A high-dimensional numeric data collection algorithm for local difference privacy based on random projection

Huizhong SUN; Jianyu YANG; Xiang CHENG; Sen SU

doi:10.11959/j.issn.2096-0271.2020001

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A high-dimensional numeric data collection algorithm for local difference privacy based on random projection

TOPIC：PRIVACY PROTECTION OF BIG DATA | 更新时间：2024-06-03

- A high-dimensional numeric data collection algorithm for local difference privacy based on random projection
- Big Data Research Vol. 6, Issue 1, Pages: 2020001-1(2020)
- 作者机构：
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- DOI：10.11959/j.issn.2096-0271.2020001
  CLC： TP309
- Online First：2020-01，
  
  Published：15 January 2020
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Huizhong SUN, Jianyu YANG, Xiang CHENG, et al. A high-dimensional numeric data collection algorithm for local difference privacy based on random projection[J]. Big Data Research, 2020, 6(1): 2020001-1.
DOI：

Huizhong SUN, Jianyu YANG, Xiang CHENG, et al. A high-dimensional numeric data collection algorithm for local difference privacy based on random projection[J]. Big Data Research, 2020, 6(1): 2020001-1. DOI： 10.11959/j.issn.2096-0271.2020001.

摘要

对满足本地差分隐私的高维数值型数据收集问题进行了研究。设计了一种基于随机投影技术的满足本地差分隐私的高维数值型数据收集算法Multi-RPHM，在满足本地差分隐私的条件下，该算法处理维度较高的数据时能够保证所收集的数据的高效用。从理论上证明了该算法满足ε-本地差分隐私的要求。在合成数据集上进行的实验结果验证了该算法的有效性。

Abstract

The problem of high-dimensional data collection satisfying local differential privacy was studied.A new locally differentially private algorithm called Multi-RPHM was proposed based on the random projection technology

which achieved the high utility of the collected high-dimensional numeric data while satisfying the local differential privacy.The algorithm was formally proved to meet ε-local differential privacy.The effectiveness of the algorithm was comfirmed through experiments on synthetic datasets.

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references

DWORK C , . Differential p rivacy [C ] // ICALP,July 10-14,2006,Venice,Italy . Heidelberg:Springer , 2006 : 1 - 12 .

DWORK C , MCSHERRY F , NISSIM K , et al . Calibrating noise to sensitivity in private data analysis [C ] // TCC,March 4-7,2006,New York,USA . Heidelberg:Springer , 2006 : 265 - 284 .

DWORK C , . Differential privacy:a survey of results [C ] // TAMC,April 25-29,2008,Xi’an,China . Heidelberg:Springer , 2008 : 1 - 19 .

SWEENY L . K-anonymity:a model for protecting privacy [J ] . International Journal on Uncertainty,Fuzziness and Knowledge Based Systems , 2012 , 10 ( 5 ): 557 - 570 .

MACHANAVAJJHALA A , KIFER D , GEHRKE J , et al . L-diversity:privacy beyond K-anonymity [J ] . ACM Transactions on Knowledge Discovery from Data , 2007 , 1 ( 1 ):24.

KASIVISWANATHAN P S , LEEHK , NISSIM K , et al . What can we learn privately [C ] // FOCS,October 25-28,2008,Philadelphia,USA . Piscataway:IEEE Press , 2008 : 531 - 540 .

叶青青 , 孟小峰 , 朱敏杰 , 等 . 本地化差分隐私研究综述 [J ] . 软件学报 , 2018 , 29 ( 7 ): 1981 - 2005 .

YE Q Q , MENG X F , ZHU M J , et al . Survey on local differential privacy [J ] . Journal of Software , 2018 , 29 ( 7 ): 1981 - 2005 .

ERLINGSSON Ú , PIHUR V , KOROLOVA A . RAPPOR:randomized aggregatable privacypreserving ordinal response [C ] // CCS,November 3-7,2014,Scottsdale,USA . New York:ACM Press , 2014 : 1054 - 1067 .

KAIROUZ P , BONAWITZ K , RAMAGE D . Discrete distribution estimation under local privacy [J ] . Proceedings on Privacy Enhancing Technologies , 2016 : 84 - 104 .

BASSILY R , SMITH A . Local,private,efficient protocols for succinct histograms [C ] // STOC,June 14-17,2015,Portland,USA . New York:ACM Press , 2015 : 127 - 135 .

WARNER S L . Randomized response:a survey technique for eliminating evasive answer bias [J ] . Journal of the American Statistical Association , 1965 , 60 ( 309 ): 63 - 69 .

NGUYÊN T T , XIAO X K , YANG Y , et al . Collecting and analyzing data from smart device users with local differential privacy [J ] . Computer Science , 2016 ,arXiv:1606.05053

QIN Z , YANG Y , YU T , et al . Heavy hitter estimation over set-valued data with local differential privacy [C ] // CCS,October 24-28,2016,Vienna,Austria . New York:ACM Press , 2016 : 192 - 203 .

WANG N , XIAO X K , YANG Y , et al . Collecting and analyzing multidimensional data with local differential privacy [C ] // ICDE,April 8-11,2019,Macao,China . Piscataway:IEEE Press , 2019 : 638 - 649 .

DUCHI J C , WAINWRIGHT M J , JORDAN M I , et al . Minimax optimal procedures for locally private estimation [J ] . American Statistical Association , 2018 , 113 ( 521 ): 182 - 201 .

MCSHERRY F , . Privacy integrated queries:an extensible platform for privacy preserving data analysis [C ] // SIGMOD,June 29-July 2,2009,Providence,USA . New York:ACM Press , 2009 : 19 - 30 .

JOHNSON W B , LINDENSTRAUSS J . Extensions of Lipschitz mappings into a Hilbert space [J ] . Contemporary Mathematics , 1984 , 26 ( 189-206 ):1.

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