PrivateClean: Data Cleaning and Differential Privacy

S. Krishnan, Jiannan Wang, M. Franklin, Ken Goldberg, Tim Kraska
{"title":"PrivateClean: Data Cleaning and Differential Privacy","authors":"S. Krishnan, Jiannan Wang, M. Franklin, Ken Goldberg, Tim Kraska","doi":"10.1145/2882903.2915248","DOIUrl":null,"url":null,"abstract":"Recent advances in differential privacy make it possible to guarantee user privacy while preserving the main characteristics of the data. However, most differential privacy mechanisms assume that the underlying dataset is clean. This paper explores the link between data cleaning and differential privacy in a framework we call PrivateClean. PrivateClean includes a technique for creating private datasets of numerical and discrete-valued attributes, a formalism for privacy-preserving data cleaning, and techniques for answering sum, count, and avg queries after cleaning. We show: (1) how the degree of privacy affects subsequent aggregate query accuracy, (2) how privacy potentially amplifies certain types of errors in a dataset, and (3) how this analysis can be used to tune the degree of privacy. The key insight is to maintain a bipartite graph relating dirty values to clean values and use this graph to estimate biases due to the interaction between cleaning and privacy. We validate these results on four datasets with a variety of well-studied cleaning techniques including using functional dependencies, outlier filtering, and resolving inconsistent attributes.","PeriodicalId":20483,"journal":{"name":"Proceedings of the 2016 International Conference on Management of Data","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2016-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"33","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2016 International Conference on Management of Data","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2882903.2915248","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 33

Abstract

Recent advances in differential privacy make it possible to guarantee user privacy while preserving the main characteristics of the data. However, most differential privacy mechanisms assume that the underlying dataset is clean. This paper explores the link between data cleaning and differential privacy in a framework we call PrivateClean. PrivateClean includes a technique for creating private datasets of numerical and discrete-valued attributes, a formalism for privacy-preserving data cleaning, and techniques for answering sum, count, and avg queries after cleaning. We show: (1) how the degree of privacy affects subsequent aggregate query accuracy, (2) how privacy potentially amplifies certain types of errors in a dataset, and (3) how this analysis can be used to tune the degree of privacy. The key insight is to maintain a bipartite graph relating dirty values to clean values and use this graph to estimate biases due to the interaction between cleaning and privacy. We validate these results on four datasets with a variety of well-studied cleaning techniques including using functional dependencies, outlier filtering, and resolving inconsistent attributes.
PrivateClean:数据清理和差异隐私
差分隐私的最新进展使得在保留数据的主要特征的同时保证用户隐私成为可能。然而,大多数差分隐私机制都假定底层数据集是干净的。本文在我们称为PrivateClean的框架中探讨了数据清理和差异隐私之间的联系。PrivateClean包括一种用于创建数字和离散值属性的私有数据集的技术,一种用于保护隐私的数据清理的形式,以及用于在清理后回答sum、count和avg查询的技术。我们展示:(1)隐私程度如何影响随后的聚合查询准确性,(2)隐私如何潜在地放大数据集中某些类型的错误,以及(3)如何使用此分析来调整隐私程度。关键的见解是维护一个关于脏值和干净值的二部图,并使用这个图来估计由于清洁和隐私之间的相互作用而产生的偏差。我们在四个数据集上验证了这些结果,这些数据集使用了各种经过充分研究的清理技术,包括使用功能依赖、异常值过滤和解决不一致的属性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信