[1]Samarati P. Protecting respondents identities in microdata release[J]. IEEE Trans on Knowledge and Data Engineering, 2001, 13(6): 10101027
[2]Srivatsa M S, Hicks M. Deanonymizing mobility traces: Using social network as a sidechannel[C] Proc of the 2012 ACM Conf on Computer and Communications Security. New York: ACM, 2012: 628637
[3]Nyholt D R, Yu ChangEn, Visscher P M. On Jim Watsons APOE status: Genetic information is hard to hide[J]. European Journal of Human Genetics, 2009, 17(2): 147149
[4]Khan R, Mittelman D. Rumors of the death of consumer genomics are greatly exaggerated[J]. Genome Biol, 2013, 14(11): 13
[5]Ye Mao, Yin Peifeng, Lee WangChien, et al. Exploiting geographical influence for collaborative pointofinterest recommendation[C] Proc of the 34th Int ACM SIGIR Conf on Research and Development in Information Retrieval. New York: ACM, 2011: 325334
[6]Goel S, Hofman J M, Lahaie S, et al. Predicting consumer behavior with Web search[J]. Proceedings of the National Academy of Sciences, 2010, 107(41): 1748617490
[7]Sweeney L, Abu A, Winn J. Identifying participants in the personal genome project by name[OL]. 2013 [20151104]. http:ssrn.com
[8]Sweeney L. kAnonymity: A model for protecting privacy 1[J]. International Journal of Uncertainty Fuzziness and KnowledgeBased Systems, 2002, 10(5): 557570
[9]National Research Council (U S). Putting People on the Map: Protecting Confidentiality with Linked SocialSpatial Data[MOL]. National Academies Press, 2007 [20151104]. http:www.nap.edu
[10]Narayanan A, Shmatikov V. How to break anonymity of the netflix prize dataset[JOL]. 2006 [20151104]. http:arxiv.org
[11]Melissa L. This website knows where your cat lives[EBOL]. [20151104]. http:time.com3019671iknowwhereyourcatliveswebsitedatavisualization
[12]de Montjoye Y A, Hidalgo C A, Verleysen M, et al. Unique in the Crowd: The privacy bounds of human mobility[J]. Scientific Reports, 2013, 3(6): 776776
[13]Peddinti ST, Ross K W, Cappos J. On the internet, nobody knows youre a dog: A twitter case study of anonymity in social networks[C] Proc of the 2nd Edition of the ACM Conf on Online Social Networks. New York: ACM, 2014: 8394
[14]Enserink M, Chin G. The end of privacy[J]. Science, 2015, 347(3): 490491
[15]Dalenius T. Towards a methodology for statistical disclosure control[J]. Statistik Tidskrift, 1977, 15(429444): 21
[16]Sweeney L. kanonymity: A model for protecting privacy[J]. International Journal of Uncertainty, Fuzziness and KnowledgeBased Systems, 2002, 10(5): 557570
[17]Machanavajjhala A, Kifer D, Gehrke J, et al. ldiversity: Privacy beyond kanonymity[J]. ACM Trans on Knowledge Discovery from Data (TKDD), 2007, 1(1): 2459
[18]Li Ninghui, Li Tiancheng, Venkatasubramanian S. tcloseness: Privacy beyond kanonymity and ldiversity[C] Proc of the 23rd IEEE Int Conf on Data Engineering (ICDE 2007). Piscataway, NJ: IEEE, 2007: 106115[19]Wong R C W, Li Jiuyong, Fu A W C, et al. (α, k)anonymity: An enhanced kanonymity model for privacy preserving data publishing[C] Proc of the 12th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining. New York: ACM, 2006: 754759
[20]Xiao Xiaokui, Tao Yufei. Minvariance: Towards privacy preserving republication of dynamic datasets[C] Proc of the 2007 ACM SIGMOD Int Conf on Management of Data. New York: ACM, 2007: 689700
[21]Wong R C W, Fu A W C, Wang Ke, et al. Minimality attack in privacy preserving data publishing[C] Proc of the 33rd Int Conf on Very Large Data Bases. New York: ACM, 2007: 543554
[22]Ganta S R, Kasiviswanathan S P, Smith A. Composition attacks and auxiliary information in data privacy[C] Proc of the 14th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining. New York: ACM, 2008: 265273[23]Wong R C W, Fu A W C, Wang Ke, et al. Can the utility of anonymized data be used for privacy breaches?[J]. ACM Trans on Knowledge Discovery from Data (TKDD), 2011, 5(3): 16(1)16(24)
[24]Kifer D. Attacks on privacy and de finetti’s theorem[C] Proc of 2009 ACM SIGMOD Int Conf. New York: ACM, 2009: 127138
[25]Dwork C. Differential privacy[C] Proc of Encyclopedia of Cryptography and Security. Berlin: Springer, 2011: 338340
[26]Haeberlen A, Pierce B C, Narayan A. Differential privacy under fire[C] Proc of Usenix Security Symposium, 2011: 3333
[27]Hardt M, Rothblum G N. A multiplicative weights mechanism for privacypreserving data analysis[C] Proc of the 51st Annual IEEE Symp on Foundations of Computer Science (FOCS). Piscataway, NJ: IEEE, 2010: 6170
[28]Dinur I, Nissim K. Revealing information while preserving privacy[C] Proc of ACM SIGMODPODS Conf. New York: ACM, 2003: 202210
[29]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): 101122
[30]Dwork C, McSherry F, Nissim K et al. Calibrating noise to sensitivity in private data analysis[C] Proc of Theory of Cryptography. Berlin: Springer, 2006: 265284
[31]Friedman A, Schuster A. Data mining with differential privacy[C] Proc of the 16th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining. New York: ACM, 2010: 493502
[32]Machanavajjhala A, Korolova A, Sarma A D. Personalized social recommendations: accurate or private[J]. Proceedings of the VLDB Endowment, 2011, 4(7): 440450
[33]McSherry F, Mironov I. Differentially private recommender systems: Building privacy into the net[C] Proc of the 15th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining. New York: ACM, 2009: 627636
[34]Zhu Tianqing, Li gang, Ren Yongli, et al. Differential privacy for neighborhoodbased collaborative filtering[C] Proc of the 2013 IEEEACM Int Conf on Advances in Social Networks Analysis and Mining. New York: ACM, 2013: 752759
[35]Zhu Tianqing, Li gang, Ren Yongli, et al. Privacy preserving for tagging recommender systems[C] Proc of 2013 IEEEWICACM Int Joint Conf on Web Intelligence (WI) and Intelligent Agent Technologies (IAT). Piscataway, NJ: IEEE, 2013: 8188
[36]Chen Rui, Fung B, Desai B C, et al. Differentially private transit data publication: A case study on the montreal transportation system[C] Proc of the 18th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining. New York: ACM, 2012: 213221
[37]McSherry F, Mahajan R. Differentiallyprivate network trace analysis[J]. ACM SIGCOMM Computer Communication Review, 2011, 41(4): 123134
[38]Johnson A, Shmatikov V. Privacypreserving data exploration in genomewide association studies[C] Proc of the 19th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining. New York: ACM, 2013:10791087
[39]Kifer D, Machanavajjhala A. No free lunch in data privacy[C] Proc of the 2011 ACM SIGMOD Int Conf on Management of Data. New York: ACM, 2011: 193204
[40]Chan T H H, Shi Elaine, Song Dawn. Private and continual release of statistics[C] Proc of Automata, Languages and Programming. Berlin: Springer, 2010: 405417
[41]McGregor A, Mironov I, Pitassi T, et al. The limits of twoparty differential privacy[C] Proc of the 51st Annual IEEE Symp on Foundations of Computer Science (FOCS). Piscataway, NJ: IEEE, 2010: 8190
[42]Beimel A, Nissim K, Omri E. Distributed private data analysis: Simultaneously solving how and what[C] Proc of Advances in CryptologyCRYPTO 2008. Berlin: Springer, 2008: 451468
[43]Gehrke J, Hay M, Lui E, et al. Crowdblending privacy[C] Proc of Advances in CryptologyCRYPTO 2012. Berlin: Springer, 2012: 479496 |