QSDA : Quality-Aware Secure Multi-Dimensional Data Aggregation with Location Privacy for HIoT

Wang, Yijun and Wu, Lei and Su, Ye and Wang, Hao and Meng, Weizhi and Liu, Zhiquan (2026) QSDA : Quality-Aware Secure Multi-Dimensional Data Aggregation with Location Privacy for HIoT. IEEE Internet of Things Journal. ISSN 2327-4662

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Abstract

Data aggregation, as a data processing technique, facilitates accurate diagnosis in the Healthcare Internet of Things (HIoT) by integrating multi-source heterogeneous health data. However, achieving efficient and secure aggregation of multi-dimensional medical data remains challenging, particularly when simultaneously preserving location privacy and providing fair, quality-driven incentives. To address these issues, this paper proposes a Quality-Aware Secure Multi-Dimensional Data Aggregation scheme with Location Privacy for HIoT (QSDA). First, the scheme employs inner product encryption to support aggregation task matching without revealing users’ actual coordinates, and further integrates symmetric homomorphic encryption with super-increasing sequences to enable one-stop compressed aggregation of multi-dimensional data, thereby effectively supporting common statistical operations such as mean and variance. Second, it introduces a data quality incentive mechanism based on offset metrics, while leveraging blockchain auditing to ensure the traceability of the aggregation process and the verifiability of the aggregation results. Finally, security analysis and performance evaluation demonstrate the scheme’s effectiveness and efficiency.