Privacy preservation on time series

Spiros Papadimitriou, Feifei Li, George Kollios, Philip S. Yu

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Time series data are sequence of values based on observations at periodic time instants and appear in a wide range of domains and applications, such as financial, retail, environmental and process monitoring, defense and health care. Additionally, massive volumes of data from various sources are continuously collected. However, when a data owner wants to publish this data he may not be willing to exactly reveal the true values due to various reasons, most notably privacy considerations. A widely employed and accepted approach for partial information hiding is based on random perturbation [4], which introduces uncertainty about individual values. Consider the following examples: (E1) A driver installing a vehicle monitoring system [5, 35] may not wish to reveal his exact speed. How can he, e.g., avoid revealing small violations of the speed limit (say, by 3-5 mph) but still allow mining of general driving patterns or detection of excessive speeding?.

Original languageEnglish (US)
Title of host publicationPrivacy-Aware Knowledge Discovery
Subtitle of host publicationNovel Applications and New Techniques
PublisherCRC Press
Pages241-263
Number of pages23
ISBN (Electronic)9781439803660
ISBN (Print)9781439803653
DOIs
StatePublished - Jan 1 2010
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

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