Abstract
Association rule hiding has been playing a vital role in sensitive knowledge preservation when sharing data between enterprises. The aim of association rule hiding is to remove sensitive association rules from the released database such that side effects are reduced as low as possible. This research proposes an efficient algorithm for hiding a specified set of sensitive association rules based on intersection lattice of frequent itemsets. In this research, we begin by analyzing the theory of the intersection lattice of frequent itemsets and the applicability of this theory into association rule hiding problem. We then formulate two heuristics in order to (a) specify the victim items based on the characteristics of the intersection lattice of frequent itemsets and (b) identify transactions for data sanitization based on the weight of transactions. Next, we propose a new algorithm for hiding a specific set of sensitive association rules with minimum side effects and low complexity. Finally, experiments were carried out to clarify the efficiency of the proposed approach. Our results showed that the proposed algorithm, AARHIL, achieved minimum side effects and CPU-Time when compared to current similar state of the art approaches in the context of hiding a specified set of sensitive association rules.
Highlights
Data mining has been recently applied in many areas of science and business, such as traffic accident detection [1], engineering asset health and reliability prediction [2], assessment of landslide susceptibility [3], enterprises [4], and supply chain management [5]
The HCSRIL algorithm applied heuristic on victim item selection based on intersection lattice theory
This study introduced in detail the theories of intersection lattice of frequent itemsets, denoted by L(D, σ), and proposed an improvement to minimize size effects and complexity of intersection lattice-based approach
Summary
Data mining has been recently applied in many areas of science and business, such as traffic accident detection [1], engineering asset health and reliability prediction [2], assessment of landslide susceptibility [3], enterprises [4], and supply chain management [5]. The discovery of association rules is one of the major techniques of data mining that extracts correlative patterns from large databases Such rules create assets that organizations can use to expand their businesses, improve profitability, decrease supply chain costs, increase the efficiencies of collaborative product developments, and support more effective marketing [4, 5]. In 2012, Hai and Somjit [23] introduced a new direction for hiding a specific set of sensitive association rules named intersection lattice based. This approach concentrated on formulating heuristics for specifying victim items and transactions for data sanitization based on intersection lattice theories.
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