In recent years, the use of data mining techniques and related applications has increased a lot as it is used to extract important knowledge from large amount of data. This has increased the disclosure risks to sensitive information when the data is released to outside parties. Database containing sensitive knowledge must be protected against unauthorized access. Seeing this it has become necessary to hide sensitive knowledge in database. To address this problem, Privacy Preservation Data Mining (PPDM) include association rule hiding method to protect privacy of sensitive data against association rule mining. In this paper, we survey existing approaches to association rule hiding, along with some open challenges. We have also summarized few of the recent evolution. Keywords Association rule hidingAn association rule is in the form X => Y, where X and Y are the subsets of item set in I, XI, YI, and X∩Y=Ø. In the rule X => Y, where X is called the antecedent (left-hand-side) and Y is the consequent (right-hand-side). Association rule mining generates high number of rules and only few of them are of interest. To solve interest measurement problem, minimum support and minimum confidence thresholds are
Dynamic: Distributed computing is the best innovation today for every one of those individuals who needs to go with least speculation on foundation and needs to redistribute the weight of taking care of specialized issues to outsider by paying the charges for the administrations used. Today there is gigantic measure of interest from the customers to utilize cloud innovation as it gives various highlights and remove the heap of looking afterfoundation. This has made a tremendous measure of burden on servers . So it is must to deal with issues identified with load adjusting. This is essentially to see that the heap on a specific server is held most extreme to its edge level. So it can deal with the undertaking and furthermore can finish it in a quicker way. It limits the cost and time associated with the major computational models and improves appropriate usage of assets and framework execution. Numerous calculations are prescribed by different specialists from everywhere throughout the world to take care of the issue of burden adjusting. In this paper, we present another calculation named as combo calculation to address the issue of burden adjusting in a cloud situation. Catchphrases -Cloud registering improvement Load Balancing NetworkI.
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