差分隐私保护下的数据分级发布机制

张文静; 李晖

doi:10.11959/j.issn.2096-109x.2015.00008

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差分隐私保护下的数据分级发布机制

学术论文 | 更新时间：2024-06-03

- 差分隐私保护下的数据分级发布机制
- A differentially-private mechanism for multi-level data publishing
- 网络与信息安全学报 2015年1卷第1期页码：58-65
- 作者机构：
- 作者简介：
  
  [ "张文静（1988-），女，黑龙江绥化人，西安电子科技大学博士生，主要研究方向为大数据安全、隐私保护。" ]
  [ "李晖（1968-），男，河南灵宝人，博士，西安电子科技大学教授、博士生导师，主要研究方向为密码学、无线网络安全、云计算安全、信息论与编码理论。" ]
- 基金信息：
  
  国家自然科学基金—广东联合基金资助项目;The National Natural Science Foundation of China—Guangdong Provincial People’s Government of the Joint Natural Science Fund Projects(U1401251)
- DOI：10.11959/j.issn.2096-109x.2015.00008
  中图分类号： TP309
- 网络首发：2015-12，
  
  纸质出版：2015-12-15
- 稿件说明：
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张文静, 李晖. 差分隐私保护下的数据分级发布机制[J]. 网络与信息安全学报, 2015,1(1):58-65.

Wen-jing ZHANG, Hui LI. A differentially-private mechanism for multi-level data publishing[J]. Chinese Journal of Network and Information Security, 2015, 1(1): 58-65.
张文静, 李晖. 差分隐私保护下的数据分级发布机制[J]. 网络与信息安全学报, 2015,1(1):58-65. DOI： 10.11959/j.issn.2096-109x.2015.00008.

Wen-jing ZHANG, Hui LI. A differentially-private mechanism for multi-level data publishing[J]. Chinese Journal of Network and Information Security, 2015, 1(1): 58-65. DOI： 10.11959/j.issn.2096-109x.2015.00008.

摘要

隐私保护技术解决了数据发布过程中的隐私泄露问题，然而当前的数据发布技术大多只面向查询用户发布同一隐私保护级别的数据，并未考虑查询用户等级不同的情况。在所提出的满足差分隐私的数据分级发布机制中，数据发布方利用隐私预算参数不同的拉普拉斯机制对数据查询结果进行隐私保护处理，实现了输出隐私保护程度不同的查询结果。在依据付费或权限对查询用户分级后，数据发布方为等级较高（低）的查询用户发布隐私保护程度较低（高）的查询结果，使得查询用户可使用错误率较低（高）的数据，达到了隐私数据分级发布的效果。实验结果与安全性分析表明该机制在抵抗背景知识攻击的同时还可有效地实现输出错误率不同的分级查询结果。

Abstract

Privacy preserving technology had addressed the problem of privacy leakage during data publishing proc-ess

however

current data publishing technologies mostly focused on publishing privacy preserving data with single level

without considering some scenarios of multi-level users.Therefore

a differentially-private mechanism for multi-level data publishing was proposed.The proposed mechanism employed the Laplace mechanism with different privacy budgets to output results with different privacy protection levels.After the user’s level was determined ac-cording to the charge or privilege of that specific user

the goal that a user with high(low) level can only use the out-put result with low(high) privacy protection level which had low(high) error rate could be achieved.Finally

the evaluation results and security analysis show that our proposed framework can not only prevent from background knowledge attack

but also achieve multi-level data publishing with different error rates effectively .

关键词

Keywords

references

DWORK C , . Differential privacy [C ] // Proceedings of the 33rd In-ternational Colloquium on Automata,Languages and Programming,Venice . 2006 : 1 - 12 .

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

NARAYANAN A , SHMATIKOV V . Robust de-anonymization of large sparse datasets [C ] // Proceedings of IEEE Symposium on Security and Privacy,Oakland . 2008 : 111 - 125 .

RIBEIRO B , CHEN W , MIKLAU G , et al . Analyzing privacy in enterprise packet trace anonymization [C ] // Proceedings of the Net-work and Distributed System Security Symposium,San Diego . 2008 .

MACHANAVAJJHALA A , KIFER D , GEHRKE J , et al . L-diversity:privacy beyond k-anonymity [C ] // Proceedings of the 22nd International Conference on Data Engineering,Washington,DC . 2006 : 24 - 35 .

LI N , LI T , VENKATASUBRAMANIAN S . T-closeness:privacy beyond k-anonymity and l-diversity [C ] // Proceedings of the 23rd International Conference on Data Engineering,Washington DC . 2007 : 106 - 115 .

DWORK C , MCSHERRY F , NISSIM K , et al . Calibrating noise to sensitivity in private data analysis [C ] // Proceedings of the 3rd Con-ference on Theory of Cryptography,New York . 2006 : 265 - 284 .

ANDRES M E , BORDENABE N E , CHATZIKOKOLAKIS K , et al . Geo-indistinguishability:differential privacy for location-based systems [C ] // Proceedings of the 2013 ACM SIGSAC Conference on Computer and Communications Security,Berlin . 2013 : 901 - 914 .

SHEN E , YU T . Mining frequent graph patterns with differential privacy [C ] // Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,Chicago . 2013 : 545 - 553 .

ZHAO J , JUNG T , WANG Y , et al . Achieving differential privacy of data disclosure in the smart grid [C ] // Proceedings of IEEE IN-FOCOM 2014,Toronto . 2014 : 504 - 512 .

XIAO Y , XIONG L . Protecting locations with differential privacy under temporal correlations [C ] // Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security,Denver . 2015 : 1298 - 1309 .

XU J , ZHANG Z , XIAO X , et al . Differentially private histogram publication [C ] // Proceedings of IEEE 28th International Conference on Data Engineering,Washington DC . 2012 : 32 - 43 .

LI H , XIONG L , JIANG X , et al . Differentially private histogram publication for dynamic datasets:an adaptive sampling ap-proach [C ] // Proceedings of the 24th ACM International Conference on Information and Knowledge Management,Melbourne . 2015 : 1001 - 1010 .

PENG S , YANG Y , ZHANG Z , et al . DP-tree:indexing multidi-mensional data under differential privacy [C ] // Proceedings of the ACM SIGMOD International Conference on Management of Data,Scottsdale . 2012 : 864 - 864 .

YAROSLAVTSEV G , CORMODE G , PROCOPIUC C M , et al . Accurate and efficient private release of datacubes and contingency tables [C ] // Proceedings of 29th IEEE International Conference on Data Engineering,Brisbane . 2013 : 745 - 756 .

CHEN R , REZNICHENKO A , FRANCIS P , et al . Towards statis-tical queries over distributed private user data [C ] // Proceedings of the 9th USENIX Symposium on Networked Systems Design and Implementation,San Jose . 2012 : 169 - 182 .

TRAMER F , HUANG Z , HUBAUX J P , et al . Differential privacy with bounded priors:reconciling utility and privacy in ge-nome-wide association studies [C ] // Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security,Denver . 2015 : 1286 - 1297 .

SANCHEZ D , DOMINGO-FERRER J , MARTINEZ S . Improving the utility of differential privacy via univariate microaggrega-tion [C ] // Proceedings of Privacy in Statistical Databases,Ibiza . 2014 : 130 - 142 .

MCSHERRY F , TARWAR K . Mechanism design via differential privacy [C ] // Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science,Providence,Rhode Island . 2007 : 97 - 103 .

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