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Additive Sanitization: A Technique for Pattern-Preserving Anonymization for Time-Series Data

International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 87 - Number 6
Year of Publication: 2014
Revathi. S
Jeyalakshmi. I

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

	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}


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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