山东大学学报(工学版) ›› 2015, Vol. 45 ›› Issue (1): 24-29.doi: 10.6040/j.issn.1672-3961.2.2014.120
黄泗勇, 陈婷婷, 卢清, 吴英杰, 叶少珍
HUANG Siyong, CHEN Tingting, LU Qing, WU Yingjie, YE Shaozhen
摘要： 为解决现有基于网格结构的差分隐私二维空间数据划分发布方法可能引起局部划分过细导致查询精度低的问题,提出了基于kd-树的差分隐私二维空间数据划分发布方法—kd-PPDP算法(differentially privacy partitioning publication algorithm based on kd-tree)。算法采用了kd-树算法思想,通过启发式地识别网格化后数据分布情况并合并相邻近似网格单元来防止局部划分过细问题,从而减少所添加的噪声,提高查询精度。通过实验对比分析了kd-PPDP算法与现有基于网格结构的划分发布方法的查询误差以及时间效率,结果表明了该算法的有效性和可行性。
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| [1] 周水庚,李丰,陶宇飞,等.面向数据库应用的隐私保护研究综述[J]. 计算机学报, 2009, 32(5):847-858. ZHOU Shuigeng, LI Feng, TAO Yufei, et al. Privacy preservation in database applications:A survey[J]. Chinese Journal of Computers, 2009, 32(5):847-858. [2] FUNG BCM, WANG Ke, CHEN Rui, et al. Privacy-preserving data publishing:A survey on recent developments[J]. ACM Computing Surveys, 2010, 42(4):1-53. [3] DWORK C. Differential privacy[C]//Proc of the 33rd Int Colloquium on Automata, Languages and Programming. Berlin:Springer, 2006:1-12. [4] DWORK C, MCSHERRY F, NISSIM F K, et al. Calibrating noise to sensitivity in private data analysis[C]//Proceedings of the 3rd Theory of Cryptography Conference (TCC). New York, USA:Springer Berlin Heidelberg, 2006:363-385. [5] SWEENEY L. k-anonymity:A model for protecting privacy[J]. International Journal on Uncertainty, Fuzziness and Knowledge Based Systems, 2002, 10(5):557-570. [6] MACHANAVAJJHALA A, GEHRKE J, KIFER D, et al. l-diversity:Privacy beyond k-anonymity[C]//Proceedings of IEEE ICDE'06. Piscataway, NJ:IEEE, 2006:24-35. [7] LI Ninghui, LI Tiancheng. t-closeness:Privacy beyond k-anonymity and l-diversity[C]//Proceedings of IEEE ICDE'07. Piscataway, NJ:IEEE, 2007:106-115. [8] CHEN R, MOHAMMED N, FUNG B C M, et al. Publishing set-valued data via differential privacy[J]. Proceedings of the VLDB Endowment, 2011, 4(11):1087-1098. [9] CHEN R, ACS G, CASTELLUCCIA C. Differentially private sequential data publication via variable-length n-grams[C]//Proceedings of the 2012 ACM conference on Computer and communications security.Raleigh, NC, USA:ACM, 2012:638-649. [10] CHEN R, FUNG B, DESAI B C, et al. Differentially private transit data publication:A case study on the montreal transportation system[C]//Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. Beijing, China:ACM, 2012:213-221. [11] CORMODE G, PROCOPIUC C, SRIVASTAVA D, et al. Differentially private spatial decompositions[C]//Proceedings of IEEE 28th International Conference on Data Engineering (ICDE), Washington D C. USA:IEEE, 2012:20-31. [12] QARDAJI W, YANG W, LI N. Differentially private grids for geospatial data[C]//Proceedings of IEEE 29th International Conference on Data Engineering (ICDE). Brisbane, Australia:IEEE, 2013:757-768. [13] HAY M, RASTOGI V, MIKLAU G, et al. Boosting the accuracy of differentially private histograms through consistency[J]. Proceedings of the VLDB Endowment, 2010, 3(1-2):1021-1032. [14] McSherry F D. Privacy integrated queries:an extensible platform for privacy-preserving data analysis[C]//Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data. Providence, Rhode Island, USA:ACM, 2009:19-30. |
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