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Operations Research Transactions >

2022 , Vol. 26 >Issue 3: 1 - 16

DOI: https://doi.org/10.15960/j.cnki.issn.1007-6093.2022.03.001

A survey of differential privacy algorithms for the $k$-means problem

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  • 1. Beijing Institute for Scientific and Engineering Computing, Beijing University of Technology, Beijing 100124, China
    2. School of Computer Science and Technology, Shandong Jianzhu University, Jinan 250101, Shandong, China

Received date: 2021-12-20

  Online published: 2022-09-07

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Abstract

The $k$-means problem is a very important problem in the field of machine learning and combinatorial optimization. It is a classic NP-hard problem, which is widely used in data mining, business production decision-making, image processing, biomedical technology, and more. As people in these fields pay more and more attention to personal privacy protection, which raises a question: In the case that decisions are usually made by artificial intelligence algorithms, how to ensure that as much information as possible is extracted from the data without revealing personal privacy? In the past ten years, experts and scholars have continuously studied and explored the $k$-means problem with privacy protection, and has also obtained many results with theoretical guiding significance and practical application value. In this paper we mainly introduce these differential privacy algorithms of the $k$-means problem for readers.

Key words： $k$-means problem; differential privacy; approximate algorithm; exponential mechanism; Laplace mechanism

Cite this article

Fan YUAN, Dachuan XU, Dongmei ZHANG . A survey of differential privacy algorithms for the $k$-means problem[J]. Operations Research Transactions, 2022 , 26(3) : 1 -16 . DOI: 10.15960/j.cnki.issn.1007-6093.2022.03.001

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