信息安全研究2025，Vol.11Issue(8)：710-717,8.DOI:10.12379/j.issn.2096-1057.2025.08.04
基于差分隐私k-means++的一种隐私预算分配方法
A Privacy Budget Allocation Method Based on Differential Privacy k-means++
摘要
Abstract
For the traditional differential privacy k-means++algorithm,uniform budget allocation by the equal division method cannot meet varying privacy needs.Meanwhile,binary division rapidly depletes the budget,leading to excessive noise later on,both impairing clustering performance.To solve this problem,a new privacy budget allocation method combining the arithmetic and equal allocation methods was proposed.For initial center selection,use an equal division budget allocation.For center updates,early stage uses arithmetic progression,later stage switches to equal division,both focused on minimal budget.This approach ensures substantial initial privacy budget for minimal cluster center distortion,and moderate budget depletion later to prevent excessive noise that could compromise clustering outcomes.A series of experiments based on real data show that,compared to the original k-means++,the minimum error is only 0.09％.Compared to the equal distribution method and the binary method,the clustering accuracy is improved by up to 14.9％and 16.9％respectively.It can be seen that this method is significantly better than the equal division and the binary division,and can improve the usability and accuracy of clustering results to a certain extent.关键词
信息安全/数据挖掘/差分隐私保护/k-means++/隐私预算分配Key words
information security/data mining/differential privacy protection/k-means++/privacy budget allocation分类
信息技术与安全科学引用本文复制引用
晏玲,赵海良..基于差分隐私k-means++的一种隐私预算分配方法[J].信息安全研究,2025,11(8):710-717,8.基金项目
国家自然科学基金项目(61806170) （61806170）
