Privacy-Preserving Data Aggregation In Wireless Body Area Networks

Wireless Body Area Networks (WBANs), as a promising health-care system, can provide tremendous benefits for timely and continuous patient care and remote health monitoring. Owing to the restriction of communication, computation and power in WBANs, cloud assisted WBANs, which offer more reliable, intelligent, and timely health-care services for mobile users and patients, are receiving increasing attention. However, how to aggregate the health data multifunctionally and efficiently is still an open issue to the cloud server (CS).This thesis proposes a privacy-preserving and multifunctional health data aggregation mechanism (PPM-HDA) with fault tolerance for cloud assisted WBANs. With PPM-HDA, the CS can compute multiple statistical functions of users’health data in a privacy-preserving way to offer various services. Specifically, this thesis first proposes a multifunctional health data additive aggregation scheme MHDA+ to support additive aggregate functions such as average, variance and one-way analysis of variance. Then, I put forward MHDA(?) as an extension of MHDA+ to support non-additive aggregations such as max/min, median, percentile and histogram.PPM-HDA can resist differential attacks, which most existing data aggregation schemes suffer from. The security analysis shows that PPM-HAD can protect users’ privacy against many threats. Performance evaluations illustrate that the computational overhead of MHDA+ is significantly reduced with the assistance of CSs. The proposed MHDA(?) scheme is more efficient than previously reported max/min aggregation schemes in terms of communication overhead when the applications require large plaintext space and highly-accurate data. In addition, scalability analysis presents that PPM-HAD can support fault tolerance, temporal and spatial aggregation and adapt to dynamic users.