Towards Smart Healthcare: Patient Data Privacy and Security in Sensor‐Cloud Infrastructure

TLDR

Wireless body area networks combined with cloud computing form a sensor‑cloud infrastructure that enables real‑time patient monitoring and early disease diagnosis, but its distributed nature introduces new threats to patient data privacy and security. This paper reviews the techniques for protecting patient data privacy and security within sensor‑cloud infrastructure. The authors categorize existing methods—multibiometric key generation, pairwise key establishment, hash functions, attribute‑based encryption, chaotic maps, hybrid encryption, Number Theory Research Unit, Tri‑Mode Algorithm, Dynamic Probability Packet Marking, and Priority‑Based Data Forwarding—according to application area, and propose a six‑step generic framework for safeguarding patient physiological parameters that involves selecting preliminaries, system entities, techniques, accessing data, analyzing security, and estimating performance. The review outlines the pros and cons of each technique chronologically, examines the performance evolution of the research area, and concludes with open challenges and future directions.

Abstract

Nowadays, wireless body area networks (WBANs) systems have adopted cloud computing (CC) technology to overcome limitations such as power, storage, scalability, management, and computing. This amalgamation of WBANs systems and CC technology, as sensor‐cloud infrastructure (S‐CI), is aiding the healthcare domain through real‐time monitoring of patients and the early diagnosis of diseases. Hence, the distributed environment of S‐CI presents new threats to patient data privacy and security. In this paper, we review the techniques for patient data privacy and security in S‐CI. Existing techniques are classified as multibiometric key generation, pairwise key establishment, hash function, attribute‐based encryption, chaotic maps, hybrid encryption, Number Theory Research Unit, Tri‐Mode Algorithm, Dynamic Probability Packet Marking, and Priority‐Based Data Forwarding techniques, according to their application areas. Their pros and cons are presented in chronological order. We also provide our six‐step generic framework for patient physiological parameters (PPPs) privacy and security in S‐CI: (1) selecting the preliminaries; (2) selecting the system entities; (3) selecting the technique; (4) accessing PPPs; (5) analysing the security; and (6) estimating performance. Meanwhile, we identify and discuss PPPs utilized as datasets and provide the performance evolution of this research area. Finally, we conclude with the open challenges and future directions for this flourishing research area.