Data privacy is a huge concern nowadays. In the context of location based services, a very important issue regards protecting the position of users issuing queries. Strong location privacy renders the user position indistinguishable from any other location. This necessitates that every query, independently of its location, should retrieve the same amount of information, determined by the query with the maximum requirements. Consequently, the processing cost and the response time are prohibitively high for datasets of realistic sizes. In this thesis, we propose a novel solution that offers both strong location privacy and efficiency by adjusting the accuracy of the query results. Our framework seamlessly combines the concepts of ∈-differential privacy and private information retriev...[ Read more ]

Data privacy is a huge concern nowadays. In the context of location based services, a very important issue regards protecting the position of users issuing queries. Strong location privacy renders the user position indistinguishable from any other location. This necessitates that every query, independently of its location, should retrieve the same amount of information, determined by the query with the maximum requirements. Consequently, the processing cost and the response time are prohibitively high for datasets of realistic sizes. In this thesis, we propose a novel solution that offers both strong location privacy and efficiency by adjusting the accuracy of the query results. Our framework seamlessly combines the concepts of ∈-differential privacy and private information retrieval (PIR), exploiting query statistics to increase efficiency without sacrificing privacy. We experimentally show that the proposed approach outperforms the current state-of-the-art by orders of magnitude, while introducing only a small bounded error.