By Aris Gkoulalas-Divanis
Privacy and defense dangers coming up from the appliance of other info mining thoughts to massive institutional facts repositories were exclusively investigated through a brand new learn area, the so-called privateness keeping facts mining. organization rule hiding is a brand new procedure on information mining, which stories the matter of hiding delicate organization principles from in the facts.
Association Rule Hiding for facts Mining addresses the optimization challenge of “hiding” delicate organization ideas which as a result of its combinatorial nature admits a few heuristic strategies that might be proposed and awarded during this publication. distinct strategies of elevated time complexity which were proposed lately also are awarded in addition to a couple of computationally effective (parallel) methods that alleviate time complexity difficulties, besides a dialogue concerning unsolved difficulties and destiny instructions. particular examples are supplied all through this publication to assist the reader examine, assimilate and relish the real features of this demanding challenge.
Association Rule Hiding for facts Mining is designed for researchers, professors and advanced-level scholars in laptop technology learning privateness protecting info mining, organization rule mining, and information mining. This publication can be compatible for practitioners operating during this industry.
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Additional info for Association Rule Hiding for Data Mining
1, we highlight the goals of association rule hiding algorithms, rank these goals in terms of importance of being satisfied, as well as discuss the side-effects that are introduced when each of them is left unsatisfied in the sanitized database. 2, we deliver the formal problem statement. For notational convenience, we will use Bd + and Bd − to refer to the positive and the negative border of a set of itemsets, respectively, when the set of frequent itemsets is obvious in our context. 1 Goals of Association Rule Hiding Methodologies Association rule hiding methodologies aim at sanitizing the original database in a way that at least one of the following goals is accomplished: 1.
All the nonsensitive rules that appear when mining the original database at prespecified thresholds of confidence and support can be successfully mined from the sanitized database at the same thresholds or higher, and 3. No rule that was not derived from the original database when the database was mined at pre-specified thresholds of confidence and support, can be derived from its sanitized counterpart when it is mined at the same or at higher thresholds. The first goal requires that all the sensitive rules disappear from the sanitized database, when the database is mined under the same thresholds of support and confidence as the original database, or at higher thresholds.
52], offer an alternative to suppression-based techniques. These approaches target at reconstructing the original database by using only supporting transactions of the nonsensitive rules. As discussed in , reconstructionbased approaches are advantageous when compared to heuristic data modification algorithms, since they hardly introduce any side-effects to the hiding process. They operate as follows. First, they perform rule-based classification to the original database to enable the data owner to identify the sensitive rules.