High Performance Private Recommender Systems



In current recommender systems user rating data is linked to specific users. If the server is compromised or the service provider chooses to share this data (even if "anonymised") then that data can release unexpected information, e.g. by combining IMDB review data with the anonymised netflix ratings users were deanonymised. In BLC users submit ratings using group identities, known as nyms. Each group contains many thousands of users and so it is v hard to guess which ratings were submitted by any specific user. This provides a strong "hiding in the crowd" type of privacy. Importantly, it also gives state of the art recommendation performance i.e. there is no trade-off between accuracy and privacy here.

Summary of BLC recommender system.

Code

Send me an email if you'd like python or matlab code implementing the BLC recommender.

Private Search

Code

Firefox/Chrome plugin (experimental!) by Kris Vanhoutte.