Privacy preserving classification mining

被引：4

作者：

Department of Computing and Information Technology, Fudan University, Shanghai 200433, China ^{[1
]}

机构：

[1] Department of Computing and Information Technology, Fudan University

来源：

Jisuanji Yanjiu yu Fazhan | 2006年 / 1卷 / 39-45期

关键词：

Classification; Data mining; Decision tree; Privacy preserving; Transition probability matrix;

D O I：

10.1360/crad20060107

中图分类号：

学科分类号：

摘要：

Privacy preserving classification mining is one of the fast-growing sub-areas of data mining. How to perturb original data and then build a decision tree based on perturbed data is the key research challenge. By applying transition probability matrix a novel privacy preserving classification mining algorithm is proposed, which suits non-char type data (Boolean, categorical, and numeric type) and non-uniform probability distribution of original data, and can perturb label attribute. Experimental results demonstrate that the decision tree built using this algorithm on perturbed data has a classifying accuracy comparable to that of the decision tree built using un-privacy-preserving algorithm on original data.

引用

页码：39 / 45

页数：6

共 11 条

[1]

Agrawal R., Data mining: Crossing the chasm, The 5th Int'l Conf. Knowledge Discovery in Databases and Data Mining, (1999)

[2]

Agrawal R., Srikant R., Privacy-preserving data mining, The ACM SIGMOD Conf. Management of Data, (2000)

[3]

Lindell Y., Pinkas B., Privacy preserving data mining, Advances in Cryptology-Crypto, pp. 36-54, (2000)

[4]

Agrawal D., Aggarwal C.C., On the design and quantification of privacy preserving data mining algorithms, 20th Symposium on Principles of Database Systems, (2001)

[5]

Du W., Zhan Z., Using randomized response techniques for privacy-preserving data mining, The 9th ACM SIGKDD Int'l Conf. Knowledge Discovery in Databases and Data Mining, (2003)

[6]

Cranor L.F., Reagle J., Ackerman M.S., Beyond concern: Understanding net users' attitudes about online privacy, (1999)

[7]

Quinlan J.R., C4.5: Programs for Machine Learning, (1993)

[8]

Agrawal R., Ghost S., Imielinski T., An interval classifier for database mining applications, Proc. VLDB Conf., (1992)

[9]

Breiman L., Friedman J.H., Olshen R.A., Classification and Regression Trees, (1984)

[10]

Han J., Kamber M., Data Mining: Concepts and Techniques, (2001)

← 1 2 →