Location Privacy

This lecture covers the toolbox for privacy engineering related to location privacy, including tools for quantifying and mitigating location-related inferences. It explores the challenges of protecting location privacy, the importance of understanding trust assumptions, and practical issues in safeguarding user whereabouts. Various techniques such as homomorphic encryption, k-anonymity, L-diversity, and subsampling are discussed. The lecture also delves into protecting location privacy through perturbation, hiding, generalization, and adding dummies, highlighting the pitfalls of k-anonymity cloaking. Additionally, it addresses the measurement of privacy, the sensitivity of aggregate location data, and the implications of different privacy mechanisms like spatial obfuscation and peer-to-peer cloaking.