No description available.

This thesis looks at the problem of discovering nearby friends and nearby places of interest in a privacy-preserving way using location-based services on mobile devices (e.g., smartphones). First, we propose a privacy-preserving protocol for the discovery of nearby friends. In this scenario, Alice wants to verify whether any of her friends are close to her or not. This should be done without disclosing any information about Alice to her friends and also any of the other parties’ information to Alice. We also demonstrate that our approach can be efficiently applied to other similar problems; in particular, we use it to provide a solution to the socialist millionaires' problem. Second, we propose a privacy-preserving protocol for discovering nearby places of interest. In this scenario, the proposed protocol allows Alice to learn whether there is any place that she is looking for near her. However, the location-based service (LBS) that tries to help Alice to find nearby places does not learn Alice’s location. Alice can send a request to the LBS database to retrieve nearby places of interest (POIs) without the database learning what Alice fetched by using private information retrieval (PIR). Our approach reduces the client side computational overhead by applying the grid square system and the POI types ideas to block-based PIR schemes to make it suitable for LBS smartphone applications. We also show our second approach is flexible and can support all types of block-based PIR schemes. As an item of independent interest, we also propose the idea of adding a machine learning algorithm to our nearby friends’ Android application to estimate the validity of a user's claimed location to prevent users from sending a fake location to the LBS application.