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Researchers have proposed several mechanisms to secure data from unauthorized use but there is very less work in the field of detecting and managing an authorized or trustworthy agent that has caused a data leak to some third party advertently or unknowingly. In this paper, we implement methods aimed at improving the odds of detecting such leakages when a distributer's sensitive data has been leaked...
Advancement of technology and use of distributed database has made an increasing concern about privacy of private data. Collaboration and team work brings huge and demanding result. But organizations are unwilling to participate for data mining due to data leakage. For availing huge demanding result, it is required to use and collect data from different participating parties in a secured way, that...
Association rule mining finds the strong association between the large items of data sets. In this paper we mainly focus on the privacy using Ck secure sum in randomized response technique. Because when the data is distributed among different sites in horizontal partitioning across multiple sites and the sites wish to collaborate to identify valid association rule globally. However the sites must...
Data mining algorithms extract knowledge from very large data. In most cases the large data has to be shared among multiple users where security plays a vital role. This paper deals with an efficient privacy preserving decision tree construction by reducing communication and computation cost while performing secure cardinality of scalar product during tree induction. The algorithm scales well with...
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