Differentially private k-center problems

IF 1.3 4区数学 Q2 MATHEMATICS, APPLIED

Optimization Letters Pub Date : 2024-01-25 DOI:10.1007/s11590-023-02090-w

Fan Yuan, Dachuan Xu, Donglei Du, Min Li

引用次数: 0

Abstract

Data privacy has become one of the most important concerns in the big data era. Because of its broad applications in machine learning and data analysis, many algorithms and theoretical results have been established for privacy clustering problems, such as k-means and k-median problems with privacy protection. However, there is little work on privacy protection in k-center clustering. Our research focuses on the k-center problem, its distributed variant, and the distributed k-center problem under differential privacy constraints. These problems model the concept of safeguarding the privacy of individual input elements, with the integration of differential privacy aimed at ensuring the security of individual information during data processing and analysis. We propose three approximation algorithms for these problems, respectively, and achieve a constant factor approximation ratio.

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差分私有 K 中心问题

数据隐私已成为大数据时代最受关注的问题之一。由于其在机器学习和数据分析中的广泛应用，针对隐私聚类问题（如具有隐私保护功能的 k-means 和 k-median 问题）已经建立了许多算法和理论成果。然而，在 k 中心聚类中保护隐私的研究却很少。我们的研究重点是 k 中心问题及其分布式变体，以及差异隐私约束下的分布式 k 中心问题。这些问题以保护单个输入元素隐私的概念为模型，结合了旨在确保数据处理和分析过程中单个信息安全的差分隐私。我们分别针对这些问题提出了三种近似算法，并实现了恒因子近似率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Optimization Letters 管理科学-应用数学

CiteScore

3.40

自引率

6.20%

发文量

116

审稿时长

9 months

期刊介绍： Optimization Letters is an international journal covering all aspects of optimization, including theory, algorithms, computational studies, and applications, and providing an outlet for rapid publication of short communications in the field. Originality, significance, quality and clarity are the essential criteria for choosing the material to be published. Optimization Letters has been expanding in all directions at an astonishing rate during the last few decades. New algorithmic and theoretical techniques have been developed, the diffusion into other disciplines has proceeded at a rapid pace, and our knowledge of all aspects of the field has grown even more profound. At the same time one of the most striking trends in optimization is the constantly increasing interdisciplinary nature of the field. Optimization Letters aims to communicate in a timely fashion all recent developments in optimization with concise short articles (limited to a total of ten journal pages). Such concise articles will be easily accessible by readers working in any aspects of optimization and wish to be informed of recent developments.