Privacy-preserving Targeted Advertising
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Recommendation systems form the center piece of a rapidly growing trillion dollar online advertisement industry. Even with optimizations/approximations, collaborative filtering (CF) 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. While achieving private recommendations in this setup 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.
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