A Frequent Itemsets Data Mining Algorithm Based on Differential Privacy

DOI

10.2991/cimns-16.2016.63How to use a DOI?

Keywords

differential privacy; data mining; frequent itemsets; privacy protection

Abstract

Differential privacy is a new privacy protection technology, which defines a strict and strong privacy protection model, by adding noise data distortion to achieve the purpose of privacy protection. Frequent pattern mining is an important field in data mining, and its purpose is to find frequent patterns in data set, but the content of the model itself, rules, and counting information is likely to lead to leaking sensitive information. This paper presents a frequent item sets mining method based on differential privacy, named DPFM, which adopts the mining strategy combined with Laplace system and index system, realizing the difference privacy under the premise of guaranteeing performance calculation of privacy protection. Experiments demonstrate that the proposed algorithm, DPFM has an advantage in decreasing error rate, and the convergence rate under two indexes is better than TF method.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Qingpeng Li
AU  - Longjun Zhang
AU  - Haoyu Li
AU  - Wenjun Sun
PY  - 2016/09
DA  - 2016/09
TI  - A Frequent Itemsets Data Mining Algorithm Based on Differential Privacy
BT  - Proceedings of the 2016 International Conference on Communications, Information Management and Network Security
PB  - Atlantis Press
SP  - 251
EP  - 253
SN  - 2352-538X
UR  - https://doi.org/10.2991/cimns-16.2016.63
DO  - 10.2991/cimns-16.2016.63
ID  - Li2016/09
ER  -