On Differentially Private Online Collaborative Recommendation Systems
This addresses privacy concerns for users in recommendation systems, but it is incremental as it builds on existing differential privacy frameworks.
The paper tackles the problem of privacy in online collaborative recommendation systems by analyzing trade-offs between recommendation quality and differential privacy, showing a lower bound and proposing a near-optimal algorithm, with results indicating little trade-off for non-trivial algorithms.
In collaborative recommendation systems, privacy may be compromised, as users' opinions are used to generate recommendations for others. In this paper, we consider an online collaborative recommendation system, and we measure users' privacy in terms of the standard differential privacy. We give the first quantitative analysis of the trade-offs between recommendation quality and users' privacy in such a system by showing a lower bound on the best achievable privacy for any non-trivial algorithm, and proposing a near-optimal algorithm. From our results, we find that there is actually little trade-off between recommendation quality and privacy for any non-trivial algorithm. Our results also identify the key parameters that determine the best achievable privacy.