PPM-HDA: Privacy-Preserving and Multifunctional Health Data Aggregation With Fault Tolerance

Abstract

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). In this paper, we propose 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, we first propose a multifunctional health data additive aggregation scheme (MHDA + ) to support additive aggregate functions such as average and variance. Then we put forward MHDA ⊕ as an extension of MHDA + to support non-additive aggregations such as min/max, median, percentile and histogram. PPM-HDA can resist differential attacks, which most existing data aggregation schemes suffer from. The security analysis shows that PPM-HDA can protect users' privacy against many threats. Performance evaluations illustrate that the computational overhead of MHDA + is significantly reduced with the assistance of CSs. Our MHDA ⊕ scheme is more efficient than previously reported min/max aggregation schemes in terms of communication overhead when the applications require large plaintext space and highly-accurate data.

Security and Privacy in the Medical Internet of Things: A Review

“…Because of the convenience and low cost, a number of devices and software services rely heavily on wireless networks, such as WiFi, which are known to be vulnerable to various intrusions including unauthorized router access, man-in-the-middle attacks, spoofing, denial of service attacks, brute-force attacks, and traffic injections [30]. In addition, most free wireless networks in public place, which have not been certified, are untrusted networks [31].…”

Re‐ADP: Real‐Time Data Aggregation with Adaptive ω‐Event Differential Privacy for Fog Computing

“…Some researchers suggest exploiting differential privacy, a convincing model for providing privacy, to protect aggregated data generated by IoT devices. Han et al in [37] proposed a scheme to provide privacy preserving for health data aggregation. It employs a differential privacy model to resist differential attacks that most existing data aggregation schemes have suffered from.…”

Privacy-preserving data aggregation in resource-constrained sensor nodes in Internet of Things: A review

“…Since sensor node life (SL) is inversely proportional to CC and CO while EC is directly proportional to CC and CO. [20] High Small Small High Yes High Zhang et al [21] High Small Small High Yes High Therefore Hayouni et al [5] have small SL and high energy consumption (EC). Finster and Baumgart [11] high SL and medium EC.…”

“…Moreover, the algorithms proposed in Refs. [17,19,20] use PKC both for encryption and signature and thus due to their very high computational cost SL is very small and consume a lot of energy ( Table 2).…”