DISTORTION-BASED HEURISTIC METHOD FOR SENSITIVE ASSOCIATION RULE HIDING

In the past few years, privacy issues in data mining have received considerable attention in the data mining literature. However, the problem of data security cannot simply be solved by restricting data collection or against unauthorized access, it should be dealt with by providing solutions that  not only protect sensitive information, but also not affect to the accuracy of the results in data mining and not violate the sensitive knowledge related with individual privacy or competitive advantage in businesses. Sensitive association rule hiding is an important issue in privacy preserving data mining. The aim of association rule hiding is to minimize the side effects on the sanitized database, which means to reduce the number of missing non-sensitive rules and the number of generated ghost rules. Current methods for hiding sensitive rules cause side effects and data loss. In this paper, we introduce a new distortion-based method to hide sensitive rules. This method proposes the determination of critical transactions based on the number of non-sensitive maximal frequent itemsets that contain at least one item to the consequent of the sensitive rule, they can be directly affected by the modified transactions. Using this set, the number of non-sensitive itemsets that need to be considered is reduced dramatically. We compute the smallest number of transactions for modification in advance to minimize the damage to the database. Comparative experimental results on real datasets showed that the proposed method can achieve better results than other methods with fewer side effects and data loss

Mining Target-Oriented Sequential Patterns with Time-Intervals

A target-oriented sequential pattern is a sequential pattern with a concerned itemset in the end of pattern. A time-interval sequential pattern is a sequential pattern with time-intervals between every pair of successive itemsets. In this paper we present an algorithm to discover target-oriented sequential pattern with time-intervals. To this end, the original sequences are reversed so that the last itemsets can be arranged in front of the sequences. The contrasts between reversed sequences and the concerned itemset are then used to exclude the irrelevant sequences. Clustering analysis is used with typical sequential pattern mining algorithm to extract the sequential patterns with time-intervals between successive itemsets. Finally, the discovered time-interval sequential patterns are reversed again to the original order for searching the target patterns.Comment: 11 pages, 9 table

A GA-Based Approach to Hide Sensitive High Utility Itemsets

A GA-based privacy preserving utility mining method is proposed to find appropriate transactions to be inserted into the database for hiding sensitive high utility itemsets. It maintains the low information loss while providing information to the data demanders and protects the high-risk information in the database. A flexible evaluation function with three factors is designed in the proposed approach to evaluate whether the processed transactions are required to be inserted. Three different weights are, respectively, assigned to the three factors according to users. Moreover, the downward closure property and the prelarge concept are adopted in the proposed approach to reduce the cost of rescanning database, thus speeding up the evaluation process of chromosomes

Reducing Side Effects of Hiding Sensitive Itemsets in Privacy Preserving Data Mining

Data mining is traditionally adopted to retrieve and analyze knowledge from large amounts of data. Private or confidential data may be sanitized or suppressed before it is shared or published in public. Privacy preserving data mining (PPDM) has thus become an important issue in recent years. The most general way of PPDM is to sanitize the database to hide the sensitive information. In this paper, a novel hiding-missing-artificial utility (HMAU) algorithm is proposed to hide sensitive itemsets through transaction deletion. The transaction with the maximal ratio of sensitive to nonsensitive one is thus selected to be entirely deleted. Three side effects of hiding failures, missing itemsets, and artificial itemsets are considered to evaluate whether the transactions are required to be deleted for hiding sensitive itemsets. Three weights are also assigned as the importance to three factors, which can be set according to the requirement of users. Experiments are then conducted to show the performance of the proposed algorithm in execution time, number of deleted transactions, and number of side effects

Revisiting Numerical Pattern Mining with Formal Concept Analysis

In this paper, we investigate the problem of mining numerical data in the framework of Formal Concept Analysis. The usual way is to use a scaling procedure --transforming numerical attributes into binary ones-- leading either to a loss of information or of efficiency, in particular w.r.t. the volume of extracted patterns. By contrast, we propose to directly work on numerical data in a more precise and efficient way, and we prove it. For that, the notions of closed patterns, generators and equivalent classes are revisited in the numerical context. Moreover, two original algorithms are proposed and used in an evaluation involving real-world data, showing the predominance of the present approach