Decentralized Matrix Factorization with Heterogeneous Differential Privacy

• URL: http://arxiv.org/abs/2212.00306v2
• Date: Sun, 17 Sep 2023 03:19:23 GMT
• Title: Decentralized Matrix Factorization with Heterogeneous Differential Privacy
• Authors: Wentao Hu and Hui Fang
• Abstract summary: We propose a novel Heterogeneous Differentially Private Matrix Factorization algorithm (denoted as HDPMF) for untrusted recommender. Our framework uses modified stretching mechanism with an innovative rescaling scheme to achieve better trade off between privacy and accuracy.
• Score: 2.4743508801114444
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Conventional matrix factorization relies on centralized collection of users' data for recommendation, which might introduce an increased risk of privacy leakage especially when the recommender is untrusted. Existing differentially private matrix factorization methods either assume the recommender is trusted, or can only provide a uniform level of privacy protection for all users and items with untrusted recommender. In this paper, we propose a novel Heterogeneous Differentially Private Matrix Factorization algorithm (denoted as HDPMF) for untrusted recommender. To the best of our knowledge, we are the first to achieve heterogeneous differential privacy for decentralized matrix factorization in untrusted recommender scenario. Specifically, our framework uses modified stretching mechanism with an innovative rescaling scheme to achieve better trade off between privacy and accuracy. Meanwhile, by allocating privacy budget properly, we can capture homogeneous privacy preference within a user/item but heterogeneous privacy preference across different users/items. Theoretical analysis confirms that HDPMF renders rigorous privacy guarantee, and exhaustive experiments demonstrate its superiority especially in strong privacy guarantee, high dimension model and sparse dataset scenario.

Related papers

• Enhancing Feature-Specific Data Protection via Bayesian Coordinate Differential Privacy [55.357715095623554]
  Local Differential Privacy (LDP) offers strong privacy guarantees without requiring users to trust external parties. We propose a Bayesian framework, Bayesian Coordinate Differential Privacy (BCDP), that enables feature-specific privacy quantification.
  arXiv  Detail & Related papers  (2024-10-24T03:39:55Z)
• Unified Mechanism-Specific Amplification by Subsampling and Group Privacy Amplification [54.1447806347273]
  Amplification by subsampling is one of the main primitives in machine learning with differential privacy. We propose the first general framework for deriving mechanism-specific guarantees. We analyze how subsampling affects the privacy of groups of multiple users.
  arXiv  Detail & Related papers  (2024-03-07T19:36:05Z)
• Causal Inference with Differentially Private (Clustered) Outcomes [16.166525280886578]
  Estimating causal effects from randomized experiments is only feasible if participants agree to reveal their responses. We suggest a new differential privacy mechanism, Cluster-DP, which leverages any given cluster structure. We show that, depending on an intuitive measure of cluster quality, we can improve the variance loss while maintaining our privacy guarantees.
  arXiv  Detail & Related papers  (2023-08-02T05:51:57Z)
• A Randomized Approach for Tight Privacy Accounting [63.67296945525791]
  We propose a new differential privacy paradigm called estimate-verify-release (EVR) EVR paradigm first estimates the privacy parameter of a mechanism, then verifies whether it meets this guarantee, and finally releases the query output. Our empirical evaluation shows the newly proposed EVR paradigm improves the utility-privacy tradeoff for privacy-preserving machine learning.
  arXiv  Detail & Related papers  (2023-04-17T00:38:01Z)
• Privacy-Preserving Matrix Factorization for Recommendation Systems using Gaussian Mechanism [2.84279467589473]
  We propose a privacy-preserving recommendation system based on the differential privacy framework and matrix factorization. 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.
  arXiv  Detail & Related papers  (2023-04-11T13:50:39Z)
• Breaking the Communication-Privacy-Accuracy Tradeoff with $f$-Differential Privacy [51.11280118806893]
  We consider a federated data analytics problem in which a server coordinates the collaborative data analysis of multiple users with privacy concerns and limited communication capability. We study the local differential privacy guarantees of discrete-valued mechanisms with finite output space through the lens of $f$-differential privacy (DP) More specifically, we advance the existing literature by deriving tight $f$-DP guarantees for a variety of discrete-valued mechanisms.
  arXiv  Detail & Related papers  (2023-02-19T16:58:53Z)
• PrivMVMF: Privacy-Preserving Multi-View Matrix Factorization for Recommender Systems [0.190365714903665]
  We propose a new privacy-preserving framework based on homomorphic encryption, Privacy-Preserving Multi-View Matrix Factorization (PrivMVMF) PrivMVMF is successfully implemented and tested thoroughly with the MovieLens dataset.
  arXiv  Detail & Related papers  (2022-09-29T03:21:24Z)
• Algorithms with More Granular Differential Privacy Guarantees [65.3684804101664]
  We consider partial differential privacy (DP), which allows quantifying the privacy guarantee on a per-attribute basis. In this work, we study several basic data analysis and learning tasks, and design algorithms whose per-attribute privacy parameter is smaller that the best possible privacy parameter for the entire record of a person.
  arXiv  Detail & Related papers  (2022-09-08T22:43:50Z)
• Decentralized Stochastic Optimization with Inherent Privacy Protection [103.62463469366557]
  Decentralized optimization is the basic building block of modern collaborative machine learning, distributed estimation and control, and large-scale sensing. Since involved data, privacy protection has become an increasingly pressing need in the implementation of decentralized optimization algorithms.
  arXiv  Detail & Related papers  (2022-05-08T14:38:23Z)
• One-Bit Matrix Completion with Differential Privacy [6.409622409155275]
  We propose a novel framework for one-bit matrix completion under the differential privacy constraint. Our proposed approaches can maintain high-level privacy with little loss of completion accuracy.
  arXiv  Detail & Related papers  (2021-10-02T03:49:55Z)
• Practical and Secure Federated Recommendation with Personalized Masks [24.565751694946062]
  Federated recommendation is a new notion of private distributed recommender systems. Current recommender systems mainly utilize homomorphic encryption and differential privacy methods. In this paper, we propose a new federated recommendation framework, named federated masked matrix factorization.
  arXiv  Detail & Related papers  (2021-08-18T07:12:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.