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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 吳家麟 | |
| dc.contributor.author | Ping-Yen Hsieh | en |
| dc.contributor.author | 謝秉諺 | zh_TW |
| dc.date.accessioned | 2021-06-13T15:43:48Z | - |
| dc.date.available | 2008-07-09 | |
| dc.date.copyright | 2008-07-09 | |
| dc.date.issued | 2008 | |
| dc.date.submitted | 2008-07-03 | |
| dc.identifier.citation | [1] AK. Jain, MN Murty, PJ. Flynn, “Data Clustering: A Review,” in ACM Computing Surveys (CSUR), 1999
[2] RO. Duda, PE. Hart, “Pattern Classification and Scene Analysis,” Wiley Interscience, 1973 [3] S. Guha, A. Meyerson, N. Mishra, R. Motwani, L. O’Callaghan, “Clustering Data Streams: Theory and Practice,” in IEEE Transactions on Knowledge and Data Engineering, vol. 15, no. 3, May/June 2003 [4] G. Adomavicius, A. Tuzhilin, “Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions,” in IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 6, June 2005 [5] S. Jha, L. Kruger, P. McDaniel, “Privacy Preserving Clustering,” in 10th European Symposium on Research in Computer Security (ESORICS), 2005 [6] R. Agrawal, R. Srikant, “Privacy-Preserving Data Mining,” in ACM SIGMOD Record, vol. 29, issue 2, June 2000 [7] M. Kantarcioglu, C. Clifton, “Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data,” in IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 9, Sep. 2004 [8] J. Vaidya, C. Clifton, “Privacy-Preserving K-Means Clustering over Vertically Partitioned Data,” in SIGKDD Knowledge Discovery and Data Mining, 2003 [9] DK. Tasoulis, EC. Laskari, GK. Meletiou, MN. Vrahatis, “Privacy Preserving Unsupervised Clustering over Vertically Partitioned Data,” in Computational Science and Its Applications (ICCSA), 2006 [10] A. Inan, Y. Saygin, E. Savas, AA. Hintoglu, A. Levi, “Privacy Preserving Clustering on Horizontally Partitioned Data,” in Proceedings of the 22nd International Conference on Data Engineering Workshops, 2006 [11] G. Jagannathan, K. Pillaipakkamnatt, RN. Wright, “A New Privacy-Preserving Distributed k-Clustering Algorithm,” in Proceedings of the 2006 SIAM International Conference on Data Mining, 2006 [12] G. Jagannathan, RN. Wright, “Privacy-Preserving Distributed K-Means Clustering over Arbitrarily Partitioned Data,” in SIGKDD Knowledge Discovery and Data Mining, 2005 [13] BCM. Fung, K. Wang, PS. Yu, “Anonymizing Classification Data for Privacy Preservation,” in IEEE Transactions on Knowledge and Data Engineering, vol. 19, no. 5, May 2007 [14] SRM. Oliveira, OR. Zaiane, “Privacy-Preserving Clustering by Object Similarity-Based Representation and Dimensionality Reduction Transformation,” in Proceedings of the Workshop on Privacy and Security Aspects of Data Mining in conjunction with the Fourth IEEE International Conference on Data Mining, 2004 [15] S. Merugu, J. Ghosh, “Privacy-Preserving Distributed Clustering using Generative Models,” in Proceedings of the Third IEEE International Conference on Data Mining, 2003 [16] R. Rivest, A. Shamir, L. Adleman, “A Method for Obtaining Digital Signatures and Public-Key Cryptosystems,” in Communications of the ACM, vol. 21, 1978 [17] P. Paillier, “Public Key Cryptosystems Based on Composite Degree Residuosity Classes,” in Eurocrypt, 1999 [18] J. Benaloh, “Dense Probabilistic Encryption,” in Proceedings of the Workshop on Selected Areas of Cryptography, 1994 [19] D. Naccache, J. Stern, “A New Public Key Cryptosystem Based on Higher Residues,” in Proceedings of the 5th ACM Conference on Computer and Communications Security, 1998 [20] T. Okamoto, S. Uchiyama, “A New Public-Key Cryptosystem as Secure as Factoring,” in Advances in Cryptography, Eurocrypt, 1998 [21] G. Nagy, “State of the Art in Pattern Recognition,” in Proceedings of IEEE 56, 1968 [22] SC. Johnson, “Hierarchical Clustering Schemes,” Psychometrika, 2: 241-254, 1967 [23] W. Du, M. Atallah, “Privacy-Preserving Cooperative Statistical Analysis,” in 17th ACSAC, pp. 102-112, 2001 [24] B. Goethals, S. Laur, H. Lipmaa, T. Mielikainen, “On Private Scalar Product Computation for Privacy-Preserving Data Mining,” in Proceedings of the 7th International Conference on Information Security and Cryptology (ICISC), 2004 [25] AC. Yao, “Protocols for Secure Computations,” in Proceedings of the 23rd Annual IEEE Symposium on Foundations of Computer Science, 1982 [26] I. Ioannidis, A. Grama, “An Efficient Protocol for Yao's Millionaires' Problem,” in Proceedings of the 36th Hawaii International Conference on System Sciences, 2002 [27] LIBSVM Data: Classification, Regression, and Multi-label: http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/ | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/37787 | - |
| dc.description.abstract | Clustering is one of the most useful techniques to do some data analysis. But the conventional way to perform clustering usually offends one’s privacy. In the era of digital information, privacy is a very important concern in our daily life. To preserve one’s privacy, we exploit several privacy-preserving clustering schemes in this thesis. In the beginning, introduction to clustering, distributed clustering, and privacy-preserving clustering schemes will be given in order. And then, two major schemes, privacy-preserving k-means clustering and privacy-preserving hierarchical clustering, are illustrated in details. We implement these algorithms and perform experiments with both simulated ones and realistic data sets to learn the characteristics of each privacy-preserving clustering algorithm and evaluate the feasibility and usefulness of privacy-preserving clustering schemes. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T15:43:48Z (GMT). No. of bitstreams: 1 ntu-97-R95944004-1.pdf: 988723 bytes, checksum: 404e3b5da52c6dcb6ff3fe9a98f1cae1 (MD5) Previous issue date: 2008 | en |
| dc.description.tableofcontents | Chapter 1 Introduction 1
Chapter 2 Related Work 5 Chapter 3 Distributed Clustering 9 3.1 Distributed K-Means Clustering 10 3.1.1 K-Means Clustering 10 3.1.2 Distributed K-Means Clustering 12 3.2 Distributed Hierarchical Clustering 16 3.2.1 Hierarchical Clustering 16 3.2.2 Modified Hierarchical Clustering 18 3.2.3 Distributed Hierarchical Clustering 20 Chapter 4 Privacy-Preserving Clustering 23 4.1 Preliminaries 23 4.1.1 Paillier Cryptosystem 23 4.1.2 Secure Weighted Average Protocol 26 4.1.3 Permute Share 28 4.1.4 Secure Scalar Product 29 4.1.5 Yao’s Millionaires’ Protocol 31 4.2 Privacy-Preserving K-Means Clustering 33 4.3 Privacy-Preserving Hierarchical Clustering 35 4.3.1 Share Centers and Weights 36 4.3.2 Construct the Proximity Matrix 38 4.3.3 Find the Nearest Pair 41 4.3.4 Merge the Nearest Pair 43 Chapter 5 System Implementation 47 5.1 Interface 47 5.2 Implementation Detail 50 5.3 Experiment Results 51 5.3.1 Characteristics of Clustering Algorithms 51 5.3.2 Distributed Clustering Algorithm Comparisons 55 5.3.3 Privacy-Preserving Clustering Algorithm Comparisons 59 Chapter 6 Conclusions and Future Work 63 Reference 65 | |
| dc.language.iso | en | |
| dc.subject | 隱私保護群聚 | zh_TW |
| dc.subject | 分散式群聚 | zh_TW |
| dc.subject | 群聚 | zh_TW |
| dc.subject | Privacy-Preserving Clustering | en |
| dc.subject | Clustering | en |
| dc.subject | Distributed Clustering | en |
| dc.title | 以隱私保護為目的之群聚方式研究 | zh_TW |
| dc.title | A Study of Some Privacy-Preserving Clustering Schemes | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 96-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 莊玉如,林育慈,朱威達 | |
| dc.subject.keyword | 群聚,分散式群聚,隱私保護群聚, | zh_TW |
| dc.subject.keyword | Clustering,Distributed Clustering,Privacy-Preserving Clustering, | en |
| dc.relation.page | 68 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2008-07-03 | |
| 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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