Privacy Preservation in Pervasive Computing Environments

Abstract

With the widespread adoption of location-based services it is vital to emphasize location privacy to prevent unexpected and unwanted location disclosure. However, privacy preservation often introduces challenges such as increased computational complexity, communication overhead, or reduced data precision. To address these challenges, this work explores novel protocols in secure multi-party computation (SMPC) to enable accurate and efficient solutions within the domains of location sharing, traffic aggregation, contact tracing, and crowdsensing without sacrificing privacy. In location sharing, the protocols are developed for kNN, range and point queries ensuring location privacy is maintained. In traffic aggregation secure multiparty computation is developed to aggregate near future traffic data in a privacy preserving manner. In contact tracing, privacy-preserving mechanisms are developed to detect potential exposure events without revealing users' movement patterns. In crowdsensing applications, protocols are developed to safeguard location privacy from the task assignment phase through payment for task completion, ensuring participant location privacy throughout the process. In addition to privacy preserving protocols, this work looks at the privacy of servers through the use of novel distributed architecture and protocols for firewall evaluation and management. Lastly, this work looks at the current state of privacy preservation in pervasive environments and the potential of these techniques in future technologies for preserving users’ privacy.