Privacy-preserving Targeted Advertising [article]

Theja Tulabandhula, Shailesh Vaya, Aritra Dhar
2018 arXiv   pre-print
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
more » ... ors, 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.
arXiv:1710.03275v2 fatcat:3ncwkclgrffx5na7ihrkbqblxm