請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/65512
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 吳家麟(Ja-Ling Wu) | |
dc.contributor.author | Jyh-Ren Shieh | en |
dc.contributor.author | 謝致仁 | zh_TW |
dc.date.accessioned | 2021-06-16T23:47:34Z | - |
dc.date.available | 2017-08-10 | |
dc.date.copyright | 2012-08-10 | |
dc.date.issued | 2012 | |
dc.date.submitted | 2012-07-23 | |
dc.identifier.citation | [AMAZON 2010]AMAZON MECHANICAL TURK https://requester.mturk.com/
[Atallah 2001] Atallah, M. AND Du, W. 2001 Secure Multi-Party Computational Geometry. In Lecture Notes in Computer Science, 2125, Springer Verlag. In Proceedings of 7th International Workshop on Algorithms and Data Structures (WADS 2001), August, 8-10, 2001, Providence, Rhode Island, USA.165-179. [Baeza-Yates and Tiberi 2007]Baeza-Yates, R., AND Tiberi, A. 2007. Extracting Semantic Relations from Query Logs. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2007), 76-85. [Bamba 2008] Bamba, B., Liu, L. Pesti, P. AND Wang, T. 2008 Supporting anonymous location queries in mobile environments with privacygrid,” In Proceedings of the 17th International World Wide Web Conference (WWW 2008), 2008, 237–246. [Becerra-Fernandez 2001]Becerra-Fernandez, I. 2001. Searching for Experts with Expertise-Locator Knowledge Management Systems. In Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics (ACL 2001) Workshop Human Language Technology and Knowledge Management, 9-16. [Beeferman 2000]Beeferman, D., AND Berger, A. 2000. Agglomerative Clustering of a Search Engine Query Log. In Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD 2000), 407-416 [Benz 2010] Benz, D., Hotho A., R. Jäschke, R., Krause, B., Mitzlaff, F., Schmitz, C., AND Stumme, G. 2010. The Social Bookmark and Publication Management System BibSonomy. In the VLDB Journal, 19(6), 849-875. [Blei et al. 2003] Blei, D. M. Ng, A. Y., AND Jordan, M. I. 2003. latent Dirichlet allocation. Journal of Machine Learning Research 3(5) 993-1022. [Budanitsky et al. 2006]Budanitsky, A. AND Hirst, G., 2006. Evaluating WordNet-based Measures of Lexical Semantic Relatedness. Computational Linguistics, 32, 13-47 [Bull et al. 2001]Bull, S., Greer, J., Mccalla, G., Lori, K., AND Bowes, J. 2001 User Modeling in I-Help: What, Why, When and How, In Proceedings of the 8th International Conference on User Modeling, Sonthofen, Germany, 117-126. [Canny 2002] Canny, J. 2002. Collaborative filtering with privacy via factor analysis. In proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (Tampere, Finland, August, 2002) 238-245. [Chow 2009] Chow, C. Y., Mokbel, M. F., AND Aref, G. W. 2009. Casper*: Query processing for location services without compromising privacy. ACM Transactions on Database System. 34, 4 (Dec. 2009), 24:1-24:48. [Chung and Lee 2001]Chung, Y. M., AND Lee, J. Y. 2001. A corpus-based approach to comparative evaluation of statistical term association measures. Journal of the American Society for Information Science and Technology, 52(4), 283-296. [Dmitriev et al. 2010]Dmitriev, P., Serdyukov, P., AND Chernov, S. 2010. Enterprise and desktop search. In Proceedings of the 19th International World Wide Web Conference (WWW 2010), 1345-1346 [Du 2001] Du, W., AND Atallah, M. 2001. Secure Multi-Party Computation Problems and Their Applications: A Review and Open Problems, In New Security Paradigms Workshop September 11th - 13th, 2001, Cloudcroft, New Mexico, USA, 11-20. [Fan 2010] Fan, J., Wu, H., Li, G. AND Zhou, L. 2010 Suggesting Topic-Based Query Terms as You Type. In Proceedings of the 12th international Asia Pacific Web Conference (APWEB 2010) 61-67. [Finkelstein et al. 2002]Finkelstein, L., Gabrilovich, E., Matias, Y., Rivlin, E. Solan, Z., Wolfman, G., AND Ruppin, E. 2002. Placing Search in Context: The Concept Revisited, ACM Transactions on Information Systems, 20(1), 116-131. [Gabrilovich 2005]Gabrilovich, E., AND Markovitch, S. 2005. Feature Generation for Text Categorization Using World Knowledge. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI 2005), 1048-1053. [Gabrilovich 2006]Gabrilovich, E., AND Markovitch, S. 2006. Overcoming the Brittleness bottleneck using Wikipedia: enhancing text categorization with encyclopedic knowledge, In Proceedings of the 21th of the American Association for Artificial Intelligence (AAAI 2006), 1301-1306. [Gedik 2005] Gedik, B., AND Liu, L. 2005. Location Privacy in Mobile Systems: A Personalized Anonymization Model. In Proceedings of 25th International Conference on Distributed Computing Systems (IEEE ICDCS 2005), 620-629. [Gentry 2009] Gentry, C. 2009. Fully Homomorphic Encryption Using Ideal Lattices. ACM 41, 169-178. [Ghinita 2008] Ghinita, G., Kalnis, P, Khoshgozaran, A. Shahabi, C. AND Tan, K-L., 2008. Private queries in location based services: anonymizers are not necessary, In Proceedings of ACM SIGMOD Conference June 2008 Conference, 121-132. [Gracia 2008] Gracia, J., AND Mena, E. 2008. Web-Based Measure of Semantic Relatedness. In Proceedings of the 9th International Conference on Web Information Systems Engineering (WISE 2008), 136-150. [Henzinger 2001] Henzinger, M. 2001. Hyperlink Analysis for the Web. IEEE Internet Computing, 5(1), 45-50, January 2001. [Harvey 2010] Harvey, M., Baillie, M., Ruthven, I., AND Carman, M. 2010. Tripartite Hidden Topic Models for Personalized Tag Suggestion. In Advances in Information Retrieval: 32nd European Conference on IR Research 432-443. [Hoffstein 1998] Hoffstein, J., Pipher, J., AND Silverman, J. H. 1998. NTRU, A Ring-Based Public Key Cryptosystem (ANTS III), LNCS 1423, Springer-Verlag, Berlin, 267-288. [Jiang 2010] Jiang, J. R. 2010. Homomorphism and Cryptanalysis of NTRU. Master Thesis, Departament of Mathematics, National Taiwan University. [Jin 2001] Jin, E., Girvan, M., AND Newman, M. 2001. The Structure of Growing Social Networks. Physical Review E, 64(4), 046132. [Khoshgozaran 2007]Khoshgozaran, A. AND Shahabi, C. Blind evaluation of nearest neighbor queries using space transformation to preserve location privacy. In SSTD'07, 2007. [Kim and Choi 2001]Kim, M., AND Choi, K. A. 1999. Comparison of collocation-based similarity measures in query expansion. Information Processing and Management 35 (1999), 19-30. [Kleinberg 1998] Kleinberg, J., 1998. Authoritative Sources in a Hyperlinked Environment. In Proceedings of the 9th ACM SIAM Symposium on Discrete Algorithms, 668-677. [Køien 2006] Køien, G.M., AND Oleshchuk, V.A. 2006. Location Privacy for Cellular Systems; Analysis and Solutions. In PET, G.Danezis, D. Martin, Ed. LNCS, Springer, Heidelberg, 3856, 40-58. [Lappas 2009] Lappas, T., Liu, K., AND Terzi, E. 2009. Finding a Team of Experts in Social Networks. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2009), 467-476. [Lathia 2007] Lathia, N., Hailes, S., AND Capra, L. 2007. Private distributed collaborative filtering using estimated concordance measures. In Proceedings of the 2007 ACM conference on Recommender systems (ACM RecSys 2007),1-8. [Luo 2001] Luo, B. AND Hancock, E. R. 2001. Structural graph matching using the em algorithm and singular value decomposition. IEEE Transactions of. Pattern Analysis 23(10) 1120 - 1136. [Ma 2008] Ma, H., Yang, H., King, I., AND Lyu, M. 2008. Learning latent Semantic relations from click through data for query suggestion. In Proceedings of the 17th ACM Conference on Information and Knowledge Management (CIKM 2008), 709-718. [Miller 2004] Miller, B. N., Konstan, J. A., AND Riedl, J. 2004. Pocketlens: Toward a personal recommender system. ACM Transactions of Information System. 22, 3 (2004) [Milne 2007] Milne, D., Witten, I. H., AND Nichols, D. 2007. A Knowledge-Based Search Engine Powered by Wikipedia. In Proceedings of the 16th ACM Conference on Information and Knowledge Management (CIKM 2007), 445-454. [Milne 2008] Milne, D., AND Witten, I. H. 2008. An effective, low-cost measure of semantic relatedness obtained from Wikipedia links. In Proceedings of the first AAAI Workshop on Wikipedia and Artifical Intellegence (WIKIAI'08), Chicago, I.L. [Page 1999] Page, L., Brin, S., Motwani, R., AND Winograd, T. 1999. The PageRank Citation Ranking: Bringing Order to the Web. Technical Report, SIDL-WP-1999-0120, Stanford University. [Polat 2006] Polat, H., AND Du, W. 2006. Privacy-Preserving Collaborative Filtering on Vertically Partitioned Data. PAKDD 2005: 651-658.. [Regev 2006] Regev, O. 2006. Lattice-based cryptography. In Advances in cryptology (CRYPTO), 131-141, (2006) [Revest1978] Rivest, R. L., Shamir, A., AND Adleman, L. 1978. A method for obtaining digital signatures and public-key cryptosystems, Commun. ACM 21, 120-126. [Salton and Buckley 1998]Salton, G., AND Buckley, C. 1988. Term-weighting approaches in automatic text retrieval. Information processing & management. Elsevier Information Processing Management 24(5) 513-523. [Sarwar 2001] Sarwar B., Karypis, G., Konstan, J., AND Riedl, J. 2001. Item-based Collaborative Filtering Recommendation Algorithms. In Proceedings of the 10th International World Wide Web Conference (WWW 2001), 285-295. [Schonhofen 2006]Schonhofen, P. 2006. Identify Document Topics Using the Wikipedia Category Network. In Proceedings of the 2006 International Conference on Web Intelligent Conference (WI 2006), 456-462. [Shieh 2009] Shieh, J. R., Hsieh, Y. H., Yeh, Y.T., Su, T. C., Lin, C. Y., AND Wu, J. L. 2009. Building Term Suggestion Relational Graphs from Collective Intelligence. In Proceedings of the 18th International World Wide Web Conference (WWW 2009), 713-721. [Shieh 2011] Shieh, J. R., Lin, C. Y., AND Wu, J. L. 2011 Recommendation in End-to-End Encrypted Domain. In Proceedings of the the 20th ACM Conference on Information and Knowledge Management (CIKM 2011), Glasgow, Scotland, UK, 915-924. [Silverstein 1998]Silverstein, C., Henzinger M., Marais, H., AND Moricz, M. 1998. Analysis of a very large AltaVista query log. Technical Report 1998014. [Staab 2001] Staab, S. 2001. Human language technologies for knowledge management. IEEE Intelligent Systems. 16(6) 84-94 [Symond 2011] Symonds, M., Bruza, D. P. Sitbon, L., AND Turber. L. 2011. Tensor Query Expansion: A cognitively motivated relevance model In Proceeding of the 16th Australasian Document Computing Symposium (ADCS 2011) Canberra, Australia. [TREC-5 1996] TREC-5, The Fifth Text REtrieval Conference (TREC-5) NIST. [van Dijk 2010] van Dijk, M., Gentry, C., Halevi, S., AND Vaikuntanathan, V. 2010. Fully homomorphic encryption over integer. In proceedings of 29th Annual International Conference on the Theory and Applications of Cryptographic Techniques [Vectomova 2006]Vectomova, O., AND Wang, Y. 2006. A study of the effect of term proximity on query expansion. Journal of Information Science 32(4), 324-333. [Voelker 2006] Voelcker, J. 2006. Stalked by Satellite: An Alarming Rise in GPS- Stalked by Satellite Harassment. IEEE Spectrum 47(7), 15-16. [Wang 2008] Wang, P., AND Domeniconi, C. 2008. Building Semantic Kernels for Text Classification using Wikipedia In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2008), 713-721. [Wang 2008] Wang, P., Domeniconi, C., AND Hu, J. 2008. Using Wikipedia for Co-clustering Based Cross-Domain Text Classification, In Proceedings of the 2008 International Conference on Data Mining (ICDM 2008), 1085-1090. [Wasserman 1995]Wasserman, S., AND Faust, K. 1995. Social Network Analysis: Theory and Methods, Cambridge, Cambridge University Press. [Watts 1999] Small Worlds: The Dynamics of Networks between Order and Randomness. Princeton University Press. [Wei 2009] Wei, X., AND Croft, W. B. 2006. LDA-based document models for ad-hoc retrieval. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR 2006), 178-185. [White2003] White, S., AND Smyth, P. 2003. Algorithms for Estimating Relative Importance in Networks. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2003), 266-275 [WordNet 1998] An Electronic Lexical Database. 1998 The MIT Press. [Xiang 2010] Xiang, W., Cao, B., Hu, H., AND Yang, Q. 2010. Bridging Domains Using World Wide Knowledge for Transfer Learning. IEEE Transactions on Knowledge and Data Engineering. 22(6), 770-783. [Xu 1996] Xu, J. 1996. Query Expansion Using Local AND Global Document Analysis, In Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR 1996), 4-11. [Yeh 2009] Yeh, E., Ramage D., Manning, C. D., Agirre, E., AND Soroa, A. 2009. WikiWalk: random walks on Wikipedia for semantic relatedness. In Proceedings of the 2009 Workshop on Graph-based Methods for Natural Language Processing (TextGraphs-4). Association for Computational Linguistics, Stroudsburg, PA, USA, 41-49. [Yu 2005] Yu, K., Yu, S., AND Tresp, V. 2005. Soft Clustering on Graphs. Advances in Neural Information Processing Systems (NIPS 2005), MIT Press. [Zhang 2002] Zhang, Y., Callan, J., AND Minka, T. 2002. Novelty and Redundancy Detection in Adaptive Filtering, In Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR 2002) 81-88 [Zhao 2010] Zhao, S., Wang, H., AND Liu, T. 2010. Paraphrasing with Search Engine Query Logs. The 23rd International Conference on Computational Linguistics (COLING 2010): 1317-1325. [Zhong 2007] Zhong, G., Goldberg, I., AND Hengartner. U. G., 2007. Louis, lester and pierre: Three protocols for location privacy. In Privacy Enhancing Technologies, 62-76. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/65512 | - |
dc.description.abstract | 「社群媒體 (Social media)」憑藉著自由開放、共同創新與互動分享的特性在當前網際網路活動中蔚為主流,人與人之間藉由作業平台,在網際網路中透過「資訊交流」與「協同合作」創造出-維基百科(Wikipedia)、部落格(Blog)、臉書(Facebook)以及Flickr等新興網路應用科技。有別於傳統網路媒體,社群媒體係憑藉著參與者的互動:如提供資訊、建立索引或對特定事物進行評分,以創造出應用平台的價值。在參與者的互動下,影響力一旦越過臨界點,就會出現病毒感染般的普遍流傳,社群媒體的發展突顯了「資訊技術」-Information Technology (IT)一詞中:「資訊」Information的重要,因為若僅著重技術-Technology,而忽略了資訊-Information,「資訊技術」Information Technology (IT) 就僅空有軀殼,不能創造資訊產業存在的價值。
分析發現,社群媒體的核心價值係由三大元素構成,其一是集思智慧 (Collective Intelligence),其二是雲端運算(Cloud Computing) ,其三是人機介面 (User Interface),這些特有的元素給我們帶來了機會,但同時亦帶來了新的挑戰。首先談到機會,由於社群 (Social Community)中人與人間的互動,推薦與分享,創造出了特有的集思智慧 (Collective Intelligence),如果我們能夠對這種集思智慧進行有效分析運用,我們就有能力運用一個寶貴的資產,開發出不同於與傳統模式的資料探勘技術,因此吾人先對社群媒體的特性進行了深入的分析,從而掌握了社群媒體中所蘊含的人與人、人與物、物與物等等的多面向資訊。再運用社群媒體所擁有的多方網路 (Multipartite Network) 關係,將社群媒體內的集思智慧做出了最有效的探勘與應用。在多媒體檢索上我們開發了「Building Multi-Modal Relational Graphs for Multimedia Retrieval」 的應用系統,而在推薦系統亦提出了 「Relational Term-Suggestion Graphs Incorporating Multi-Partite Concept and Expertise Networks」的創新方法,對檢索與推薦系統的發展與人機介面的運用均產生具體的貢獻。 接著敘述我們的挑戰,在研究中我們發現,由於社群媒體中參與者間公開互動的特性:參與者在提供資訊、建立索引或進行評分之餘,往往會不經意的洩漏了個人的背景與好惡:最重要的是越來越多的機敏社群資料被置放於具有安全疑慮的雲端伺服器(Cloud Server)上,因此如何在保護個人隱私與集思智慧的前提下,遂行資訊檢索與推薦,遂成為了另一個我們責無旁貸的研究挑戰。 面對此一挑戰,經過深入研析,我們以密碼學為學理基礎,率先提出「個人加密、自主保護」與「一密到底(End-to-end)」的加密域(Encrypted Domain)資料探勘做法,提出個人在加密自己的智慧結晶後,送入資料庫後,仍能在保持加密的形態下,進行精確而有效率的加密域資料探勘,從而提供高度隱私保障的資料檢索與推薦,除此之外基於完善維護個人隱私原則,我們更率先提出運用個人專屬加解、密密鑰的加密域同態運算演算法,使加密域資料探勘的發展更為實用與安全。 在本研究中,我們透過多方網路的分析與應用,得以對社群媒體的寶藏-集思智慧做出最有效的探勘,配合完備的人機介面提供了更現代化的多媒體檢索與推薦。在另一方面,面對於社群媒體隱私權的挑戰,我們亦以創新的高效率加密域資料探勘思維,再以先進的不同加解密鑰對的同態加密運算,使社群網路中每一位智慧貢獻者的隱私都能獲得最佳的保障,企盼每一個人都能樂於悠遊於網路社群媒體之間,安全無慮的分享與貢獻自己所擁有的寶貴智慧與經驗。 | zh_TW |
dc.description.abstract | Social media currently dominates the activity of the World-Wide Web due to its open nature, collaborative structure, and support for interactive sharing. It has made possible information sharing and collaboration with such social platforms such as Wikipedia, blogs, Facebook, and Flickr. Social media’s primary strength is clearly in its user interaction, which is leveraged to create value for any platform that uses social media as it provides information, performs indexing, and provides scores for various items. Thus the reputation of a product or content can spread from a few people to a huge crowd, and can even “go viral” and end up on international headlines. The advance of social media has strongly underlined the importance of “Information” in Information Technology (IT): for IT without “Information” is nothing but an empty shell and can create no value.
According to our analysis, there are three major core values in social media: collective intelligence, cloud computing, and user interface. The rapid development of social media has brought not only new opportunities but also new challenges. Opportunities abound in social media because of the interaction, recommendations, and sharing among people in social communities. This has led to the emergence of collective intelligence. Effective analysis will help identify relationships from multipartite linkages such as contributor-contributor, contributor-term, and term-term. This, along with contributor expertise, will lead to new, non-traditional data-mining approaches that in turn will lead to new systems equipped with excellent search recall, search precision, and meaningful semantic relatedness. We have developed a series of innovative mathematical models for different applications: for multimedia retrieval, we use multi-modal relational graphs; for recommendation systems, we create an approaches using relational term-suggestion graphs incorporating multi-partite concept and expertise networks. This use of collective intelligence yields important contributions for data retrieval, as well as the development of recommendation systems, in particular their user interface. For social media, because of its heavy focus on interpersonal relationships, privacy is now the biggest challenge. For instance, the privacy of medical records should be carefully protected. Most important of all, now that more and more of this kind of data has been stored on cloud servers, the challenge has become how to protect and secure everyone’s precious knowledge, experiences. Retrieval can be done effectively only if such protection is offered. To meet these challenges, we propose an end-to-end encrypted-domain data mining scheme based on the belief that every individual has the right to protect his or her own privacy. We accomplish this using ring homomorphic encryption, which allows for data mining to be conducted even in the encrypted domain. We are the first team to propose that each user use their own encryption and decryption keys to allow the un-trusted server to conduct mathematical operations on their data in the encrypted domain, thus making encrypted-domain data mining not only more practical but also more secure In this research, we use multi-partite analysis to effectively explore the collective intelligence embedded in social media and then leverage it for innovative approaches for recommendation systems and data retrieval systems. The comprehensive user interface demonstrated using this approach modernizes not only data retrieval for text and multimedia but also personalized recommendations. In addition, we propose a novel, highly-efficient encrypted-domain data mining scheme based on ring homomorphic encryption with user-specific encryption and decryption keys that protects the intellectual property of each user of the social media. It is our sincere hope that this will allow for the sharing of knowledge over the Internet with no nagging worries about privacy. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T23:47:34Z (GMT). No. of bitstreams: 1 ntu-101-D93944002-1.pdf: 2167269 bytes, checksum: c72bcac13528a35dadcc3a56b719061c (MD5) Previous issue date: 2012 | en |
dc.description.tableofcontents | 口試委員會審定書 i
致謝 ii 摘要 v Abstract vii List of Figures xiii List of Tables xv Chapter 1 Introduction 1 1.1 MOTIVATION 1 1.2 SUMMARY OF CONTRIBUTIONS 3 1.2.1 Multipartite Networks for Social Media Data Mining 3 1.2.2 Multi-modal Relational Graphs for Multimedia Retrieval 4 1.2.3 Encrypted-Domain Data Mining 5 1.2.4 Secret Sharing for Encrypted-Domain Recommendations 5 1.3 DISSERTATION ORGANIZATION 6 Chapter 2 Multipartite Networks for Social Media Data Mining 7 2.1 INTRODUCTION 7 2.2 RELATED WORKS 10 2.2.1 Co-occurrence Based Term-Term Associations 10 2.2.2 Using Wikipedia as a Knowledge Repository 11 2.2.3 Expertise-mining 12 2.2.4 Term Suggestion and Paraphrase for Search Assistant 14 2.3 A FRAMEWORK FOR PROVIDING SEMANTIC TERM GRAPH SUGGESTION 15 2.4 SYSTEM CONSTRUCTION 19 2.4.1 Effective Data Sampling 19 2.4.2 Soft Clustering 21 2.4.3 Incorporating Contributor Expertise 25 2.5 RELATED-TERM GRAPH APPLICATIONS 33 2.6 EVALUATIONS 35 2.6.1 Evaluation Based on Practical Data 35 2.6.2 Retrieval Evaluation Metrics 36 2.6.3 Experimental Results 37 2.6.4 Comparison with Other Wikipedia-based Term Suggestion System 39 2.6.5 Comparison with Paraphrase Extraction Approaches 41 2.6.6 Comparison with Other Graph-based Approaches 42 2.6.7 Advantages of Using Content, Link, and Category Information 44 2.6.8 Comparison with Different Thesaurus 45 2.7 CONCLUSIONS AND FUTURE RESEARCH 47 Chapter 3 Encrypted-Domain Data Mining for Recommendation 49 3.1 INTRODUCTION 49 3.2 RELATED WORKS 52 3.2.1 Randomized Perturbation 52 3.2.2 Homomorphic Encryption 52 3.2.3 User Profile Distribution 53 3.2.4 Collaborative Recommendation 54 3.3 PRIVACY-PRESERVING RECOMMENDATION SYSTEM 56 3.3.1 Encrypted-Domain Recommendation System 56 3.3.2 System Structure 57 3.3.3 Ring Homomorphic Encryption 61 3.3.4 Lattice Based Polynomial Ring Homomorphic Encryptions 64 3.3.5 Order-Preserving Encrypted-Domain Ranking 71 3.3.6 Secure Channel between Parties 73 3.4 EXPERIMENTAL RESULTS 75 3.4.1 Experiment setting 75 3.4.2 Data sets 75 3.4.3 Efficiency Comparison 75 3.4.4 Accuracy Comparison 76 3.4.5 Security Comparison 78 3.4.6 Computation Cost and Complexity 80 3.5 CONCLUSIONS 83 Chapter 4 Secret Sharing for Encrypted-Domain Recommendations 84 4.1 INTRODUCTION 84 4.1.1 Secret Sharing for Homomorphic Encryption 84 4.1.2 Location Privacy Recommendation 86 4.1.3 Contributions 90 4.2 RELATED WORKS 91 4.2.1 Location Cloaking 91 4.2.2 Preservation by Secure Protocol 92 4.2.3 Combined Keys for Equity 93 4.3 SYSTEM CONSTRUCTION 94 4.3.1 Notations 94 4.3.2 A Frame Work for Providing Location Privacy 94 4.4 ENCRYPTION DOMAIN DISTANCE MEASURE 99 4.4.1 Homomorphic Encryption 99 4.4.2 Encryption Domain Operation 103 4.5 ILLUSTRATIVE EXAMPLE 111 4.5.1 Secret Sharing Key Creation 112 4.5.2 Individual Encryption Key Data Encryption 112 4.6 EXPERIMENT 117 4.6.1 Experiment Setting 117 4.6.2 Comparison with the Location Cloaking Recommendation 117 4.6.3 Comparison with the Security Protocol based Recommendation 118 4.7 SUMMARY 120 Chapter 5 Conclusions and Future Works 121 Bibliography 123 Appendices 131 | |
dc.language.iso | en | |
dc.title | 多方網路與加密域資料探勘技術在搜尋與推薦之應用 | zh_TW |
dc.title | Multipartite Networks and Encrypted-Domain Data Mining for Search and Recommendation | en |
dc.type | Thesis | |
dc.date.schoolyear | 100-2 | |
dc.description.degree | 博士 | |
dc.contributor.oralexamcommittee | 貝蘇章(Soo-Chang Pei),林清詠(Ching-Yung Lin),歐陽明(Ming Ouhyoung),謝續平(Shiuhpyng Shieh),陳文進(Wen-Chin Chen) | |
dc.subject.keyword | 社群媒體,社群網路,多媒體檢索,多媒體安全,隱私權保護, | zh_TW |
dc.subject.keyword | social media,social network,multimedia retrieval,multimedia security,privacy, | en |
dc.relation.page | 130 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2012-07-24 | |
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 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-101-1.pdf 目前未授權公開取用 | 2.12 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。