DP-SIPS: A simpler, more scalable mechanism for differentially private partition selection

Marika Swanberg, Damien Desfontaines, Samuel Haney
{"title":"DP-SIPS: A simpler, more scalable mechanism for differentially private partition selection","authors":"Marika Swanberg, Damien Desfontaines, Samuel Haney","doi":"10.56553/popets-2023-0109","DOIUrl":null,"url":null,"abstract":"Partition selection, or set union, is an important primitive in differentially private mechanism design: in a database where each user contributes a list of items, the goal is to publish as many of these items as possible under differential privacy. In this work, we present a novel mechanism for differentially private partition selection. This mechanism, which we call {DP-SIPS}, is very simple: it consists of iterating the naive algorithm over the data set multiple times, removing the released partitions from the data set while increasing the privacy budget at each step. This approach preserves the scalability benefits of the naive mechanism, yet its utility compares favorably to more complex approaches developed in prior work.","PeriodicalId":74556,"journal":{"name":"Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.56553/popets-2023-0109","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Partition selection, or set union, is an important primitive in differentially private mechanism design: in a database where each user contributes a list of items, the goal is to publish as many of these items as possible under differential privacy. In this work, we present a novel mechanism for differentially private partition selection. This mechanism, which we call {DP-SIPS}, is very simple: it consists of iterating the naive algorithm over the data set multiple times, removing the released partitions from the data set while increasing the privacy budget at each step. This approach preserves the scalability benefits of the naive mechanism, yet its utility compares favorably to more complex approaches developed in prior work.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
DP-SIPS:用于区分私有分区选择的更简单、更可扩展的机制
分区选择(或集合联合)是差异私有机制设计中的一个重要原语:在每个用户贡献一个项列表的数据库中,目标是在差异隐私下发布尽可能多的这些项。在这项工作中,我们提出了一种新的差分私有分区选择机制。这种机制,我们称之为{DP-SIPS},非常简单:它包括在数据集上多次迭代朴素算法,从数据集中删除释放的分区,同时在每一步增加隐私预算。这种方法保留了原始机制的可伸缩性优势,但它的实用性比以前工作中开发的更复杂的方法要好。
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 去求助
来源期刊
自引率
0.00%
发文量
0
审稿时长
16 weeks
期刊最新文献
Editors' Introduction Compact and Divisible E-Cash with Threshold Issuance On the Robustness of Topics API to a Re-Identification Attack DP-SIPS: A simpler, more scalable mechanism for differentially private partition selection Privacy-Preserving Federated Recurrent Neural Networks
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1