Research Article
Additive Sanitization: A Technique for Pattern-Preserving Anonymization for Time-Series Data
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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 |
10.5120/15214-3710
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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
Abstract
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.
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