1 research outputs found
How Much Are You Willing to Share? A "Poker-Styled" Selective Privacy Preserving Framework for Recommender Systems
Most industrial recommender systems rely on the popular collaborative
filtering (CF) technique for providing personalized recommendations to its
users. However, the very nature of CF is adversarial to the idea of user
privacy, because users need to share their preferences with others in order to
be grouped with like-minded people and receive accurate recommendations. While
previous privacy preserving approaches have been successful inasmuch as they
concealed user preference information to some extent from a centralized
recommender system, they have also, nevertheless, incurred significant
trade-offs in terms of privacy, scalability, and accuracy. They are also
vulnerable to privacy breaches by malicious actors. In light of these
observations, we propose a novel selective privacy preserving (SP2) paradigm
that allows users to custom define the scope and extent of their individual
privacies, by marking their personal ratings as either public (which can be
shared) or private (which are never shared and stored only on the user device).
Our SP2 framework works in two steps: (i) First, it builds an initial
recommendation model based on the sum of all public ratings that have been
shared by users and (ii) then, this public model is fine-tuned on each user's
device based on the user private ratings, thus eventually learning a more
accurate model. Furthermore, in this work, we introduce three different
algorithms for implementing an end-to-end SP2 framework that can scale
effectively from thousands to hundreds of millions of items. Our user survey
shows that an overwhelming fraction of users are likely to rate much more items
to improve the overall recommendations when they can control what ratings will
be publicly shared with others