Related papers: Privacy-preserving recommender system using the data collaboration analysis for distributed datasets

Privacy-preserving recommender system using the data collaboration analysis for distributed datasets

URL: http://arxiv.org/abs/2406.01603v1
Date: Fri, 24 May 2024 07:43:00 GMT
Title: Privacy-preserving recommender system using the data collaboration analysis for distributed datasets
Authors: Tomoya Yanagi, Shunnosuke Ikeda, Noriyoshi Sukegawa, Yuichi Takano,
Abstract summary: We establish a framework for privacy-preserving recommender systems using the data collaboration analysis of distributed datasets. Numerical experiments with two public rating datasets demonstrate that our privacy-preserving method for rating prediction can improve the prediction accuracy for distributed datasets.
Score: 2.9061423802698565
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: In order to provide high-quality recommendations for users, it is desirable to share and integrate multiple datasets held by different parties. However, when sharing such distributed datasets, we need to protect personal and confidential information contained in the datasets. To this end, we establish a framework for privacy-preserving recommender systems using the data collaboration analysis of distributed datasets. Numerical experiments with two public rating datasets demonstrate that our privacy-preserving method for rating prediction can improve the prediction accuracy for distributed datasets. This study opens up new possibilities for privacy-preserving techniques in recommender systems.

Related papers

Privacy-Preserving Optimal Parameter Selection for Collaborative Clustering [2.107610564835429]
We focus on key clustering algorithms within a collaborative framework, where multiple data owners combine their data. A semi-trusted server assists in recommending the most suitable clustering algorithm and its parameters. Our findings indicate that the privacy parameter ($epsilon$) minimally impacts the server's recommendations, but an increase in $epsilon$ raises the risk of membership inference attacks.
arXiv Detail & Related papers (2024-06-08T18:21:12Z)
Optimizing Privacy and Utility Tradeoffs for Group Interests Through Harmonization [2.54365580380609]
We introduce a collaborative data-sharing mechanism between two user groups through a trusted third party. This third party uses adversarial privacy techniques with our proposed data-sharing mechanism to internally sanitize data for both groups. Our methodology ensures that private attributes cannot be accurately inferred while enabling highly accurate predictions of utility features.
arXiv Detail & Related papers (2024-04-07T18:55:33Z)
A Unified View of Differentially Private Deep Generative Modeling [60.72161965018005]
Data with privacy concerns comes with stringent regulations that frequently prohibited data access and data sharing. Overcoming these obstacles is key for technological progress in many real-world application scenarios that involve privacy sensitive data. Differentially private (DP) data publishing provides a compelling solution, where only a sanitized form of the data is publicly released.
arXiv Detail & Related papers (2023-09-27T14:38:16Z)
Private and Collaborative Kaplan-Meier Estimators [44.61287171386347]
We introduce two novel differentially private methods that offer flexibility in applying differential privacy to various functions of the data. We propose various paths that allow a joint estimation of the Kaplan-Meier curves with strict privacy guarantees.
arXiv Detail & Related papers (2023-05-24T17:11:48Z)
Private Set Generation with Discriminative Information [63.851085173614]
Differentially private data generation is a promising solution to the data privacy challenge. Existing private generative models are struggling with the utility of synthetic samples. We introduce a simple yet effective method that greatly improves the sample utility of state-of-the-art approaches.
arXiv Detail & Related papers (2022-11-07T10:02:55Z)
DP2-Pub: Differentially Private High-Dimensional Data Publication with Invariant Post Randomization [58.155151571362914]
We propose a differentially private high-dimensional data publication mechanism (DP2-Pub) that runs in two phases. splitting attributes into several low-dimensional clusters with high intra-cluster cohesion and low inter-cluster coupling helps obtain a reasonable privacy budget. We also extend our DP2-Pub mechanism to the scenario with a semi-honest server which satisfies local differential privacy.
arXiv Detail & Related papers (2022-08-24T17:52:43Z)
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Optimization [57.98426940386627]
We show that coordinating local learning with private centralized learning yields a generically useful and improved tradeoff between accuracy and privacy. We illustrate our theoretical results with experiments on synthetic and real-world datasets.
arXiv Detail & Related papers (2022-02-10T20:44:44Z)
Private Prediction Sets [72.75711776601973]
Machine learning systems need reliable uncertainty quantification and protection of individuals' privacy. We present a framework that treats these two desiderata jointly. We evaluate the method on large-scale computer vision datasets.
arXiv Detail & Related papers (2021-02-11T18:59:11Z)
Accuracy and Privacy Evaluations of Collaborative Data Analysis [4.987315310656657]
A collaborative data analysis through sharing dimensionality reduced representations of data has been proposed as a non-model sharing-type federated learning. This paper analyzes the accuracy and privacy evaluations of this novel framework.
arXiv Detail & Related papers (2021-01-27T00:38:47Z)
A Critical Overview of Privacy-Preserving Approaches for Collaborative Forecasting [0.0]
Cooperation between different data owners may lead to an improvement in forecast quality. Due to business competitive factors and personal data protection questions, said data owners might be unwilling to share their data. This paper analyses the state-of-the-art and unveils several shortcomings of existing methods in guaranteeing data privacy.
arXiv Detail & Related papers (2020-04-20T20:21:04Z)

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