Abstract :
As extensive chronicles of information contain classified rules that must be protected before distributed, association rule hiding winds up one of basic privacy preserving data mining issues. Information sharing between two associations is ordinary in various application zones for instance business planning or marketing. Profitable overall patterns can be found from the incorporated dataset. In any case, some delicate patterns that ought to have been kept private could likewise be uncovered. Vast disclosure of touchy patterns could diminish the forceful limit of the information owner. Database outsourcing is becoming a necessary business approach in the ongoing distributed and parallel frameworks for incessant things identification. This paper focuses on introducing a few adjustments to safeguard both customer and server privacy. Adjustment strategies like hash tree to existing APRIORI algorithm are recommended that will be helping in safeguarding the accuracy, utility loss and data privacy and result is generated in small execution time. We implement the modified algorithm to two custom datasets of different sizes. Garvit Khurana "Association Rule Hiding using Hash Tree" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23037.pdf Paper URL: https://www.ijtsrd.com/computer-science/data-miining/23037/association-rule-hiding-using-hash-tree/garvit-khurana
Keyword :
AssociationRuleMining, ModifiedAPRIORI, FrequentItemsetMining, HashTree