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프라이버시 보존형 데이터 마이닝 방법 및 척도 분석

Privacy Preserving Data Mining Methods and Metrics Analysis

  • Hong, Eun-Ju (Dept. of Convergence Science, Kongju National University) ;
  • Hong, Do-won (Dept. of Applied Mathematics, Kongju National University) ;
  • Seo, Chang-Ho (Dept. of Applied Mathematics, Kongju National University)
  • 투고 : 2018.08.07
  • 심사 : 2018.10.20
  • 발행 : 2018.10.28

초록

생활의 모든 것들이 데이터화 되어가고 있는 세상에서 데이터의 양은 기하급수적으로 증가하고 있다. 이러한 데이터는 수집 및 분석을 통하여 새로운 데이터로 가공되어진다. 새로운 데이터는 병원, 금융, 기업 등 여러 분야에서 다양한 용도로 사용되고 있다. 그러나 기존의 데이터에는 개인들의 민감한 정보가 포함되어 있기 때문에 수집 및 분석과정에서 개인의 프라이버시 노출 우려가 있다. 해결 방안으로 프라이버시 보존형 데이터 마이닝(PPDM)기술이 있다. PPDM은 프라이버시를 보존하면서 동시에 데이터로부터 유용한 정보를 추출하는 방법이다. 본 논문에서는 PPDM을 조사하고 데이터의 프라이버시와 유틸리티를 평가하기 위한 다양한 측정방법을 분석한다.

In a world where everything in life is being digitized, the amount of data is increasing exponentially. These data are processed into new data through collection and analysis. New data is used for a variety of purposes in hospitals, finance, and businesses. However, since existing data contains sensitive information of individuals, there is a fear of personal privacy exposure during collection and analysis. As a solution, there is privacy-preserving data mining (PPDM) technology. PPDM is a method of extracting useful information from data while preserving privacy. In this paper, we investigate PPDM and analyze various measures for evaluating the privacy and utility of data.

키워드

참고문헌

  1. C. C. Aggarwal. (2015) Data Mining: The Textbook. New York, NY, USA:Springer.
  2. S. Fletcher & M. Z. Islam. (2015) Measuring information quality for privacy preserving data mining. Int. J. Comput. Theory Eng, 7(1), 2128.
  3. Y. A. A. S. Aldeen, M. Salleh & M. A. Razzaque.(2015) A comprehensive review on privacy preserving data mining. SpringerPlus, 4(1), 694. https://doi.org/10.1186/s40064-015-1481-x
  4. S. Yu. (2016). Big privacy: Challenges and opportunities of privacy study in the age of big data. IEEE Access, 4, 2751-2763. https://doi.org/10.1109/ACCESS.2016.2577036
  5. A. Shah & R. Gulati. (2016) Privacy preserving data mining: Techniques, classication and implicationsA survey. Int. J. Comput. Appl, 137(12), 40-46.
  6. R. Mendes & J. P. Vilela.(2017) Privacy-preserving data mining: Methods, metrics, and applications. IEEE Access, 5, 10562-10582. https://doi.org/10.1109/ACCESS.2017.2706947
  7. U. Fayyad, G. Piatetsky-Shapiro & P. Smyth. (1996) From data mining to knowledge discovery in databases. AI Mag, 17(3), 3754.
  8. C. M. Bishop. (2006) Pattern Recognition and Machine Learning. vol. 4. New York, NY, USA: Springer-Verla.
  9. J. Han, M. Kamber & J. Pei. (2012) Data Mining: Concepts and Techniques. Amsterdam, The Netherlands: Elsevier.
  10. C. C. Aggarwal & P. S. Yu. (2008) A general survey of privacy-preserving data mining models and algorithms. in Privacy-Preserving Data Mining. New York, NY, USA: Springer, 1152.
  11. L. Xu, C. Jiang, J. Wang, J. Yuan & Y. Ren. Information security in big data: Privacy and data mining. IEEE Access, 2, 1149-1176.
  12. K. Liu, H. Kargupta & J. Ryan. (2006) Random projection-based multiplicative data perturbation for privacy preserving distributed data mining. IEEE Trans. Knowl. Data Eng, 18(1), 92-106. https://doi.org/10.1109/TKDE.2006.14
  13. L. Sweeney. (2002) K-anonymity: A model for protecting privacy. Int. J.Uncertainty, Fuzziness Knowl.-Based Syst, 10(5), 557-570. https://doi.org/10.1142/S0218488502001648
  14. A. Machanavajjhala, D. Kifer, J. Gehrke & M. Venkitasubramaniam. l-diversity: Privacy beyond k-anonymity. ACM Trans. Knowl. Discovery Data, 1(1), 3.
  15. N. Li, T. Li & S. Venkatasubramanian. t-closeness: Privacy beyond k-anonymity and l-diversity. in Proc. IEEE 23rd Int. Conf. Data Eng, (ICDE), Apr, 106-115.
  16. X. Xiao & Y. Tao. (2006) Personalized privacy preservation. in Proc, VLDB, 139-150.
  17. C. Dwork. (2006) Differential privacy. in Automata, Languages and Programming, 4052. Venice, Italy: Springer-Verlag, Jul. 1-12.
  18. V. S. Verykios. (2013) Association rule hiding methods. Wiley Interdiscipl. Rev., Data Mining Knowl. Discovery, 3(1), 28-36. https://doi.org/10.1002/widm.1082
  19. R. Agrawal & R. Srikant. (2000) Privacy-preserving data mining. ACM SIGMOD Rec, 29(2), 439-450. https://doi.org/10.1145/335191.335438
  20. S. R. Oliveira & O. R. Zaiane. (2002) Privacy preserving frequent itemset mining. in Proc. IEEE Int. Conf. Privacy, Secur. Data Mining, 14 Dec, 43-54.
  21. E. Bertino, D. Lin & W. Jiang. (2008) A survey of quantication of privacy preserving data mining algorithms. in Privacy-Preserving Data Mining. New York, NY, USA: Springer, 183-205.