skip to main content
10.1145/1754239.1754274acmotherconferencesArticle/Chapter ViewAbstractPublication PagesedbtConference Proceedingsconference-collections
research-article

Collaborative data privacy for the web

Published:22 March 2010Publication History

ABSTRACT

While data privacy is a human right, it is challenging to enforce it. For example, if multiple retailers execute a single order at Amazon Marketplace, each retailer can use different agencies for shipment, payment etc., resulting in unmanageable flows of personal data. In this work, we present the Privacy 2.0 system, which enables people to share experiences, observations, and recommendations regarding the privacy practices of data collectors. The basis of Privacy 2.0 is a folksonomy where a user community tags web sites on the Internet with privacy-related labels, e.g., "no privacy policy" or "collects too much personal data". Privacy 2.0 evaluates this folksonomy, and issues a warning if a user is about to enter a web site that has been marked with alarming tags by the majority of users. We have evaluated an operative implementation of our approach by means of a user study. The study indicates that the Privacy 2.0 system helps to assess the privacy practices of service providers and adapts well to a wide range of privacy threats.

References

  1. E. Buchmann, K. Böhm, and O. Raabe. Privacy2.0: Towards collaborative data-privacy protection. In Trust Management II, volume 263 of IFIP International Federation for Information Processing, pages 247--262. Springer-Verlag, Boston, MA, USA, 2008.Google ScholarGoogle ScholarCross RefCross Ref
  2. T. Burghardt, E. Buchmann, and K. Böhm. Why do privacy-enhancement mechanisms fail, after all? a survey of both, the user and the provider perspective. In Workshop W2Trust, in conjunction with IFIPTM'08, 2008.Google ScholarGoogle Scholar
  3. T. Burghardt et al. A study on the lack of enforcement of data protection acts. In Proceedings of the 3rd International Conference on e-Democracy, 2009.Google ScholarGoogle Scholar
  4. K. Dave, S. Lawrence, and D. M. Pennock. Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In WWW '03: Proceedings of the twelfth international conference on World Wide Web, pages 519--528. ACM Press, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Electronic Privacy Information Center. Pretty Poor Privacy: An Assessment of P3P and Internet Privacy. Available at http://www.epic.org/reports/prettypoorprivacy.html, 2000.Google ScholarGoogle Scholar
  6. European Parliament and the Council of the European Union. Directive 95/46/EC on the protection of individuals with regard to the processing of personal data and on the free movement of such data. Official Journal L 281, 11/23/1995, p. 31., 1995.Google ScholarGoogle Scholar
  7. U. Farooq, T. G. Kannampallil, Y. Song, C. H. Ganoe, J. M. Carroll, and L. Giles. Evaluating tagging behavior in social bookmarking systems: metrics and design heuristics. In GROUP '07: Proceedings of the 2007 international ACM conference on Supporting group work, pages 351--360, New York, NY, USA, 2007. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. O. for Economic Cooperation and D. (OECD). Guidelines on the Protection of Privacy and Transborder Flows of Personal Data, 1980.Google ScholarGoogle Scholar
  9. S. Golder and B. A. Huberman. The structure of collaborative tagging systems, Aug 2005.Google ScholarGoogle Scholar
  10. P. Heymann, G. Koutrika, and H. Garcia-Molina. Fighting spam on social web sites: A survey of approaches and future challenges. IEEE Internet Computing, 11:36--45, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. M. Hu and B. Liu. Mining and summarizing customer reviews. In KDD '04: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 168--177, New York, NY, USA, 2004. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. K. Lerman. User participation in social media: Digg study. CoRR, abs/0708.2414, 2007.Google ScholarGoogle Scholar
  13. C. Marlow, M. Naaman, D. Boyd, and M. Davis. Ht06, tagging paper, taxonomy, flickr, academic article, to read. In HYPERTEXT '06: Proceedings of the seventeenth conference on Hypertext and hypermedia, pages 31--40, New York, NY, USA, 2006. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. A. W. Rivadeneira, D. M. Gruen, M. J. Muller, and D. R. Millen. Getting our head in the clouds: toward evaluation studies of tagclouds. In CHI '07: Proceedings of the SIGCHI conference on Human factors in computing systems, pages 995--998, New York, NY, USA, 2007. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. S. Sen, S. K. Lam, A. M. Rashid, D. Cosley, D. Frankowski, J. Osterhouse, F. M. Harper, and J. Riedl. tagging, communities, vocabulary, evolution. In CSCW '06: Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work, pages 181--190, New York, NY, USA, 2006. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. S. Spiekermann, J. Grossklags, and B. Berendt. E-privacy in 2nd generation e-commerce: privacy preferences versus actual behavior. In EC '01: Proceedings of the 3rd ACM conference on Electronic Commerce, pages 38--47, New York, NY, USA, 2001. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Collaborative data privacy for the web

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Other conferences
      EDBT '10: Proceedings of the 2010 EDBT/ICDT Workshops
      March 2010
      290 pages
      ISBN:9781605589909
      DOI:10.1145/1754239

      Copyright © 2010 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 22 March 2010

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      Overall Acceptance Rate7of10submissions,70%

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader