Efficient User-Centric Privacy-Friendly and Flexible Wearable Data Aggregation and Sharing

TL;DR

This paper introduces SAMA, a novel scheme for privacy-preserving, flexible, and user-centric data aggregation and sharing in wearable healthcare systems, addressing limitations of existing solutions.

Contribution

SAMA combines multi-key partial homomorphic encryption and ciphertext-policy attribute-based encryption to enable efficient, fine-grained, privacy-preserving data sharing for multiple users and requesters.

Findings

SAMA supports data confidentiality and authorization.

SAMA demonstrates improved efficiency over state-of-the-art solutions.

Experimental results confirm its practicality for wearable healthcare data sharing.

Abstract

Wearable devices can offer services to individuals and the public. However, wearable data collected by cloud providers may pose privacy risks. To reduce these risks while maintaining full functionality, healthcare systems require solutions for privacy-friendly data processing and sharing that can accommodate three main use cases: (i) data owners requesting processing of their own data, and multiple data requesters requesting data processing of (ii) a single or (iii) multiple data owners. Existing work lacks data owner access control and does not efficiently support these cases, making them unsuitable for wearable devices. To address these limitations, we propose a novel, efficient, user-centric, privacy-friendly, and flexible data aggregation and sharing scheme, named SAMA. SAMA uses a multi-key partial homomorphic encryption scheme to allow flexibility in accommodating the aggregation of data originating from a single or multiple data owners while preserving privacy during the processing. It also uses ciphertext-policy attribute-based encryption scheme to support fine-grain sharing with multiple data requesters based on user-centric access control. Formal security analysis shows that SAMA supports data confidentiality and authorisation. SAMA has also been analysed in terms of computational and communication overheads. Our experimental results demonstrate that SAMA supports privacy-preserving flexible data aggregation more efficiently than the relevant state-of-the-art solutions.