Abstract
The development of technology in different domains facilitated organizations to collect huge amount of data for analysis purpose. Such data is analyzed by data mining tools to find trends, relationships and outcomes to enhance business activities and discover new patterns that may allow them to serve their customers and society in a better way. The data collected from various sources also consists of sensitive information. The application of data mining techniques on such databases may violate the privacy of an individual by revealing the information which is private and confidential. The threat of privacy due to data mining results has triggered development of many privacy-preserving data mining techniques.. In this paper we discussed the negative side of randomized additive perturbation technique and proposed a framework SARP which protects privacy by integrating basic data modification techniques. The experimental results shows that better privacy protection can be achieved with this framework than randomized additive perturbation technique.
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Kamakshi, P., Vinaya Babu, A. (2011). Framework to Reduce the Hiding Failure Due to Randomized Additive Data Modification PPDM Technique. In: Das, V.V., Thankachan, N. (eds) Computational Intelligence and Information Technology. CIIT 2011. Communications in Computer and Information Science, vol 250. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25734-6_11
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DOI: https://doi.org/10.1007/978-3-642-25734-6_11
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