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Additive Sanitization: A Technique for Pattern-Preserving Anonymization for Time-Series Data
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 87 - Number 6 |
| Year of Publication: 2014 |
| Authors: Revathi. S, Jeyalakshmi. I |
10.5120/15214-3710
|
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
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.
References
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