- Volume 16 Issue 10
DOI QR Code
Privacy Preserving Data Mining Methods and Metrics Analysis
프라이버시 보존형 데이터 마이닝 방법 및 척도 분석
- 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)
- Received : 2018.08.07
- Accepted : 2018.10.20
- Published : 2018.10.28
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.
Privacy;Data Mining;Privacy-Preserving Data Mining;Metric;Utility
Supported by : National Research Foundation of Korea(NRF)
- C. C. Aggarwal. (2015) Data Mining: The Textbook. New York, NY, USA:Springer.
- S. Fletcher & M. Z. Islam. (2015) Measuring information quality for privacy preserving data mining. Int. J. Comput. Theory Eng, 7(1), 2128.
- 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
- 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
- A. Shah & R. Gulati. (2016) Privacy preserving data mining: Techniques, classication and implicationsA survey. Int. J. Comput. Appl, 137(12), 40-46.
- 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
- U. Fayyad, G. Piatetsky-Shapiro & P. Smyth. (1996) From data mining to knowledge discovery in databases. AI Mag, 17(3), 3754.
- C. M. Bishop. (2006) Pattern Recognition and Machine Learning. vol. 4. New York, NY, USA: Springer-Verla.
- J. Han, M. Kamber & J. Pei. (2012) Data Mining: Concepts and Techniques. Amsterdam, The Netherlands: Elsevier.
- 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.
- L. Xu, C. Jiang, J. Wang, J. Yuan & Y. Ren. Information security in big data: Privacy and data mining. IEEE Access, 2, 1149-1176.
- 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
- 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
- A. Machanavajjhala, D. Kifer, J. Gehrke & M. Venkitasubramaniam. l-diversity: Privacy beyond k-anonymity. ACM Trans. Knowl. Discovery Data, 1(1), 3.
- 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.
- X. Xiao & Y. Tao. (2006) Personalized privacy preservation. in Proc, VLDB, 139-150.
- C. Dwork. (2006) Differential privacy. in Automata, Languages and Programming, 4052. Venice, Italy: Springer-Verlag, Jul. 1-12.
- 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
- R. Agrawal & R. Srikant. (2000) Privacy-preserving data mining. ACM SIGMOD Rec, 29(2), 439-450. https://doi.org/10.1145/335191.335438
- 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.
- 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.