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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 逄愛君(Ai-Chun Pang) | |
dc.contributor.author | Chih-Hsien Yeh | en |
dc.contributor.author | 葉治顯 | zh_TW |
dc.date.accessioned | 2021-06-16T10:23:07Z | - |
dc.date.available | 2018-08-26 | |
dc.date.copyright | 2013-08-26 | |
dc.date.issued | 2013 | |
dc.date.submitted | 2013-08-16 | |
dc.identifier.citation | [1] C. Vicente, D. Freni, C. Bettini, and C. S. Jensen, ``Location-related privacy in geo-
social networks,' Internet Computing, IEEE, vol. 15, no. 3, pp. 20--27, 2011. [2] R. Dey, Z. Jelveh, and K. W. Ross, ``Facebook users have become much more private: A large-scale study,' in PerCom Workshops'12, 2012, pp. 346--352. [3] J. He, W. W. Chu, and Z. V. Liu, ``Inferring privacy information from social networks,' in Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics, ser. ISI'06. Berlin, Heidelberg: Springer-Verlag, 2006, pp. 154--165. [Online]. Available: http://dx.doi.org/10.1007/11760146_14 [4] W. Xu, X. Zhou, and L. Li, ``Inferring privacy information via social relations,' in Data Engineering Workshop, 2008. ICDEW 2008. IEEE 24th International Confer- ence on, 2008, pp. 525--530. [5] E. Zheleva and L. Getoor, ``To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles,' in Proceedings of the 18th international conference on World wide web, ser. WWW '09. New York, NY, USA: ACM, 2009, pp. 531--540. [Online]. Available: http: //doi.acm.org/10.1145/1526709.1526781 [6] T. Pontes, G. Magno, M. Vasconcelos, A. Gupta, J. Almeida, P. Kumaraguru, and V. Almeida, ``Beware of what you share: Inferring home location in social networks,' in Data Mining Workshops (ICDMW), 2012 IEEE 12th International Conference on, 2012, pp. 571--578. [7] P. Kozikowski and G. Groh, ``Inferring profile elements from publicly available social network data,' in Privacy, security, risk and trust (passat), 2011 ieee third international conference on and 2011 ieee third international conference on social computing (socialcom), 2011, pp. 876--881. [8] M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten, ``The weka data mining software: an update,' SIGKDD Explor. Newsl., vol. 11, no. 1, pp. 10--18, Nov. 2009. [Online]. Available: http://doi.acm.org/10.1145/1656274. 1656278 [9] K. Thomas, C. Grier, and D. M. Nicol, ``unfriendly: multi-party privacy risks in social networks,' in Proceedings of the 10th international conference on Privacy enhancing technologies, ser. PETS'10. Berlin, Heidelberg: Springer- Verlag, 2010, pp. 236--252. [Online]. Available: http://dl.acm.org/citation.cfm?id= 1881151.1881165 [10] G. Kotyuk and L. Buttyan, ``A machine learning based approach for predicting undis- closed attributes in social networks,' in Pervasive Computing and Communications Workshops (PERCOM Workshops), 2012 IEEE International Conference on, 2012, pp. 361--366. [11] L. Takac and M. Zabovsky, ``Data analysis in public social networks,' International Scientific Conference & International Workshop Present Day Trends of Innovations, May 2012. [12] N. Friedman, L. Getoor, D. Koller, and A. Pfeffer, ``Learning probabilistic relational models,' in In IJCAI. Springer-Verlag, 1999, pp. 1300--1309. [13] S. Keerthi, K. Duan, S. Shevade, and A. Poo, ``A fast dual algorithm for kernel logistic regression,' Machine Learning, vol. 61, no. 1-3, pp. 151--165, 2005. [Online]. Available: http://dx.doi.org/10.1007/s10994-005-0768-5 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/60604 | - |
dc.description.abstract | Currently, online social networks (OSNs) have become one of the most popular web
services. Billions of people share personal information and build social relations with others. Many service providers are interested in users' living areas for improving their quality of service. To gather users' living areas, one possible solution is to collect infor- mation of living areas from OSNs. Unfortunately, most of the users don't want to share this information to the public. An inference technique is needed for these service providers in order to develop these applications. In this thesis, we investigate and apply several methods including most-frequent, bayesian, and support vector machine (SVM) algorithms to infer users' living areas. An iterative algorithm is designed and implemented to infer user living area information while considering users' inclination to conceal personal data. We con- ducted our experiment by applying our method to a real world dataset, and the inference accuracy of the proposed method is (over than) 60%, which is much higher compared to non-iterative methods (13%). | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T10:23:07Z (GMT). No. of bitstreams: 1 ntu-102-R00944014-1.pdf: 1779512 bytes, checksum: 16b452d5a3cbec29a0f0434c644e705f (MD5) Previous issue date: 2013 | en |
dc.description.tableofcontents | 口試委員會審定書 i
摘要 ii Abstract iii Contents iv List of Figures v List of Tables vi 1 Introduction 1 2 Related Work 3 3 Analysis 5 3.1 Dataset . . . . . . . . . . . . . 5 3.2 Problem Definition . . . . . . . 5 3.3 Existed Inference Model . . . . 6 3.3.1 Most-Frequent Inference 6 3.3.2 Bayesian Inference . . . 7 3.3.3 Support Vector Machine 8 4 Iterative Inference 11 5 Experimental Result 13 6 Conclusions 17 Bibliography 18 | |
dc.language.iso | en | |
dc.title | 社群網路之用戶隱私資料推知技術 | zh_TW |
dc.title | User Profile Data Inference for Online Social Network | en |
dc.type | Thesis | |
dc.date.schoolyear | 101-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 張宏慶(Hung-Chin Jang),蘇淑茵(Sok-Ian Sou),周承復(Cheng-Fu Chou) | |
dc.subject.keyword | 社群網路,推知,貝式定理, | zh_TW |
dc.subject.keyword | Social Network,Inference,Bayesian Theorem, | en |
dc.relation.page | 19 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2013-08-16 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊網路與多媒體研究所 | zh_TW |
顯示於系所單位: | 資訊網路與多媒體研究所 |
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ntu-102-1.pdf 目前未授權公開取用 | 1.74 MB | Adobe PDF |
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