差分隐私技术研究进展

高志强; 王宇涛

doi:10.11959/j.issn.1000-436x.2017241

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差分隐私技术研究进展

学术论文 | 更新时间：2024-06-05

- 差分隐私技术研究进展
- Survey on differential privacy and its progress
- 通信学报 2017年38卷第Z1期页码：151-155
- 作者机构：
  
  武警工程大学信息工程系，陕西西安710086
- 作者简介：
  
  [ "高志强（1989-），男，黑龙江齐齐哈尔人，武警工程大学博士生，主要研究方向为隐私计算、深度神经网络、群智能优化等。" ]
  [ "王宇涛（1989-），男，贵州贵阳人，武警工程大学硕士生，主要研究方向为大数据挖掘。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61402529)
- DOI：10.11959/j.issn.1000-436x.2017241
  中图分类号： TP391
- 网络首发：2017-10，
  
  纸质出版：2017-10-25
- 稿件说明：
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高志强, 王宇涛. 差分隐私技术研究进展[J]. 通信学报, 2017,38(Z1):151-155.

Zhi-qiang GAO, Yu-tao WANG. Survey on differential privacy and its progress[J]. Journal on Communications, 2017, 38(Z1): 151-155.
高志强, 王宇涛. 差分隐私技术研究进展[J]. 通信学报, 2017,38(Z1):151-155. DOI： 10.11959/j.issn.1000-436x.2017241.

Zhi-qiang GAO, Yu-tao WANG. Survey on differential privacy and its progress[J]. Journal on Communications, 2017, 38(Z1): 151-155. DOI： 10.11959/j.issn.1000-436x.2017241.

摘要

随着大数据共享时代的到来，数据隐私保护问题也随之突显。自 2006 年提出以来，差分隐私技术在支持隐私保护的数据挖掘与数据发布方面得到了广泛研究。近年来，Google、Apple 等公司陆续将差分隐私技术应用于最新产品中，差分隐私技术再次成为学术界和产业界的焦点。首先，对传统集中式模型下的差分隐私技术进行综述，介绍了面向数据挖掘与数据发布的差分隐私技术。然后，着重对最新的基于本地差分隐私模型下的数据收集与数据分析进行阐述，涉及众包模型下的随机响应、BloomFilter、统计推断等技术。最后，对差分隐私技术面临的主要问题和解决方案进行总结。

Abstract

With the arrival of the era of big data sharing

data privacy protection issues will be highlighted.Since its introduction in 2006

differential privacy technology has been widely researched in data mining and data publishing.In recent years

Google

Apple and other companies have introduced differential privacy technology into the latest products

and differential privacy technology has become the focus of academia and industry again.Firstly

the traditional centralized model of differential privacy was summarized

from the perspective of analysis of data mining and data released in the differential privacy way.Then the latest local differential privacy regarding data collection and data analysis based on the local model was described

involving crowdsourcing with random response technology

BloomFilter

statistical inference techniques.Finally

the main problems and solutions of differential privacy technology were summarized.

关键词

Keywords

references

DALENIUS T . Towards a methodology for statistical disclosure control [J ] . Statistic Tidskrift , 1977 , 15 ( 2 ): 429 - 444 .

SWEENEY L . K-anonymity:a model for protecting privacy [J ] . International Journal of Uncertainty,Fuzziness and Knowledge-Based Systems , 2002 , 10 ( 5 ): 557 - 570 .

MACHNAVAJJHALA A , GEHRKE J , KIFER D , et al . l-diversity:privacy beyond k-anonymity [C ] // International Conference on Data Engineering . IEEE , 2006 :24.

LI N H , LI T C , VENKATASUBRAMANIAN S . t-closeness:privacy beyond k-anonymity and l-diversity [C ] // IEEE International Conference on Data Engineering . IEEE , 2007 : 106 - 115 .

WONG R C , LI J Y , FU A W , et al . (α,k)-anonymity:an enhanced k-anonymity model for privacy preserving data publishing [C ] // ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM , 2006 : 754 - 759 .

DWORK C . A firm foundation for private data analysis [J ] . Communications of the ACM , 2011 , 54 ( 1 ): 86 - 95 .

DWORK C , KENTHAPADI K , MCSHERRY F , et al . Our data,ourselves:privacy via distributed noise generation [C ] // International Conference on the Theory and Applications of Cryptographic Techniques . 2006 : 486 - 503 .

DWORK C , MCSHERRY F , NISSIM K . Calibrating noise to sensitivity in private data analysis [M ] . Theory of Cryptography . Springer Berlin Heidelberg , 2006 : 637 - 648 .

DWORK C , NAOR M , PITASSI T , et al . Differential privacy under continual observation [J ] . Stoc , 2010 : 715 - 724 .

DWORK C , NAOR M , PITASSI T , et al . Pan-private streaming algorithms [C ] // The First Symposium on Innovations in Computer Science . 2010 : 66 - 80 .

NOVAC O C , NOVAC M , CORDAN O , et al . Comparative study of Google,Android,Apple iOS and Microsoft Windows Phone mobile operating systems [C ] // International Conference on Engineering of Modern Electric System . 2017 : 154 - 159 .

SLVA M R D , ROMOS T M , HOLANDA M T D . Geographic information system with public participat on iOS system [C ] // Information Systems and Technologies . 2017 : 1 - 5 .

PIHUR V , KOROLOVA A . RAPPOR:randomized aggregatable privacy-preserving ordinal response [C ] // ACM Sigsac Conference on Computer and Communications Security . ACM , 2014 : 1054 - 1067 .

FANTI G , PIHUR V , ULFAR E . Building a RAPPOR with the unknown:privacy-preserving learning of associations and data dictionaries [J ] . Proceedings on Privacy Enhancing Technologies , 2016 , 2016 ( 3 ): 41 - 61 .

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

DUCHI J C , JORDAN M I , WAINWRIGHT M J . Local privacy and statistical minimax rates [C ] // Annual IEEE Symposium on Foundations of Computer Science . 2013 : 429 - 438 .

WARNER S L . Randomized response:a survey technique for eliminating evasive answer bias [J ] . Journal of the American Statistical Association , 1965 , 60 ( 309 ): 63 - 66 .

DWORK C , ARON R . The algorithmic foundations of differential privacy [M ] . Now Publishers Inc . 2014 .

XU J , ZHANG Z , XIAO X , et al . Differentially private histogram publication [C ] // International Conference on Data Engineering . IEEE , 2012 : 32 - 43 .

ENGEL D , EIBL G . Wavelet-based multiresolution smart meter privacy [J ] . IEEE Transactions on Smart Grid , 2017 , 8 ( 4 ): 1710 - 1721 .

HAY M , MACHANAVAJJHALA A , MIKLAU G , et al . Principled evaluation of differentially private algorithms using DPBench [J ] . arXiv:1512.04817 .

QIN Z , YANG Y , YU T , et al . Heavy hitter estimation over set-valued data with local differential privacy [C ] // ACM Sigsac Conference on Computer and Communications Security . ACM , 2016 : 192 - 203 .

HITAJ B , ATENIESE G , PEREZ F . Deep models under the GAN:information leakage from collaborative deep learning [J ] . arXiv:1702.07464 .

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