Differential Privacy Protection Scheme of Adaptive Clustering by Fast Search and Find of Density Peaks

NAVIGATE

Table of Contents

STATISTICS

Viewed1165

Downloads1263

Differential Privacy Protection Scheme of Adaptive Clustering by Fast Search and Find of Density Peaks

[HTML] PDF下载 (1263)

[1]GE Lina,CHEN Yuanyuan,WANG Jie,et al.Differential Privacy Protection Scheme of Adaptive Clustering by Fast Search and Find of Density Peaks[J].Journal of Zhengzhou University (Engineering Science),2023,44(06):19-24.[doi:10.13705/j.issn.1671-6833.2023.03.010]

Copy

Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 44 Number of periods: 2023 06 Page number: 19-24 Column: Public date: 2023-12-25

Title:: Differential Privacy Protection Scheme of Adaptive Clustering by Fast Search and Find of Density Peaks

Author(s):: GE Lina¹; 2; 3; CHEN Yuanyuan¹; WANG Jie¹; 2; WANG Zhe¹; 1. School of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China; 2. Key Laboratory of Network Communication Engineering, Guangxi Minzu University, Nanning 530006, China; 3. Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Guangxi Minzu University, Nanning 530006, China

Keywords:: density peaks; differential privacy; random noise; clustering algorithm

CLC:: TP391

DOI:: 10.13705/j.issn.1671-6833.2023.03.010

Abstract:: In order to solve the privacy leakage problem caused by adaptive clustering by fast search and find of density peaks(AdDPC) when calculating the local density and the primary allocation strategy, a differential privacy protection scheme of an improved density peak clustering algorithm was proposed. In this scheme, the Laplace random noise was added in the process of calculating the local density of the algorithm. In this way, even if the attacker had the maximum background knowledge, it could not obtain the corresponding information by adding or deleting a point in the dataset, thereby, differential attack was used to obtain the information of the target data point, and to achieve the purpose of protecting the privacy data. In addition, the reachability definition was introduced to improve the allocation strategy of AdDPC when assigning non-clustered center points, so as to avoid the problem of data point allocation error caused by the one-time allocation strategy. The experiment compared F-Measure and ARI values of DP-rcCFSFDP, AdAPC-rDP, IDP K-means, and results showed that: when the privacy budget was greater than 1. 5, the F-Measure and ARI values of the proposed algorithm were better than those of other algorithms, and this algorithm could protect sensitive data and data availability at the same time.

References:: [1] SARLE W S. Algorithms for clustering data[ J] . Technometrics, 1990, 32(2) : 227-229.
[2] MA S H, GUO P K, YOU H R, et al. An image matching optimization algorithm based on pixel shift clustering RANSAC[J]. Information Sciences, 2021, 562: 452-474.
[3] DU Z J, LUO H Y, LIN X D, et al. A trust-similarity analysis-based clustering method for large-scale group decision-making under a social network [ J ] . Information Fusion, 2020, 63: 13-29.
[4] HASSAN B A, RASHID T A, HAMARASHID H K. A novel cluster detection of COVID-19 patients and medical disease conditions using improved evolutionary clustering algorithm star[ J] . Computers in Biology and Medicine, 2021, 138: 104866.
[5] 熊平, 朱天清, 王晓峰. 差分隐私保护及其应用[ J] . 计算机学报, 2014, 37(1) : 101-122.
XIONG P, ZHU T Q, WANG X F. A survey on differential privacy and applications [ J ] . Chinese Journal of Computers, 2014, 37(1) : 101-122.
[6] DWORK C. Differential privacy[M]∥Automata, Languages and Programming. Berlin: Springer, 2006: 1-12.
[7] 李杨, 郝志峰, 温雯, 等. 差分隐私保护 k-means 聚类方法研究[J]. 计算机科学, 2013, 40(3): 287-290.
LI Y, HAO Z F, WEN W, et al. Research on differential privacy preserving k-means clustering [ J] . ComputerScience, 2013, 40(3) : 287-290.
[8] RODRIGUEZ A, LAIO A. Clustering by fast search and find of density peaks[ J] . Science, 2014, 344 ( 6191) : 1492-1496.
[9] 谢娟英, 高红超, 谢维信. K 近邻优化的密度峰值快速搜索聚类算法[ J] . 中国科学: 信息科学, 2016, 46 (2) : 258-280.
XIE J Y, GAO H C, XIE W X. K-nearest neighbors optimized clustering algorithm by fast search and finding the density peaks of a dataset[ J] . Scientia Sinica ( Informationis) , 2016, 46(2) : 258-280.
[10] DWORK C, MCSHERRY F, NISSIM K, et al. Calibrating noise to sensitivity in private data analysis[M]∥Theory of Cryptography. Berlin: Springer, 2006: 265-284.
[11] 陈韵. 基于差分隐私密度峰值聚类算法的研究和应用 [D] . 南京: 南京邮电大学, 2020.
CHEN Y. Research of density peak clustering algorithm based on differential privacy preserving[D]. Nanjing: Nanjing University of Posts and Telecommunications, 2020.
[12] SASAKL Y. The truth of the F-Measure [ J] . Teach tutor mater, 2007, 1(5) : 1-5.
[13] HUBERT L, ARABIE P. Comparing partitions[ J] . Journal of Classification, 1985, 2(1) : 193-218.
[14] DUA D, TANISKIDOU E K. UCI Machine learning repository[EB / OL] . ( 2017 - 02 - 13) [ 2022 - 10 - 11] . https:∥archive. ics. uci. edu / ml / index. php.
[15] STREET W N, WOLBERG W H, MANGASARIAN O L. Nuclear feature extraction for breast tumor diagnosis[C]∥ Proc SPIE 1905, Biomedical Image Processing and Biomedical Visualization. San Jose: SPIE, 1993: 861-870.
[16] GIONIS A, MANNILA H, TSAPARAS P. Clustering aggregation[ J] . ACM Transactions on Knowledge Discovery from Data, 2007, 1(1) : 1-12.

Similar References:

Memo

Last Update: 2023-10-22