PrAd

Smart phones and mobile devices have become more and more ubiquitous recently. This ubiquity gives chance for mobile advertising, especially location-based advertising, to develop into a very promising market. In many location-based advertising services, it is implied that service providers would obtain actual locations of users in order to serve relevant advertisements which are near users' current locations. However, this practice has raised a significant privacy concern as various private information of an user can be inferred based on her locations and trajectories. In this work, we propose PrAd, a location-based advertising model that appreciates users' location privacy; i.e. it never reveals their locations to any untrusted party. Our solution is conceptualized based on several state-of-the-art privacy preserving techniques such as data obfuscation, space encoding and private information retrieval (PIR). We especially introduce algorithmic modification to existing hardware-based PIR technique to make it more practical and thus suit real-time applications. Moreover, PrAd enables a correct billing mechanism among involved parties without revealing any individual sensitive information. Finally, we confirm the effectiveness of our proposed framework by evaluating its performance using a real world dataset.