7 articles for “Privacy preserving data mining”

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Research ArticleOpen accessPhrase in the titleUtility Independent Privacy Preserving Data Mining on Vertically Partitioned DataE. Poovammal, M. PonnavaikkoProblem statement: Driven by mutual benefits, or by regulations that require certain data to be published, there has been a demand for the exchange and publication of data among various parties. Data publishing has been ubiquitous in many domains such as medical, business and education. Detailed person-specific data, present in the centralized server or in the distributed environment, in its … …Journal of Computer Science, Vol. 5, Issue 9 · 30 Sep 2009 · 6,835 accessesView PDF Cite

Review ArticleOpen accessEvery word in the titleA Survey of Data Anonymization Techniques for Privacy-Preserving Mining in BigdataHelen Wilfred Raj, Santhi BalachandranBigdata era is seeing the data burst occurring in a multitude of angles that are better expressed in terms of the 4Vs (Volume, Velocity, Velocity, Veracity). While trying to infer information from data, care should be exercised as not to reveal the identity of the data owner, which breaches the privacy rights. Leakage of information can happen right from the … …Journal of Computer Science, Vol. 16, Issue 2 · 31 Dec 2019 · 8,060 accessesView PDF Cite

Research ArticleOpen accessIn the keywordsPrivacy Preserved Collaborative Secure Multiparty Data MiningM. Balamurugan, J. Bhuvana, S. Chenthur PandianProblem statement: In the current modern business environment, its success is defined by collaboration, team efforts and partnership, rather than lonely spectacular individual efforts in isolation. So the collaboration becomes especially important because of the mutual benefit it brings. Sometimes, such collaboration even occurs among competitors, or among companies that have conflict of interests, but the collaborators are aware that … …Journal of Computer Science, Vol. 8, Issue 6 · 16 Mar 2012 · 7,161 accessesView PDF Cite

Research ArticleOpen accessIn the keywordsPRESERVATION OF THE PRIVACY FOR MULTIPLE CUSTODIAN SYSTEMS WITH RULE SHARINGB. Murugeshwari, C. Jaya Kumar, K. SarukesiThe aim of the research work is to mine the data set available with each custodian in a semi honest model, securely without disclosure of any data amongst various custodians involved. No custodian discloses any information. In the proposed scheme in order to reduce the computational complexity, the data partitioning has been done in the horizontal way. The proposed research … …Journal of Computer Science, Vol. 9, Issue 9 · 6 Aug 2013 · 6,311 accessesView PDF Cite

Research ArticleOpen accessIn the abstractHybrid Algorithm for Privacy Preserving Association Rule MiningIla Chandrakar, Yelipe Usha Rani, Mortha Manasa, Kondabala RenukaProblem statement: The objective of the hybrid algorithm for privacy preserving data mining is to hide certain sensitive information so that they cannot be discovered through association rule mining techniques. Approach: The sensitive items whether in Left Hand Side (LHS) or Right Hand Side (RHS) of the rule cannot be inferred through association rule mining algorithms by combining the concept … …Journal of Computer Science, Vol. 6, Issue 12 · 22 Nov 2010 · 7,334 accessesView PDF Cite

Research ArticleOpen accessIn the abstractAn Effective Data Transformation Approach for Privacy Preserving ClusteringR. R. Rajalaxmi, A. M. NatarajanA new stream of research privacy preserving data mining emerged due to the recent advances in data mining, Internet and security technologies. Data sharing among organizations considered to be useful which offer mutual benefit for business growth. Preserving the privacy of shared data for clustering was considered as the most challenging problem. To overcome the problem, the data owner published … …Journal of Computer Science, Vol. 4, Issue 4 · 30 Apr 2008 · 7,112 accessesView PDF Cite

Research ArticleOpen accessIn the abstractCustomized Privacy Preservation Using Unknowns to Stymie Unearthing Of Association RulesJ. Indumathi, G.V. UmaThe explosions of new data mining techniques has augmented privacy risks because now it is probable to powerfully coalesce and cross-examine massive data stores, accessible on the web, in the rummage around of earlier unidentified hidden patterns. Consecutively to make a overtly accessible system safe and sound, we must guarantee not only that private sensitive data have been trimmed out, … …Journal of Computer Science, Vol. 3, Issue 11 · 30 Nov 2007 · 7,117 accessesView PDF Cite