Privacy-Preserving Clustering on Distributed Databases: A Review and Some Contributions

Huge Volumes of detailed personal data is continuously collected and analyzed by different types ofapplications using data mining, analysing such datais beneficial to the application users. It is an importantasset to application users like business organizations, governments for taking effective decisions. Butanalysing such data opens treats to privacy if notdone properly. This work aims to reveal the informationby protecting sensitive data. Various methods including Randomization, k-anonymity and data hiding havebeen suggested for the same. In this work, a noveltechnique is suggested that makes use of LBG designalgorithm to preserve the privacy of data along with compression of data. Quantization will be performedon training data it will produce transformed data set. It provides individual privacy while allowingextraction of useful knowledge from data, Hence privacy is preserved. Distortion measures are used toanalyze the accuracy of transformed data

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Privacy-Preserving Clustering on Distributed Databases: A Review and Some Contributions

Cited by 2 publications

References 76 publications

Comparison of distributed evolutionary k-means clustering algorithms

Comparison of distributed evolutionary k-means clustering algorithms

Privacy Preserving Data Mining Based on Vector Quantization

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