NDLI: Privacy Preserving Burst Detection of Distributed Time Series Data Using Linear Transforms

Content Provider	IEEE Xplore Digital Library
Author	Singh, L. Sayal, M.
Copyright Year	2007
Description	Author affiliation: Dept. of Comput. Sci., Georgetown Univ., Washington, DC (Singh, L.)
Abstract	In this paper, we consider burst detection within the context of privacy. In our scenario, multiple parties want to detect a burst in aggregated time series data, but none of the parties want to disclose their individual data. Our approach calculates bursts directly from linear transform coefficients using a cumulative sum calculation. In order to reduce the chance of a privacy breech, we present multiple data perturbation strategies and compare the varying degrees of privacy preserved. Our strategies do not share raw time series data and still detect significant bursts. We empirically demonstrate this using both real and synthetic distributed data sets. When evaluating both privacy guarantees and burst detection accuracy, we find that our percentage thresholding heuristic maintains a high degree of privacy while accurately identifying bursts of varying widths
Starting Page	646
Ending Page	653
File Size	1358591
Page Count	8
File Format	PDF
ISBN	1424407052
DOI	10.1109/CIDM.2007.368937
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-03-01
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer science Data privacy Hospitals Aggregates Voting Government Regression analysis Data mining Association rules Computational intelligence
Content Type	Text
Resource Type	Article

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