Additive Sanitization: A Technique for Pattern-Preserving Anonymization for Time-Series Data
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10.5120/15214-3710 |
Revathi. S and Jeyalakshmi. I. Article: Additive Sanitization: A Technique for Pattern-Preserving Anonymization for Time-Series Data. International Journal of Computer Applications 87(6):35-38, February 2014. Full text available. BibTeX
@article{key:article,
author = {Revathi. S and Jeyalakshmi. I},
title = {Article: 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},
month = {February},
note = {Full text available}
}
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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