Privacy-preserving Targeted Advertising
This work addresses privacy concerns for users in online advertising, though it is incremental as it builds on existing association rule methods.
The paper tackles the problem of privacy breaches in collaborative filtering-based targeted advertising by proposing a system where user profiles are stored on-device and recommendations are fetched efficiently using association rules, achieving a 60% reduction in communication overhead.
Recommendation systems form the center piece of a rapidly growing trillion dollar online advertisement industry. Even with numerous optimizations and approximations, collaborative filtering (CF) based approaches require real-time computations involving very large vectors. Curating and storing such related profile information vectors on web portals seriously breaches the user's privacy. Modifying such systems to achieve private recommendations further requires communication of long encrypted vectors, making the whole process inefficient. We present a more efficient recommendation system alternative, in which user profiles are maintained entirely on their device, and appropriate recommendations are fetched from web portals in an efficient privacy preserving manner. We base this approach on association rules.