Privacy Protection Practice for Data Mining with Multiple Data Sources: An Example with Data Clustering

被引:2
|
作者
O'Shaughnessy, Pauline [1 ]
Lin, Yan-Xia [1 ]
机构
[1] Univ Wollongong, Sch Math & Appl Stat, Wollongong, NSW 2522, Australia
关键词
data masking; multiplicative noise; data mining; sample size calculation;
D O I
10.3390/math10244744
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
In the age of data, data mining provides feasible tools with which to handle large datasets consisting of data from multiple sources. However, there is limited research on retrieving statistical information from data when data are confidential and cannot be shared directly. In this paper, we address this problem and propose a framework for performing data analysis using data from multiple sources without revealing true values for privacy purposes. The proposed framework includes three steps. First, data custodians individually mask data before publishing; then, the masked data collection is used to reconstruct the density function of the original dataset, from which resampled values are generated; last, existing data mining techniques are applied directly to the resampled data. This framework utilises the technique of reconstructing an original density function from noise-masked data using the moment-based density estimation method, which plays an essential role. Simulation studies show that the proposed framework performs well; analysis results from the resampled data are comparable to those of the original data when the density of the original data is estimated well. The proposed framework is demonstrated in data clustering analysis using the example of a real-life Australian soybean dataset. Results from the k-means algorithms with two and three fitted clusters are presented to show that cluster analysis using resampled data can well replicate that of the original data.
引用
收藏
页数:13
相关论文
共 50 条
  • [41] Privacy preserving data mining
    Lindell, Y
    Pinkas, B
    JOURNAL OF CRYPTOLOGY, 2002, 15 (03) : 177 - 206
  • [42] Privacy during Data Mining
    Kumari, Aruna
    Rao, K. Rajasekhara
    Suman, M.
    EMERGING ICT FOR BRIDGING THE FUTURE, VOL 2, 2015, 338 : 593 - 600
  • [43] Privacy Preserving Data Mining
    Journal of Cryptology, 2002, 15 : 177 - 206
  • [44] Output Privacy in Data Mining
    Wang, Ting
    Liu, Ling
    ACM TRANSACTIONS ON DATABASE SYSTEMS, 2011, 36 (01):
  • [45] Information Security in Big Data: Privacy and Data Mining
    Xu, Lei
    Jiang, Chunxiao
    Wang, Jian
    Yuan, Jian
    Ren, Yong
    IEEE ACCESS, 2014, 2 : 1149 - 1176
  • [46] Data mining and privacy: An overview
    Clifton, CW
    Mulligan, DK
    Ramakrishnan, R
    PRIVACY AND TECHNOLOGIES OF IDENTITY: A CROSS-DISCIPLINARY CONVERSATION, 2006, : 191 - 208
  • [47] Privacy in Data Mining: A Review
    Dutta, Sharmistha
    Gupta, Ankit Kumar
    PROCEEDINGS OF THE 10TH INDIACOM - 2016 3RD INTERNATIONAL CONFERENCE ON COMPUTING FOR SUSTAINABLE GLOBAL DEVELOPMENT, 2016, : 556 - 559
  • [48] Data Mining, the Internet, and privacy
    Broder, AJ
    WEB USAGE ANALYSIS AND USER PROFILING, 2000, 1836 : 56 - 73
  • [49] Privacy preserving data mining
    Lindell, Y
    Pinkas, B
    ADVANCES IN CRYPTOLOGY-CRYPTO 2000, PROCEEDINGS, 2000, 1880 : 36 - 54
  • [50] Incorporating privacy concerns in data mining on distributed data
    Shen, Hui-zhang
    Zhao, Ji-di
    Yao, Ruipu
    ARTIFICIAL INTELLIGENCE: METHODOLOGY, SYSTEMS, AND APPLICATIONS, PROCEEDINGS, 2006, 4183 : 87 - 97