International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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Volume 87 - Issue 6 |
Published: February 2014 |
Authors: Revathi. S, Jeyalakshmi. I |
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Revathi. S, Jeyalakshmi. I . Additive Sanitization: A Technique for Pattern-Preserving Anonymization for Time-Series Data. International Journal of Computer Applications. 87, 6 (February 2014), 35-38. DOI=10.5120/15214-3710
@article{ 10.5120/15214-3710, author = { Revathi. S,Jeyalakshmi. I }, title = { Additive Sanitization: A Technique for Pattern-Preserving Anonymization for Time-Series Data }, journal = { International Journal of Computer Applications }, year = { 2014 }, volume = { 87 }, number = { 6 }, pages = { 35-38 }, doi = { 10.5120/15214-3710 }, publisher = { Foundation of Computer Science (FCS), NY, USA } }
%0 Journal Article %D 2014 %A Revathi. S %A Jeyalakshmi. I %T Additive Sanitization: A Technique for Pattern-Preserving Anonymization for Time-Series Data%T %J International Journal of Computer Applications %V 87 %N 6 %P 35-38 %R 10.5120/15214-3710 %I Foundation of Computer Science (FCS), NY, USA
A time series is a set of data normally collected at usual intervals and often contains huge amount of individual privacy. The need to protect privacy and anonymization of time-series while trying to support complex queries such as pattern range and pattern matching queries. The conventional (k, p)-anonymity model cannot effectively address this problem as it may suffer serious pattern loss. In the proposed work a new technique called additive sanitization has been developed which increment the supports of item sets and their subsets in order to reduce pattern loss and prevent linkage attack.