References
- L.Sweeney, "k-anonymity: A model for protecting privacy," International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 10, no.5, pp.557-570, 2002. https://doi.org/10.1142/S0218488502001648
- Office for government Policy Coordination, Prime Minister's Secretariat, Ministry of the Interior and Safety, Korea Communications Commission, Financial Services Commission, Ministry of Science and ICT, Ministry of Health & Welfare, "Guidelines for data de-identification - Guidance on de-identification standard, support and management system,", https://www.privacy.go.kr/inf/gdl/selectBoardArticle.do?nttId=7187&bbsId=BBSMSTR_000000000044&bbsTyCode=BBST01&bbsAttrbCode=BBSA03&authFlag=Y&pageIndex=1&searchCnd=&searchWrd=&replyLc=0&nttSj, June, 2016.
- J.Kim, "Presentation of data linkage case of SK Telecom: Creation and distribution demonstration of personal information de-identification data," Seminar on de-identified demonstaration for big data on the fourth industrial revolution, 2017.
- A.Machanavajjhala, D.Kifer, J.Gehrke and M.Venkitasubramaniam, "L-diversity: Privacy beyond k-anonymity," ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 1, no. 1, Article 3, 2007.
-
C.Wong, J.Li, W.Fu and K.Wang, "(
${\alpha}$ , k)-anonymity: an enhanced k-anonymity model for privacy-preserving data publishing," Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.754-759, 2006. - N.Li, T.Li, and S.Venkatasubramanian, "t-closeness: Privacy beyond k-anonymity and l-diversity," Data Engineering, 2007. ICDE 2007. IEEE 23rd International Conference on, pp. 106-115, April, 2007.
- N.Mohammed, R.Chen, B.Fung and P.S.Yu, "Differentially private data release for data mining," Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp.493-501, 2011.
- A.Narayanan and V.Shmatikov, "Robust de-anonymization of large sparse datasets," Security and Privacy, IEEE Symposium on, pp. 111-125, May, 2008.
- P.Ohm, "Broken Promises of Privacy: Responding to the Surprising Failure of Anonymization," UCLA Law Review, Research Information Network, vol.57, no.6, pp-1701-1777, 2009.
-
C.Dwork, A.Roth, "The algorithmic foundations of differential privacy," Foundations and Trends
$^{(R)}$ in Theoretical Computer Science, pp.211-407, 2014. - C.Dwork, F.McSherry, L.Nissim and A.Smith, "Calibrating noise to sensitivity in private data analysis, " Third Theory of Cryptography Conference(TCC), vol.3876, pp.265-284, 2006.
- C.Park, D.Hong, C.Seo "Differentially private data release method for general use of data," Korea Computer Congress, pp.1036-1038, 2017.
- Financial Security Institue, "Present condition on introduction for domestic and foreign financial machine learning techniques," http://www.fsec.or.kr/user/bbs/fsec/42/312/bbsDataView/899.do, 2017.
- K.Ligett, "Introduction to differential privacy, randomized response, basic properties," The 7th BIU Winter School on Cryptography, BIU, 2017.
- J.Wang, S.Liu and Y.Li, "A review of differential privacy in individual data release," International Journal of Distributed Sensor Networks, vol.11, no.10, 2015.
- F.McSherry and K.Talwar, "Mechanism design via differential privacy,", Foundations of Computer Science, pp.94-103, 2007.
- C. Dwork, G. N. Rothblum, and S. P. Vadhan, "Boosting and differential privacy," Foundations of Computer Science, pp 51-60. 2010.
- F.McSherry, "Privacy integrated queries: an extensible platform for privacy-preserving data analysis," Communications of the ACM, vol. 53, no. 9, pp. 89-97, 2010. https://doi.org/10.1145/1810891.1810916
- S.L.Garfinkel, "NISTIR8053: De-identification of personal information," Technical report, National Institute of Standards Technology, 2015.
- B.C.Fung, K.Wang, P.S.Yu, "Top-down specialization for information and privacy preservation, " Data Engineering, Proceedings 21st International Conference on IEEE, pp.205-216, 2005.
- J.Gardner, L.Xiong, Y.Xiao, J.Gao, A.R.Post, X.Jiang and L.Ohno-Machado, "SHARE: system design and case studies for statistical health information release," Journal of the American Medical Informatics Association, vol.20, no.1, pp.109-116, 2012.
- Y.Xiao, L.Xiong, C.Yuan, "Differentially private data release through multidimensional partitioning,", Secure Data Management, pp.150-168, 2010.
- J.L.Bentley, "Multidimensional binary search trees used for associative searching,", Communications of the ACM, vol.18, no.9, pp.509-517, 1975. https://doi.org/10.1145/361002.361007
- Y.Lim, "Evaluation and future challenges of de-identification techniques," Big data utilization and privacy protection: Information technology solution for object conflicts, Financial Information Society of Korea, Korea Money and Finance Association, Common policy symposium on spring, 2017.
- "https://onthemap.ces.census.gov/", OnTheMap.
- A.Machanvajjhala, D.Kifer, J.Abowd, J.Gehrke and L.Vilhuber, "Privacy: Theory meets practice on the map," Data Engineering, IEEE 24th International Conference on, pp.277-286, 2008.
- N.Li, W.H.Qardaji and D.Su, "Provably private data anonymization:Or, k-anonymity meets differential privacy, " CERIAS Technical Report, 2010.
- Z.Ji, Z.Lipton and C.Elkan, "Differential privacy and machine learning: a survey and review," arXiv preprint, 2014.
- J.R. Quinlan, "Induction of decision trees," Machine learning, vol.1, no.1, pp.81-106, 1986. https://doi.org/10.1007/BF00116251
- J.R. Quinlan, C4.5: Programs for machine learning, Elsevier, 2014.
- S.Fletcher, M.Z.Islam, "Decision tree classfication with differential privacy: A Survey,", arXiv preprint, 2016.
- S.P.Kasiviswanathan, H.K.Lee, K.Nissim, S.Raskhodnikova and A.Smith, "What can we learn privately?," SIAM Journal on Computing, vol.40, no.3, pp.793-826, 2011. https://doi.org/10.1137/090756090
- U.Erlingsson, V.Pihur and A.Korolova, "RAPPOR: Randomized aggregatable privacy-preserving ordinal response, " Proceedings of the 2014 ACM SIGSAC conference on computer and communications security, pp.1054-1067, 2014.
- Google, "Chrome Privacy Whitepaper, "https://www.google.co.kr/intl/ko/chrome/browser/privacy/whitepaper.html
- Apple, "guides and sample code," https://developer.apple.com/library/content/releasenotes/General/WhatsNewIniOS/Articles/iOS10.html
- L.Fan and L.Xiong, "Differentially private anomaly detection with a case study on epidemic outbreak detection," Data Mining Workshops, IEEE 13th International Conference on, pp.833-840, 2013.
- J.Reed and B.C.Pierce, "Distance makes the types grow stronger: a calculus for differential privacy," ACM Sigplan Notices, vol.45, no.9, pp.157-168, 2010. https://doi.org/10.1145/1932681.1863568
- M.Gaboardi, A.Haeberlen, J.Hsu, A.Narayan and B.C.Pierce, "Linear dependent types for differential privacy," ACM SIGPLAN Notices, vol.48, no.1, pp.357-370, 2013.
- A.Friedman and A.Schuster, "Data mining with differential privacy," Proceedings of the 16th ACM SIGKDD International Conference on Konwledge Discovery and Data Mining, pp.493-502, 2010.
- J.Gardner and L.Xiong, "HIDE: an integrated system for health information DE-identification," Computer-Based Medical Systems, 2008.
- Financial Security Institute, "Survey on machine learning technologies," http://www.fsec.or.kr/user/bbs/fsec/42/312/bbsDataView/355.do?page=7&column=&search=&searchSDate=&searchEDate=&bbsDataCategory=
- UCI Repository, "German Credit Data, https://archive.ics.uci.edu/ml/datasets/Statlog+%28German+Credit+Data%29
- R.Shokri, M.Stronati, C.Song and V.Shmatikov, "Membership inference attacks against machine learning models," Security and Privacy, IEEE Symposium on, pp.3-18, 2017.
- M.Fredrikson, S.Jha and T.Ristenpart, "Model inversion attacks that exploit confidence information and basic countermeasures," Proceedings of the 22nd ACM SIGSAC conference on computer and communications security, pp.1322-1333, 2015.