Towards Privacy in Decentralized IoT: A Blockchain-Based Dual Response DP Mechanism - Kai Zhang; Pei-Wei Tsai; Jiao Tian; Wenyu Zhao; Xinyi Cai; Longxiang Gao; Jinjun Chen (Big Data Mining and Analytics)
Differential Privacy (DP) stands as a secure and efficient mechanism for privacy preservation, offering enhanced data utility without compromising computational complexity. Its adaptability is evidenced by its integration into blockchain-based Internet of Things (IoT) contexts, including smart wearables, smart homes, etc. Nevertheless, a notable vulnerability surfaces in decentralized environments where existing DP mechanisms falter in withstanding collusion attacks. This vulnerability stems from the absence of an efficient strategy to synchronize the privacy budget consumption and historical query information among all network participants. Adversaries can exploit this weakness, collaborating to inject a substantial volume of queries simultaneously into disparate blockchain nodes to extract more precise results. To address this issue, we propose a novel dual response DP mechanism to preserve privacy in blockchain-based IoT scenarios. It encompasses both direct and indirect response strategies, enabling an adaptive response to external queries, aiming to provide better data utility while preserving privacy. Additionally, this mechanism can synchronize historical query information and privacy budget consumption within the blockchain network to prevent privacy leakage. We employ Relative Error (RE), Mean Square Error (MSE), and privacy budget consumption as evaluation metrics to measure the performance of the proposed mechanism. Experimental outcomes substantiate that the proposed mechanism can adapt to blockchain networks well, affirming its capacity for privacy and great utility.