Privacy-aware personalization for mobile advertising

Mobile advertising is an increasingly important driver in the Internet economy. We point out fundamental trade-offs between important variables in the mobile advertisement ecosystem. In order to increase relevance, ad campaigns tend to become more targeted and personalized by using context information extracted from user's interactions and smartphone's sensors. This raises privacy concerns that are hard to overcome due to the limited resources (energy and bandwidth) available on the phones. We point out that in the absence of a trusted third party, it is impossible to maximize these three variables - ad relevance, privacy, and efficiency - in a single system. This leads to the natural question: can we formalize a common framework for personalized ad delivery that can be instantiated to any desired trade-off point? We propose such a flexible ad-delivery framework where personalization is done jointly by the server and the phone. We show that the underlying optimization problem is NP-hard and present an efficient algorithm with a tight approximation guarantee.