Affiliations: [a] Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, Guangdong, China | [b] School of IT and Mathematical Sciences, University of South Australia, South Australia, Australia | [c] College of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, China | [d] School of Computer and Information Science, Southwest University, Chongqing, China
Correspondence:
[*]
Corresponding author: Jeng-Shyang Pan, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, Guangdong, China. E-mail:[email protected]
Abstract: When data mining techniques are applied to discover
useful knowledge behind a large data collection, they are often required to
preserve some confidential information, such as sensitive frequent itemsets,
rules and so on. A feasible way to ensure the confidentiality is to sanitize
the database and conceal sensitive information. However, the sanitization
process often produces side effects, thus minimizing these side effects is
an important task. An important but ignored fact is that a tradeoff exists
within different side effects. When attempting to improve the performance on
one dimension, the performance on other dimensions often will be degraded.
In this paper, we focus on privacy preserving in association rule mining.
Since there is a tradeoff within different side effects, we tried to
minimize them from the view of multi-objective optimization. A rule hiding
approach based on evolutionary multi-objective optimization (EMO) is
proposed. It hides sensitive rules through removing identified items. The
side effects on missing non-sensitive rules, ghost rules and data loss are
formulated as optimization objectives. EMO is utilized to find a suitable
subset of transactions for modification so that side effects can be
minimized. Experimental results on real datasets illustrate that the
proposed approach can achieve satisfactory results with fewer side effects.
In addition, the EMO-based approach can produce multiple hiding solutions in
a single run. It provides the opportunity for a user to choose freely the
preferred one by preference or experience.
Keywords: Privacy preserving data mining, association rule hiding, evolutionary multi-objective optimization, EMO
