set the privacy budget (epsilon) and sensitivity (delta) of the data. Based on the budget value, the privacy module will generate noise from either Laplace or Gaussian distribution as requested by the data owner. The Data Sharing phase is mainly responsible for transmitting and processing the transactions inside the blockchain network. This is followed by the data analysis phase, which will check for the budget value and the amount of noise added to the data before the noisy data is handed over to the end user. We demonstrate the efficacy of our approach through multiple experimental evaluations and simulation results evince that our approach attains high levels of privacy preservation while upholding data utility and blockchain consistency. Overall, our proposed framework provides a promising solution to the privacy challenges in IoT-based blockchain systems, offering adjustable privacy levels to accommodate different privacy requirements. This DP-based approach and the adjustable privacy levels ensure alignment with the growing regulatory requirements for data privacy, such as GDPR, demonstrating compliance with these regulations and building trust with customers.
引用
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页码:1 / 23
页数:23
相关论文
共 52 条
[1]  
Al Omar R., 2019, IEEE Access, V7, P5622
[2]   Internet of Things security: A survey [J].
Alaba, Fadele Ayotunde ;
Othman, Mazliza ;
Hashem, Ibrahim Abaker Targio ;
Alotaibi, Faiz .
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS, 2017, 88 :10-28
[3]  
Alam T, 2020, Arxiv, DOI arXiv:1904.00226
[4]   A Deep Blockchain Framework-Enabled Collaborative Intrusion Detection for Protecting IoT and Cloud Networks [J].
Alkadi, Osama ;
Moustafa, Nour ;
Turnbull, Benjamin ;
Choo, Kim-Kwang Raymond .
IEEE INTERNET OF THINGS JOURNAL, 2021, 8 (12) :9463-9472
[5]   A Trustworthy Privacy Preserving Framework for Machine Learning in Industrial IoT Systems [J].
Arachchige, Pathum Chamikara Mahawaga ;
Bertok, Peter ;
Khalil, Ibrahim ;
Liu, Dongxi ;
Camtepe, Seyit ;
Atiquzzaman, Mohammed .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2020, 16 (09) :6092-6102
[6]  
Arshad J., 2021, Futur Gener Comput Syst, V118, P131
[7]  
ConsenSys, 2024, Quorum whitepaper
[8]   Ancile: Privacy-preserving framework for access control and interoperability of electronic health records using blockchain technology [J].
Dagher, Gaby G. ;
Mohler, Jordan ;
Milojkovic, Matea ;
Marella, Praneeth Babu .
SUSTAINABLE CITIES AND SOCIETY, 2018, 39 :283-297
[9]   Blockchain-Based Privacy Enforcement in the IoT Domain [J].
Daidone, Federico ;
Carminati, Barbara ;
Ferrari, Elena .
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING, 2022, 19 (06) :3887-3898
[10]   A Survey of Blockchain-Based Strategies for Healthcare [J].
De Aguiar, Erikson Julio ;
Faical, Bruno S. ;
Krishnamachari, Bhaskar ;
Ueyama, Jo .
ACM COMPUTING SURVEYS, 2020, 53 (02)