Preventing automatic user profiling in Web 2.0 applications

Knowledge-Based Systems

Volumn 36, Issue , 2012, Pages 191-205

  Viejo, Alexandre ^a   Sánchez, David ^a   Castellà Roca, Jordi ^a  

^a UNIVERSITAT ROVIRA I VIRGILI   (Spain)

Author keywords

Knowledge based system; Privacy; Profiling; Twitter; Web 2.0

Indexed keywords

EVALUATION RESULTS; INDIVIDUAL USER PROFILE; MICROBLOGGING; PROFILING; PROFILING SYSTEMS; RANDOM DISTORTIONS; TWITTER; USER PROFILE; USER PROFILING; USER-GENERATED CONTENT; WEB 2.0; WEB 2.0 APPLICATIONS;

DATA PRIVACY; KNOWLEDGE BASED SYSTEMS; SOCIAL NETWORKING (ONLINE);

WORLD WIDE WEB;

EID: 84867842712     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2012.07.001     Document Type: Article

References (47)

1. F. Abel, Q. Gao, G.J. Houben, K. Tao, Semantic enrichment of twitter posts for user profile construction on the social web, in: Proc. of the 8th Extended Semantic Web Conference on The Semantic Web: Research and Applications - ESWC'11, 2011, pp. 375-389.
2. D. Abril, G. Navarro-Arribas, V. Torra, On the declassification of confidential documents, in: Proc. of Modeling Decisions for Artificial Intelligence - MDAI'11, 2011, pp. 235-246.
3. H. Achrekar, A. Gandhe, R. Lazarus, S.H. Yu, and B. Liu Predicting flu trends using twitter data Proc. of the IEEE Conference on Computer Communications Workshops - InfoCom 2011 IEEE Computer Society 702 707
4. M. Bernstein, B. Suh, L. Hong, J. Chen, S. Kairam, E. Chi, Eddi: interactive topic-based browsing of social status streams, in: Proc. of the 23nd Annual ACM Symposium on User Interface Software and Technology, 2010, pp. 303-312.
5. B. Bilton, Price of facebook privacy? Start clicking, in: The New York Times, 2010.
6. R. Brand Microdata protection through noise addition Inference Control in Statistical Databases 2002 97 116
7. E. Brown, Twitter exposes historical data in partnership with datasift, ZDNET, 2012.
8. R. Cilibrasi, and P. Vitányi The google similarity distance IEEE Trans. Knowl. Data. Eng. 19 2006 370 383
9. Consumer Reports National Research Center, Annual state of the net survey 2010, Consumer Reports 75, 2010.
10. S. Crimes, Twitter sells old tweets to marketers - should users be worried? The Inquirer, 2012.
11. DBpedia, 2012. < http://dbpedia.org > (last accessed 19.01.12).
12. M. Ebner, H. Mühlburger, S. Schaffert, M. Schiefner, W. Reinhardt, and S. Wheeler Getting granular on twitter: tweets from a conference and their limited usefulness for non-participants Key Compet. Knowl. Soc. 324 2010 102 113
13. C. Fellbaum WordNet: An Electronic Lexical Database 1998 MIT Press
14. D. Freni, C. Ruiz Vicente, S. Mascetti, C. Bettini, and C.S. Jensen Preserving location and absence privacy in geo-social networks Proc. of the 19th ACM International Conference on Information and Knowledge Management 2010 ACM Press
15. S. Garcia Esparza, M.P. O'Mahoney, and B. Smyth Mining the real-time web: a novel approach to product recommendation Knowl.-Based Syst. 29 2012 3 11
16. N. Guarino, Formal ontology in information systems, in: Proc. of the 1st International Conference on Formal Ontology in Information Systems - FOIS'98, 1998, pp. 3-15.
17. S. Guha, K. Tang, P. Francis, NOYB: privacy in online social networks, in: Proc. of the First Workshop on Online Social Networks, 2008.
18. M. Hay, G. Miklau, D. Jensen, D. Towsley, P. Weis, Resisting structural identification in anonymized social networks, in: Proc. of the 2008 Conference on Very Large Databases - VLDB'08, 2008.
19. Hotonline.net, Twitter involved in a huge scandal: selling users private data for marketing strategies, Hotonline.net, 2012.
20. D. Li, Q. Lv, X. Xie, L. Shang, H. Xia, T. Lu, and N. Gu Interest-based real-time content recommendation in online social communities Knowl.-Based Syst. 28 2012 1 12
21. W. Luo, Q. Xie, U. Hengartner, Facecloak: an architecture for user privacy on social networking sites, in: Proc. of the 2009 International Conference on Computational Science and Engineering, 2009, pp. 26-33.
22. L. Marin, D. Isern, A. Moreno, A. Valls, On-line dynamic adaptation of fuzzy preferences, Inf. Sciences, in press, http://dx.doi.org/10.1016/j.ins. 2011.10.008.
23. S. Martínez, D. Sánchez, and A. Valls Semantic adaptive microaggregation of categorical microdata Computers & Security 31 2012 653 672
24. S. Martínez, D. Sánchez, A. Valls, and M. Batet Privacy protection of textual attributes through a semantic-based masking method Inform. Fusion 13 2012 304 314
25. G. McMillan, Twitter reveals active user number, how many actually say something, in: Time - Techland, 2011.
26. M. Michelson, S. Macskassy, Discovering users' topics of interest on twitter: a first look, in: Proc. of the Fourth Workshop on Analytics for Noisy Unstructured Text Data, 2010.
27. Peerindex, 2012. < http://www.peerindex.com > (last accessed 19.01.12).
28. P. Resnik, Using information content to evalutate semantic similarity in a taxonomy, in: Proc. of the 14th International Joint Conference on Artificial Intelligence - IJCAI'95, 1995, pp. 448-453.
29. S. Ross A First Course in Probability 1976 Macmillan
30. H. Rui, and A. Whinston Verification and validation issues in electronic voting Inf. Syst. E-Bussiness Manage. 2011 1 16
31. H. Rui, A. Whinston, E. Winkler, Follow the tweets, in: The Wallstreet Journal, 2009.
32. T. Sakaki, M. Okazaki, and Y. Matsuo Earthquake shakes twitter users: real-time event detection by social sensors Proc. of the ACM 19th Int. Conf. on World Wide Web 2010 ACM Press 851 860
33. G. Salton, and C. Buckley Term-weighting approaches in automatic text retrieval Inf. Proc. Manage. 24 1988 513 523
34. D. Sánchez A methodology to learn ontological attributes from the web Data & Knowl. Eng. 63 2010 573 597
35. D. Sánchez, and M. Batet Semantic similarity estimation in the biomedical domain: an ontology-based information-theoretic perspective J. Biomed. Inform. 44 2011 749 759
36. D. Sánchez, M. Batet, and D. Isern Ontology-based information content computation Knowl.-Based Syst. 24 2011 297 303
37. D. Sánchez, M. Batet, A. Valls, and K. Gibert Ontology-driven web-based semantic similarity J. Intell. Inf. Syst. 35 2010 383 413
38. D. Sánchez, D. Isern, and M. Millan Content annotation for the semantic web: an automatic web-based approach Knowl. Inf. Syst. 27 2011 393 418
39. D. Sánchez, and A. Moreno Learning non-taxonomic relationships from web documents for domain ontology construction Data & Knowl. Eng. 63 2008 600 623
40. R. Scoble, Facebook disabled my account, in: Scobleizer, 2008.
41. P. Turney, Mining the web for synonyms: Pmi-ir versus lsa on toefl, in: Proc. of the 12th European Conference on Machine Learning - ECML'01, 2001, pp. 491-502.
42. TweetPsych, 2012. < http://tweetpsych.com > (last accessed 19.01.12).
43. P. Van Eecke, and M. Truyens Privacy and social networks Comput. Law & Security Rev. 26 2010 535 546
44. Wefollow, 2012. < http://wefollow.com > (last accessed 16.11.11).
45. D. Wilson, Users are worried about social network security and privacy, The Inquirer, 2011.
46. C. Zhang, J. Sun, X. Zhu, and Y. Fang Privacy and security for online social networks: challenges and opportunities IEEE Netw. 24 2010 13 18
47. K. Zoltan, S. Johann, Semantic analysis of microposts for efficient people to people interactions, in: Proc. of the Roedunet International Conference - RoEduNet'11, 2011, pp. 1-4.