# Privacy-Preserving Matrix Factorization for Recommendation Systems using Gaussian Mechanism

arXiv: https://arxiv.org/abs/2304.09096 · HTML: https://mlsanity.com/paper/2304.09096/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 30 references that are in the map.
- First posted on arXiv 11 Apr 2023.

## Builds on (most cited first, 5 of 5)

- [Renyi Differential Privacy](https://mlsanity.com/paper/1702.07476/) (2017)
- [The Noisy Power Method: A Meta Algorithm with Applications](https://mlsanity.com/paper/1311.2495/) (2013)
- [Fast Differentially Private Matrix Factorization](https://mlsanity.com/paper/1505.01419/) (2015)
- [Differentially Private Distributed Constrained Optimization](https://mlsanity.com/paper/1411.4105/) (2014)
- [Distributed Differentially-Private Algorithms for Matrix and Tensor Factorization](https://mlsanity.com/paper/1804.10299/) (2018)

## Abstract (arXiv)

Building a recommendation system involves analyzing user data, which can potentially leak sensitive information about users. Anonymizing user data is often not sufficient for preserving user privacy. Motivated by this, we propose a privacy-preserving recommendation system based on the differential privacy framework and matrix factorization, which is one of the most popular algorithms for recommendation systems. As differential privacy is a powerful and robust mathematical framework for designing privacy-preserving machine learning algorithms, it is possible to prevent adversaries from extracting sensitive user information even if the adversary possesses their publicly available (auxiliary) information. We implement differential privacy via the Gaussian mechanism in the form of output perturbation and release user profiles that satisfy privacy definitions. We employ R'enyi Differential Privacy for a tight characterization of the overall privacy loss. We perform extensive experiments on real data to demonstrate that our proposed algorithm can offer excellent utility for some parameter choices, while guaranteeing strict privacy.