An Optimized Privacy-Utility Tradeoff Framework for Differentially Private Data Sharing in Blockchain-Based Internet of Things

Muhammad Islam, Mubashir Husain Rehmani, Longxiang Gao, Jinjun Chen

Abstract: Differential private (DP) query and response mechanisms have been widely adopted in various applications based on Internet of Things (IoT) to leverage variety of benefits through data analysis. The protection of sensitive information is achieved through the addition of noise into the query response which hides the individual records in a dataset. However, the noise addition negatively impacts the accuracy which gives rise to privacy-utility tradeoff. Moreover, the DP budget or cost $\epsilon $ is often fixed and it accumulates due to the sequential composition which limits the number of queries. Therefore, in this article, we propose a framework known as optimized privacy-utility tradeoff framework for data sharing in IoT (OPU-TF-IoT). First, OPU-TF-IoT uses an adaptive approach to utilize the DP budget $\epsilon $ by considering a new metric of population or dataset size along with the query. Second, our proposed heuristic search algorithm (HSA) reduces the DP budget accordingly whereas satisfying both data owner and data user. Third, to make the utilization of DP budget transparent to the data owners, a blockchain-based verification mechanism is also proposed. Finally, the proposed framework is evaluated using real-world datasets and compared with the traditional DP model and other related state-of-the-art works. The results demonstrate that our proposed framework not only utilizes the DP budget $\epsilon $ efficiently, but also optimizes the number of queries by 49% and 54% on average compared to state-of-the-art and standard DP models, respectively. Furthermore, the data owners can effectively make sure that their data are shared accordingly through our blockchain-based verification mechanism which encourages them to share their data into the IoT system.