Two Differentially Private Rating Collection Mechanisms for Recommender Systems
arXiv:1604.08402v1
Originality Synthesis-oriented
AI Analysis
This work addresses privacy concerns in recommender systems for users and developers, but it appears incremental as it adapts existing mechanisms rather than introducing a fundamentally new approach.
The paper tackles the problem of collecting user ratings in recommender systems while preserving privacy, proposing two differentially private mechanisms (modified Laplace and randomized response) that maintain data utility.
We design two mechanisms for the recommender system to collect user ratings. One is modified Laplace mechanism, and the other is randomized response mechanism. We prove that they are both differentially private and preserve the data utility.