Authors : J. Gitanjali , Shaik Nusrath Banu , J. Indumathi and G.V. Uma
Abstract: Knowledge is preeminence and the more conversant we are about information burglarizes; we are a lesser amount of prone to fall prey to the malevolence hacker sharks of information technology. The information technology is eternally emerging and we are all beyond uncertainty infinitesimal gravels in a extraterrestrial aquatic of information. Knowledge is pre-eminence, but as humble users of the most modern technologies we are pitted with possessions that may even make us paranoid concerning usage of a computer. The goal of privacy-preserving data mining is to liberate a dataset that researchers can study without being able to identify sensitive information about any individuals in the data (with high probability). One technique for privacy-preserving data mining is to replace the sensitive items by unknown values. For many situations it is safer if the sanitization process consign unknown values as a substitute of fake values. This obscures the susceptible rules, whilst defending the punter of the data commencing false rules. In this study, we remove some items which are sensitive from the transactional database at the same time retaining knowledge for sharing information. In this research, we have adapted the one-scan SWA christening it as Panglossian Solitary-Skim Sanitization algorithm and used it for Privacy Preserving Data Archaeology. We have utilised the notion of disclosure threshold for each lone pattern to curb and provide an enhanced agility allowing an administrator to place disparate weights for varied rules. Our research overcomes the privacy breach problem of existing blocking sanitizing approaches. We have investigated how probabilistic and information theoretic techniques can be applied to this problem. More complete analysis of the effectiveness of this Panglossian Solitary-Skim Sterilization for Privacy Preserving Data Archeology (P3SPPDA) based technique and formal study of the problem has been made. Our experiment reveals that our algorithm is competent, scalable and achieves noteworthy enhancement in excess of the other approaches offered in the literature. Our preliminary domino effect point toward deterministic algorithms for privacy preserved data and accuracy by controlling disclosure of sensitive data and knowledge.
J. Gitanjali , Shaik Nusrath Banu , J. Indumathi and G.V. Uma , 2008. A Panglossian Solitary-Skim Sanitisation for Privacy Preserving Data Archeology. International Journal of Electrical and Power Engineering, 2: 154-165.