VADP: Visitor-attribute-based adaptive differential privacy for IoMT data sharing

Shaobo Zhang, Lujie Zhang, Tao Peng, Qin Liu, Xiong Li

Abstract: The Internet of Medical Things (IoMT) improves medical services by collecting and sharing patient data, but it also increases the risk of sensitive privacy breaches. To mitigate the risks, existing methods based on personalized differential privacy add different noises to the query results of each data visitor. However, these methods require additional computation to assign a constant privacy budget for each visitor, leading to low sharing efficiency and data utility. To overcome these challenges, this paper proposes a visitor-attribute-based adaptive differential privacy (VADP) data-sharing scheme. The scheme first constructs a quantifiable hierarchical access structure to control visitors’ access to data attributes precisely, and adaptively determines the privacy level for each data attribute by quantifying the matching degree between the visitor attributes and the access structure. To enhance sharing efficiency, the scheme devises a lightweight privacy budget calculation matrix to compute privacy budgets efficiently, reducing computational overhead. Additionally, integrating the VIKOR method enables the scheme to balance data privacy and utility flexibly. Experiments show that regarding the data utility, the VADP scheme reduces the average query error by 39.4% compared with non-adaptive differential privacy methods. It also decreases computational overhead in the data-sharing phase by 40.3% compared to the state-of-the-art schemes.

External IDs:dblp:journals/compsec/ZhangZPLL25