Multi-Selection for Recommendation Systems

However, these practices can lead to significant privacy risks, including data exploitation Barocas and Nissenbaum [2014], re-identification threats Narayanan and Shmatikov [2008], and surveillance concerns Lyon [2014]. To address these issues, several privacy-preserving techniques have been proposed, including differential privacy McSherry and Mironov [2009], federated learning Ammad-Ud-Din et al. [2019], homomorphic encryption Kim et al. [2016], privacy-preserving matrix factorization Hua and Xiong [2015], and K-anonymity Polat and Du [2005]. Despite their potential, these methods often face challenges such as reduced utility, computational complexity, and communication overhead. In this work, we explore a privacy-preserving recommendation system where user queries are protected using differential privacy within the local trust model Bebensee [2019], with a focus on balancing the trade-offs between utility and privacy. In the local trust model, user queries and user features are changed from the original to preserve privacy (typically by adding noise), which can lead to less accurate results from the server. To mitigate this issue, Goel et al. [2024] introduced the concept of multi-selection, where the server returns multiple results, allowing the user to select the most relevant one without disclosing its Supported by NSF awards CCF-2113798 and IIS-2402823. 1 arXiv:2504.07403v1