基于本地化差分隐私的键值数据关联分析

doi:10.11896/jsjkx.201200122

摘要/Abstract

摘要： 在群智感知系统中,从分布式数据源中持续收集和分析数据可以为先进的数据挖掘模型提供决策支持。由于数据中可能包含个人相关的信息,数据的采集和分析过程中通常伴随着隐私泄露的风险。本地化差分隐私作为先进的隐私保护方案可在用户的隐私性和数据的可用性之间提供较好的权衡。当前,键值数据作为异构类型数据,其同时含有分类数据和数值数据,基于本地化差分隐私在多维度下对键值数据进行关联分析面临着一定的挑战。针对隐私保护前提下键值数据的发布和关联分析问题,首先定义了键值数据的频率关联和均值关联问题,然后提出了适用于键值对的索引独热编码,为键值数据提供本地化差分隐私保护,最后在扰动的数据上对键值数据进行关联分析。基于仿真数据集和真实数据集的实验和理论分析验证了所提方案的有效性。

关键词: 本地化差分隐私, 关联分析, 键值数据, 均值估计, 频率估计

Abstract: Crowdsourced data from distributed sources are routinely collected and analyzed to produce effective data-mining mo-dels in crowdsensing systems.Data usually contains personal information,which leads to possible privacy leakage in data collection and analysis.The local differential privacy (LDP) has been deemed as the de facto measure for trade-off between privacy guarantee and data utility.Currently,the key-value data is a kind of heterogeneous data types in which the key is categorical data and the value is numerical data.Achieving LDP for key-value data is challenging.This paper focuses on key-value data publishing and correlation analysis under the framework of LDP.Firstly,the frequency correlation and mean correlation in key-value data are defined.Then the indexing one-hot perturbation mechanism is proposed to provide LDP guarantees.At last,the correlation results can be estimated in the perturbed space.Theoretical analysis and experimental results on both real-word and synthetic dataset va-lidate the effectiveness of proposed mechanism.

Key words: Correlation analysis, frequency estimation, Key-value data, Local differential privacy, Mean estimation

中图分类号:

TP391

孙林, 平国楼, 叶晓俊. 基于本地化差分隐私的键值数据关联分析[J]. 计算机科学, 2021, 48(8): 278-283. https://doi.org/10.11896/jsjkx.201200122

SUN Lin, PING Guo-lou, YE Xiao-jun. Correlation Analysis for Key-Value Data with Local Differential Privacy[J]. Computer Science, 2021, 48(8): 278-283. https://doi.org/10.11896/jsjkx.201200122

参考文献

[1]YANG G M,YANG J,ZHANG J P.Research on Data Publi-shing of Privacy Preserving[J].Computer Science,2011(9):17-23.
[2]WANG B,YANG J.Research on Anonymity Technique forPersonalization Privacy-preserving Data Publishing[J].Compu-ter Science,2012,39(4):168-171.
[3]DWORK C,MCSHERRY F,NISSIM K,et al.Calibrating noise to sensitivity in private data analysis[C]//Theory of Cryptography Conference.Heidelberg:Springer,2006:265-284.
[4]ABADI M,CHU A,GOODFELLOW I,et al.Deep learningwith differential privacy[C]//Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security.Vienna:ACM,2016:308-318.
[5]PHAN N H,WANG Y,WU X,et al.Differential privacy preservation for deep auto-encoders:an application of human behavior prediction[C]//Proceedings of the AAAI Conference on Artificial Intelligence.Arizona:AAAI Press,2016:1309-1315.
[6]CHEN R,XIAO Q,ZHANG Y,et al.Differentially privatehigh-dimensional data publication via sampling-based inference[C]//Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.Sydeny:ACM,2015:129-138.
[7]DUCHI J C,JORDAN M I,WAINWRIGHT M J.Local privacy and statistical minimax rates[C]//2013 IEEE 54th Annual Symposium on Foundations of Computer Science.Washington:IEEE,2013:429-438.
[8]BLOOM B H.Space/time trade-offs in hash coding with allowa-ble errors[J].Communications of the ACM,1970,13(7):422-426.
[9]ERLINGSSON U,PIHUR V,KOROLOVA A.RAPPOR:Randomized aggregatable privacy-preserving ordinal response[C]//Proceedings of the 2014 ACM SIGSAC Conference on Compu-ter and Communications Security.Arizona:ACM,2014:1054-1067.
[10]NGUYEN T T,XIAO X X,YANG Y,et al.Collecting and Ana-lyzing Data from Smart Device Users with Local Differential Privacy[EB/OL].https://arxiv.org/abs/1606.05053.
[11]WANG N,XIAO X K,YANG Y,et al.Collecting and Analyzing Multidimensional Data with Local Differential Privacy[C]//IEEE 35th International Conference on Data Engineering.Macau:IEEE Press,2019:638-649.
[12]QIN Z,YANG Y,YU T,et al.Heavy hitter estimation over set-valued data with local differential privacy[C]//Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security.Vienna:ACM,2016:192-203.
[13]SWANG T H,LI N H,JHA S.Locally differentially private frequent itemset mining[C]//2018 IEEE Symposium on Security and Privacy.San Francisco:IEEE Press,2018:127-143.
[14]BASSILY R,NISSIM K,STEMMER U,et al.Practical locally private heavy hitters[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems.New York:Curran Associates Inc,2017:2285-2293.
[15]CORMODE G,KULKARNI T,SRIVASTAVA D.Marginalrelease under local differential privacy[C]//Proceedings of the 2018 International Conference on Management of Data.Houston:ACM,2018:131-146.
[16]YE Q Q,HU H B,MENG X F.PrivKV:Key-Value Data Collection with Local Differential Privacy[C]//2019 IEEE Symposium on Security and Privacy (SP).San Francisco:IEEE Press,2019:317-331.
[17]GU X L,LI M,CHENG Y Q,et al.PCKV:Locally Differentially Private Correlated Key-Value Data Collection with Optimized Utility[C]//29th USENIX Security Symposium.USENIX Association,2020:967-984.
[18]ZHANG J,CORMODE G,PROCOPIUC C M,et al.Privbayes:Private data release via bayesian networks[J].ACM Transactions on Database Systems,2017,42(4):1-41.
[19]BARAK B,CHAUDHURI K,DWORK C,et al.Privacy,accuracy,and consistency too:a holistic solution to contingency table release[C]//Proceedings of the Twenty-sixth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems.New York:ACM,2007:273-282.
[20]DING B L,KULKARNI J,YEKHANIN S.Collecting telemetry data privately[C]//Proceedings of the 31st International Conference on Neural Information Processing.California:ACM,2017:3574-3583.
[21]DUCHI J C,JORDAN M I.Minimax Optimal Procedures forLocally Private Estimation[J].Journal of the American Statistical Association,2018,113(521):182-201.
[22]WANG T H,BLOCKI J,LI N H,et al.Locally differentiallyprivate protocols for frequency estimation[C]//26th USENIX Security Symposium.Vancouver:USENIX Association,2017:729-745.

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