Biblio

The problem of maintaining the privacy of sensitive healthcare data is crucial yet the significance of research efforts achieved still need robust development in privacy protection techniques for Wireless Multimedia Sensor Networks (WMSNs). This paper aims to investigate different privacy-preserving methods for WMSNs that can be applied in healthcare, to guarantee a privacy-aware transmission of multimedia data between sensors and base stations. The combination of ant colony optimization-based routing and hierarchical structure of the network have been proposed in the AntSensNet WMSN-based routing protocol to offer QoS and power efficient multipath multimedia packet scheduling. In this paper, the AntSensNet routing protocol was extended by utilizing privacy-preserving mechanisms thus achieving anonymity / pseudonymity, unlinkability, and location privacy. The vulnerability of standard AntSensNet routing protocol to privacy threats have raised the need for the following privacy attacks’ countermeasures: (i) injection of fake traffic, which achieved anonymity, privacy of source and base locations, as well as unlinkability; (ii) encrypting and correlating the size of scalar and multimedia data which is transmitted through a WMSN, along with encrypting and correlating the size of ants, to achieve unlinkability and location privacy; (iii) pseudonyms to achieve unlinkability. The impact of these countermeasures is assessed using quantitative performance analysis conducted through simulation to gauge the overhead of the added privacy countermeasures. It can be concluded that the introduced modifications did enhance the privacy but with a penalty of increased delay and multimedia jitter. The health condition of a patient determines the vitals to be monitored which affects the volumes and sources of fake traffic. Consequently, desired privacy level will dictate incurred overhead due to multimedia transmissions and privacy measures.

Nowadays, ubiquitous healthcare monitoring applications are becoming a necessity. In a pervasive smart healthcare system, the user's location information is always transmitted periodically to healthcare providers to increase the quality of the service provided to the user. However, revealing the user's location will affect the user's privacy. This paper presents a novel cloud-based secure location privacy-preserving mobile healthcare framework with decision-making capabilities. A user's vital signs are sensed possibly through a wearable healthcare device and transmitted to a cloud server for securely storing user's data, processing, and decision making. The proposed framework integrates a number of features such as machine learning (ML) for classifying a user's health state, and crowdsensing for collecting information about a person's privacy preferences for possible locations and applying such information to a user who did not set his privacy preferences. In addition to location privacy preservation methods (LPPM) such as obfuscation, perturbation and encryption to protect the location of the user and provide a secure monitoring framework. The proposed framework detects clear emergency cases and quickly decides about sending a help message to a healthcare provider before sending data to the cloud server. To validate the efficiency of the proposed framework, a prototype is developed and tested. The obtained results from the proposed prototype prove its feasibility and utility. Compared to the state of art, the proposed framework offers an adaptive context-based decision for location sharing privacy and controlling the trade-off between location privacy and service utility.