Regulating privacy in wireless advertising messaging: FIPP compliance by policy vs. by design

Heng Xu, John W. Bagby, Terence Ryan Melonas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

This research analyzes consumer privacy issues pertaining to the newly developing wireless marketing context, specifically, wireless advertising messaging (WAM). We develop a conceptual framework named as DIGs (Design innovation/Industry self-regulation/Government regulation/Standards) to assess the efficacy of industry self-regulation, government regulation, and technological solutions in ensuring consumer privacy in WAM. In addition to enhancing our theoretical understanding of WAM privacy, these findings have important implications for WAM service providers, mobile consumers, as well as for regulatory bodies and technology developers.

Original languageEnglish (US)
Title of host publicationPrivacy Enhancing Technologies - 9th International Symposium, PETS 2009, Proceedings
Pages19-36
Number of pages18
DOIs
StatePublished - Sep 15 2009
Event9th International Symposium on Privacy Enhancing Technologies, PETS 2009 - Seattle, WA, United States
Duration: Aug 5 2009Aug 7 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5672 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th International Symposium on Privacy Enhancing Technologies, PETS 2009
CountryUnited States
CitySeattle, WA
Period8/5/098/7/09

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All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Xu, H., Bagby, J. W., & Melonas, T. R. (2009). Regulating privacy in wireless advertising messaging: FIPP compliance by policy vs. by design. In Privacy Enhancing Technologies - 9th International Symposium, PETS 2009, Proceedings (pp. 19-36). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5672 LNCS). https://doi.org/10.1007/978-3-642-03168-7_2