MAXGDDP：基于差分隐私的决策数据发布算法

doi:10.11959/j.issn.1000-436x.2018049

Abstract

Abstract:

Specialization-based private decision data release has attracted considerable research attention in recent years.The relation among hierarchical node,taxonomy tree,and budget allocation directly constrains the accuracy of data release and classification.Most existing methods based on hierarchical specialization cannot efficiently address the above problems.An effective method was proposed,called MAXGDDP to publish decision data with specialization.MAXGDDP employed MAX index attribute selection algorithm to select the highlight concept for furthering specialization in each hierarchy.Besides,for making more rational use of privacy budget,MAXGDDP relied on geometric strategy to allocate the privacy budget in each hierarchy.Compared with existing methods such as DiffGen on the real datasets,MAXGDDP outperforms its competitors,achieves data privacy and the better result of classification simultaneously.

Key words: decision data, data release, differential privacy, hierarchical specialization

CLC Number:

TP309

Jibin FU,Xiaojian ZHANG,Liping DING. MAXGDDP:decision data release with differential privacy[J]. Journal on Communications, 2018, 39(3): 136-146.

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References 21

[1]	SWEENEY L . k-anonymity:a model for protecting privacy[J]. International Journal of Uncertainty,Fuzziness and Knowledge-Based Systems, 2002,10(5): 557-570.
[2]	MACHANAVAJJHALA A , KIFER D , GEHRKE J ,et al. l-diversity:privacy beyond k-anonymity[J]. ACM Transactions on Knowledge Discovery from Data (TKDD), 2007,1(1): 3-52.
[3]	LI N , LI T , VENKATASUBRAMANIAN S . t-closeness:privacy beyond k-anonymity and l-diversity[C]// 2007 IEEE 23rd International Conference on Data Engineering. 2007: 106-115.
[4]	TERROVITIS M , MAMOULIS N , KALNIS P . Privacy-preserving anonymization of set-valued data[C]// Very Large Data Base Endowment. 2008: 115-125.
[5]	DWORK C , . Differential privacy[C]// 33rd International Colloquium on Automata,Languages and Programming,part II (ICALP 2006). 2006: 1-12.
[6]	DWORK C , LEI J . Differential privacy and robust statistics[C]// The 41th Annual ACM Symposium on Theory of Computing (STOC). 2009: 371-380.
[7]	FRIEDMAN A , SCHUSTER A . Data mining with differential privacy[C]// The 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2010: 493-502.
[8]	MOHAMMED N , CHEN R , FUNG B ,et al. Differentially private data release for data mining[C]// The 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2011: 493-501.
[9]	ZHU T , XIONG P , XIANG Y ,et al. An effective deferentially private data releasing algorithm for decision tree[C]// 12th IEEE International Conference on Trust,Security and Privacy in Computing and Communications. 2013: 388-395.
[10]	GANTA S R , KASIVISWANATHAN S P , SMITH A . Composition attacks and auxiliary information in data privacy[C]// The 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2008: 265-273.
[11]	ZHU T , LI G , ZHOU W ,et al. Differentially private data publishing and analysis:a survey[J]. IEEE Transactions on Knowledge and Data Engineering, 2017,29(8): 1619-1638.
[12]	熊平, 朱天清, 王晓峰 . 差分隐私保护及其应用[J]. 计算机学报, 2014,37(1): 101-122.
	XIONG P , ZHU T Q , WANG X F . A survey on differential privacy and applications[J]. Chinese Journal of Computers, 2014,37(1): 101-122.
[13]	康海燕, 马跃雷 . 差分隐私保护在数据挖掘中应用综述[J]. 山东大学学报:理学版, 2017(3): 16-23.
	KANG H Y , MA Y L . Survey on application of data ming via differential privacy[J]. Journal of Shangdong University(Natural Science), 2017(3): 16-23.
[14]	张啸剑, 孟小峰 . 面向数据发布和分析的差分隐私保护[J]. 计算机学报, 2014,37(4): 927-949.
	ZHANG X J , MENG X F . Differential privacy in data publication and analysis[J]. Chinese Journal of Computers, 2014,37(4): 927-949.
[15]	BLUM A , DWORK C , MCSHERRY F ,et al. Practical privacy:the SuLQ framework[C]// The Twenty-Fourth ACM SIGMOD-SIGACTSIGART Symposium on Principles of Database Systems. 2005: 128-138.
[16]	MCSHERRY F D , . Privacy integrated queries:an extensible platform for privacy-preserving data analysis[C]// The 2009 ACM SIGMOD International Conference on Management of Data. 2009: 19-30.
[17]	GOSWAMI P , MADAN S . Privacy preserving data publishing and data anonymization approaches:a review[C]// 2017 International Conference on Computing,Communication and Automation. 2017: 139-142.
[18]	FLETCHER S , ISLAM M Z . A differentially private random decision forest using reliable signal-to-noise ratios[C]// Australasian Joint Conference on Artificial Intelligence. 2015: 192-203.
[19]	CORMODE G , PROCOPIUC C , SRIVASTAVA D ,et al. Differentially private spatial decompositions[C]// IEEE 28th International Conference on Data Engineering. 2012: 20-31.
[20]	DWORK C , MCSHERRY F , NISSIM K ,et al. Calibrating noise to sensitivity in private data analysis[C]// Theory of Cryptography Conference. 2006: 265-284.
[21]	MCSHERRY F , TALWAR K . Mechanism design via differential privacy[C]// 48th Annual IEEE Symposium on Foundations of Computer Science. 2007: 94-103.

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MAXGDDP:decision data release with differential privacy

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