From data privacy to location privacy

Ting Wang, Ling Liu

Research output: Chapter in Book/Report/Conference proceedingChapter

5 Scopus citations

Abstract

Over the past decade, the research on data privacy has achieved considerable advancement in the following two aspects: First, a variety of privacy threat models and privacy principles have been proposed, aiming at providing sufficient protection against different types of inference attacks; Second, a plethora of algorithms and methods have been developed to implement the proposed privacy principles, while attempting to optimize the utility of the resulting data. The first part of the chapter presents an overview of data privacy research by taking a close examination at the achievements from the above two aspects, with the objective of pinpointing individual research efforts on the grand map of data privacy protection. As a special form of data privacy, location privacy possesses its unique characteristics. In the second part of the chapter, we examine the research challenges and opportunities of location privacy protection, in a perspective analogous to data privacy. Our discussion attempts to answer the following three questions: (1) Is it sufficient to apply the data privacy models and algorithms developed to date for protecting location privacy? (2) What is the current state of the research on location privacy? (3) What are the open issues and technical challenges that demand further investigation? Through answering these questions, we intend to provide a comprehensive review of the state of the art in location privacy research.

Original languageEnglish (US)
Title of host publicationMachine Learning in Cyber Trust
Subtitle of host publicationSecurity, Privacy, and Reliability
PublisherSpringer US
Pages217-246
Number of pages30
ISBN (Print)9780387887340
DOIs
Publication statusPublished - Dec 1 2009

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All Science Journal Classification (ASJC) codes

  • Computer Science(all)

Cite this

Wang, T., & Liu, L. (2009). From data privacy to location privacy. In Machine Learning in Cyber Trust: Security, Privacy, and Reliability (pp. 217-246). Springer US. https://doi.org/10.1007/978-0-387-88735-7_9