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An efficient data perturbation scheme for preserving privacy on a numerical database

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dc.contributor.author Iuon-Chang Lin
dc.contributor.author Cheng-Yi Tsai
dc.contributor.author Li-Cheng Yang
dc.date.accessioned 2018-08-15T07:50:16Z
dc.date.available 2018-08-15T07:50:16Z
dc.date.issued 2018
dc.identifier.citation Iuon-Chang Lin , Cheng-Yi Tsai and Li-Cheng Yang. (2018). An efficient data perturbation scheme for preserving privacy on a numerical database. International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka. p.138. en_US
dc.identifier.uri http://repository.kln.ac.lk/handle/123456789/19019
dc.description.abstract The data retention within an organization may increase rapidly with time. In order to reduce cost of organization, they may choose a third-party storage provider. There is a leakage crisis when provider cannot be trusted. Another scenario is a dealer collects all transaction data and provides it to a data analysis company for marketing purpose. For these reasons and beyons, preserving privacy in database becomes an important issue. This paper concerns the prediction of disclosure risk in numerical database. It presents an efficient noise generation that relies on Huffman coding algorithm and builds a noise matrix that can add noise intuitively to original value. Moreover, we adopt clustering technique before generating noise. The result shows that the running time of noise generation of clustering scheme is faster than non-clustering scheme. en_US
dc.language.iso en en_US
dc.publisher International Research Conference on Smart Computing and Systems Engineering - SCSE 2018 en_US
dc.subject Database privacy en_US
dc.subject Disclosure control en_US
dc.subject Huffman coding en_US
dc.subject Micro aggregation en_US
dc.subject Noise matrix en_US
dc.title An efficient data perturbation scheme for preserving privacy on a numerical database en_US
dc.type Article en_US


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