Ali Khoshgozaran

TL;DR: This work proposes a novel framework to support private location-dependent queries, based on the theoretical work on Private Information Retrieval (PIR), which achieves stronger privacy for snapshots of user locations and is the first to provide provable privacy guarantees against correlation attacks.

TL;DR: The results show that the results very closely approximate the result set generated from performing KNN queries in the original space while enforcing the new location privacy metrics termed u-anonymity and a- anonymity, which are stronger and more generalized privacy measures than the commonly used K-anonymsity and cloaked region size measures.

TL;DR: This paper proposes a fundamental approach based on private information retrieval to process range and K-nearest neighbor queries, the prevalent queries used in many location-based services, with stronger privacy guarantees compared to those of the cloaking and anonymity approaches.

TL;DR: A set of fundamental approaches based on private information retrieval to process range and k-nearest neighbor queries, the elemental queries used in many Location Based Services, with significantly stronger privacy guarantees as opposed to cloaking or anonymity approaches are reviewed.

TL;DR: This work revisits the location privacy problem with the objective of providing significantly more stringent privacy guarantees and proposes SPIRAL, a scalable private information retrieval approach to location privacy, which is to the best of the knowledge, the first approach to utilize practicalPrivate information retrieval (PIR) as a more fundamental approach to enable blind evaluation of range queries.