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Integrating user feedback with heuristic security and privacy management systems

Published:07 May 2011Publication History

ABSTRACT

Tools aimed at helping users safely navigate the web and safeguard themselves against potential online predators have become reasonably common. Currently there are two families of tools; heuristics analysis tools that test websites directly using automated scripts and programs, and community based tools where users rate websites and write reviews for the benefit of others. In this paper we examine the relative strengths and weaknesses of each technique, whether these techniques are compatible, and how community feedback can be combined with heuristic-based evaluations. In order to do this we conduct a large-scale comparison of the ratings of heuristic and community based tools, and explore novel methods for abstracting key information from user comments, which could be used to add context and nuance to heuristic based ratings. We find that heuristic and community based ratings are highly complementary, and can be combined to potentially guide users to make more informed decisions.

References

  1. Alexa., http://alexa.com.Google ScholarGoogle Scholar
  2. AlpineLinux., http://www.alpinelinux.org/wiki.Google ScholarGoogle Scholar
  3. Amatriain, X., Pujol, J. M., Tintarev, N., Rate it Again: Increasing Recommendation Accuracy by User re-Rating. RecSys'09, October 23--25, 2009, New York, USA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. AOL explorer., http://www.aol.com/.Google ScholarGoogle Scholar
  5. Artviper., http://www.artviper.net/texttagcloud/.Google ScholarGoogle Scholar
  6. Avira., http://www.avira.com/en/pages/index.php.Google ScholarGoogle Scholar
  7. Bilgic, M., and Mooney, R. J., Explaining recommendations: Satisfaction vs. promotion. In Proceedings of Beyond Personalization Workshop, IUI, 2005. San Diego, California, USA.Google ScholarGoogle Scholar
  8. Brightmail., http://www.symantec.com/business/products/family.jsp?familyid=brightmail.Google ScholarGoogle Scholar
  9. Chen, M., Singh, J. P., Computing and Using Reputations for Internet Ratings. EC'01, October 14-17, 2001, Tampa, Florida, USA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Dellarocas, C., Immunizing Online Reputation Reporting Systems Against Unfair Ratings and Discriminatory Behavior. EC'00, October 17-20, 2000, Minneapolis, Minnesota. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Dourish, P., Grinter, R., Delgado de la Flor, J. and Joseph, M. Security in the Wild: User Strategies for Managing Security as an Everyday, Practical Problem. Personal and Ubiquitous Computing, 8 (6). 391--401. Google ScholarGoogle ScholarCross RefCross Ref
  12. Edwards, W. K., Poole, E. S. and Stoll, J., Security Automation Considered Harmful? In Proceedings of the IEEE New Security Paradigms Workshop 2007, (White Mountain, New Hampshire, 2007). Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Goecks, J., Edwards, W. K., and Mynatt, E. D., Challenges in Supporting End-User Privacy and Security Management with Social Navigation. Symposium On Usable Privacy and Security (SOUPS) 2009, July 15-17 2009, Mountain View, CA, USA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Gross, J. and Rosson, M. B., Looking for Trouble: Understanding End-User Security Management. in 2007 Computer Human Interaction for the Management of Information Technology, (2007), ACM Press, 10. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Herlocker, J., Konstan, J., and Riedl, J., Explaining collaborative filtering recommendations. In Proceedings of the ACM Conference on Computer Supported Cooperative Work, 2000. CHI Letters 5(1). Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Hu, N., Pavlou, P. A., Zhang, J., Can Online Reviews Reveal a Product's True Quality? Empirical Findings and Analytical Modeling of Online Word-of-Mouth Communication. EC'06, June 11-15, 2006, Ann Arbor, Michigan, USA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Ipfire., http://www.ipfire.org/en/index.Google ScholarGoogle Scholar
  18. Jin, R., Si, L., A Study of Methods for Normalizing User Ratings in Collaborative Filtering. SIGIR'04, July 25-29, 2004, Sheffield, South Yorkshire, UK. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. McAfee., http://www.mcafee.com/us/.Google ScholarGoogle Scholar
  20. McAfee SiteAdvisor., http://www.siteadvisor.com/.Google ScholarGoogle Scholar
  21. McAfee SpamKiller., http://us.mcafee.com/root/product.asp?productid=msk.Google ScholarGoogle Scholar
  22. McNee, S., Kapoor, N. and Konstan, J. A., Don't Look Stupid: Avoiding Pitfalls when Recommending Research Papers. In 2006 Conference on Computer-Support Cooperative Work (CSCW 2006), (2006), 171--180. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Mozilla Firefox anti-phishing., http://www.mozilla.com/en-US/firefox/phishing-protection/.Google ScholarGoogle Scholar
  24. Norton., http://us.norton.com/index.jsp.Google ScholarGoogle Scholar
  25. Norton SafeWeb., http://safeweb.norton.com/.Google ScholarGoogle Scholar
  26. Sen, S., Lam, S. K., Rashid, A. M., Cosley, D., Frankowski, D., Osterhouse, J., Harper, F. M., and Riedl, J., tagging, communities, vocabulary, evolution. In Proceedings of the ACM 2006 Conference on CSCW, Banff, Alberta, Canada, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Sinha, R., and Swearingen, K., The role of transparency in recommender systems. In CHI '02: CHI '02 extended abstracts on Human factors in computing systems, pages 830--831, New York, NY, USA, 2002. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Vig, J., Sen, S., Riedl, J., Tagsplanations: Explaining Recommendations Using Tags. IUI'09, February 8 - 11, 2009, Sanibel Island, Florida, USA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Spamassassin., http://spamassassin.apache.org/.Google ScholarGoogle Scholar
  30. SpyBot., http://www.safer-networking.org/en/index.html.Google ScholarGoogle Scholar
  31. Svensson, M., Höök, K., Laaksolahti, J. and Waern, A., Social Navigation of Food Recipes. in 2001 Conference on Human Factors in Computing, (2001), 341--348. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Tagcrowd., http://tagcrowd.com.Google ScholarGoogle Scholar
  33. Untangle., http://www.untangle.com/.Google ScholarGoogle Scholar
  34. Web of Trust., http://www.mywot.com/.Google ScholarGoogle Scholar
  35. Webroot popup washer., http://www.webroot.com/En_US/consumer-products-spysweeper.html.Google ScholarGoogle Scholar
  36. Wordle., http://www.wordle.net/create.Google ScholarGoogle Scholar
  37. Zdziarski, J. A., Ending spam: Bayesian content filtering and the art of statistical language classification. Google ScholarGoogle ScholarDigital LibraryDigital Library

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            cover image ACM Conferences
            CHI '11: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
            May 2011
            3530 pages
            ISBN:9781450302289
            DOI:10.1145/1978942

            Copyright © 2011 ACM

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            Publication History

            • Published: 7 May 2011

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            CHI '11 Paper Acceptance Rate410of1,532submissions,27%Overall Acceptance Rate6,199of26,314submissions,24%

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