臺灣博碩士論文加值系統

資料比對可以判斷那一對資料，記錄著在真實世界中的同一個物體，它是在資料探勘領域中，一個重要並且基本的資料整合運作。近幾年由於對網路安全的意識抬頭，保護隱私已經引起廣大的注意。因此具保護隱私之資料比對協定的目的，就是去判定在兩個獨立資料庫中的共同資料紀錄，又同時避免洩露任何私密的資料，雖然保護隱私與資訊分享是兩個彼此衝突的概念。在本論文中，一組經改進用來設定資料轉換空間的參考集合被提出來，並由實驗顯示當為了保護資料私密而將明文資料值轉換到一度量空間時，它是一個更有效率且更精確的方法。

Record matching or data linkage identifies all pairs of matched records that refer to the same entity in the real world; it is an important issue and a basic operation of information integration in the data mining field. In recent years, preservation of privacy has gained a lot of attention because of an increasing awareness of the importance of security. Therefore, the aim of the privacy preserving record matching protocol is to recognize the common records shared between two autonomous data sources and keep privacy leakage low without revealing any private data at the same time, although the main idea of privacy-preserving and information sharing conflicts in nature. In this thesis, one modified reference set to set the embedding space is proposed and demonstrated by experiment that it is more efficient and precise when transforming plain record values into a metric space in order to keep confidential.

誌謝 i
中文摘要 ii
Abstract iii
Table of Contents iv
List of Figures v
List of Tables vi
Chapter 1 Introduction 1
Chapter 2 Related Works 4
2.1 Record Matching 4
2.2 Private Information Sharing 7
2.3 Blocking 10
Chapter 3 Problem Statement and Overall Protocol 11
3.1 Problem Statement 11
3.2 Overall Protocol 11
3.3 Phases of Protocol 12
3.4 Pseudo code of Protocol 14
Chapter 4 Transformation of Records and Record Matching 16
4.1 Setting up the Embedding Space 16
4.2 Embedding RA and RB 18
4.3 Record Matching 21
Chapter 5 Experiments 23
Chapter 6 Conclusions and Future Work 31
References 32

[1]R. Agrawal, A. Evfimievski, and R. Srikant, Information sharing across private databases, In Proceedings of 2003 ACM SIGMOD International Conference on Management of Data, pages 86-97, ACM Press, 2003.
[2]R. Agrawal and E. Terzi, On honesty in sovereign information sharing, In Proceedings of the 10th International Conference on Extending Database Technology (EDBT), pages 240-256, 2006.
[3]M. J. Atallah, F. Kerschbaum, and W. Du, Secure and private sequence comparisons, In Proceedings of the 2003 ACM Workshop on Privacy in the Electronic Society, pages 39-44, 2003.
[4]E. Bertino and R. Sandhu, Database security: Concepts, approaches, and challenges, IEEE Transactions on Dependable and Secure Computing, Vol. 2, pages 2-19, 2005.
[5]M. Bilenko, B. Kamath, and R. J. Mooney, Adaptive blocking: Learning to scale up record linkage, In IEEE International Conference on Data Mining (ICDM’06), pages 87–96, Hong Kong, 2006.
[6]C. Clifton, M. Kantarcioglu, A. Doan, G. Schadow, J. Vaidya, A. Elmagarmid, and D. Suciu, Privacy-preserving data integration and sharing, In Proceedings of the 9th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pages 19-26, 2004.
[7]W. W. Cohen, P. Ravikumar, and S. E. Fienberg, A comparison of string distance metrics for name-matching tasks, In IIWEB, pages 73-78, 2003.
[8]W. W. Cohen, P. Ravikumar, and S. E. Fienberg, A comparison of string metrics for matching names and records, KDD Workshop on Data Cleaning and Object Consolidation, 2003.
[9]W. Du and M. J. Atallah, Protocols for secure remote database access with approximate matching, In 7th ACM Conference on Computer and Communications Security (ACMCCS 2000), the First Workshop on Security and Privacy in E-Commerce, Athens, Greece, November 2000.
[10]K. Elmagarmid, P. G. Ipeirotis, and V. S. Verykios, Duplicate record detection: A survey, IEEE Transactions on Knowledge and Data Engineering, Vol. 19, No. 1, pages 1-16, 2007.
[11]M. J. Freedman, K. Nissim, and B. Pinkas, Efficient private matching and set intersection, In Proceedings of Eurocrypt 2004, Vol. 3027 of LNCS, pages 1-19, Springer-Verlag, May 2004.
[12]K. Frikken, Privacy-preserving set union, Lecture Notes in Computer Science, Vol. 4521, pages 237-252, 2007.
[13]K. Goiser and P. Christen, Towards automated record linkage, In Proceedings of the fifth Australasian Conference on Data Mining and Analytics, Vol. 61, pages 23–31, 2006.
[14]R. Hjaltason, and H. Samet, Properties of embedding methods for similarity searching in metric spaces, IEEE Transactions on Pattern Analysis and Machine Intelligence 25, No. 5, pages 530-549, May 2003.
[15]L. Kissner and D. Song, Private and threshold set-intersection, Technical Report CMU-CS-05-113, Carnegie Mellon University, 2005.
[16]L. Kissner and D. Song, Privacy-preserving set operations, In Advances in Cryptology - Crypto 2005, LNCS Vol. 3621, pages 241-257, Springer-Verlag, 2005.
[17]N. Koudas, S. Sarawagi, and D. Srivastava, Record linkage: Similarity measures and algorithms, SIGMOD 2006, pages 802-803, 2006.
[18]Al-Lawati, D. Lee, and P. McDaniel, Blocking aware private record linkage, In Proceedings of the ACM SIGMOD Workshop on Information Quality in Information, pages 59-68, 2005.
[19]J. Liang, C. Li, and S. Mehrotra, Efficient record linkage in large data sets, 8th Annual International Conference on Database Systems for Advanced Applications, DASFAA 2003, pages 26-28, March 2003.
[20]M. O’Keefe, M. Yung, L. Gu, and R. Baxter, Privacy-preserving data linkage protocols, In WPES’04: Proceedings of the 2004 ACM Workshop on Privacy in the Electronic Society, pages 94–102, Washington DC, 2004.
[21]E. Rahm, and P.A. Bernstein, A survey of approaches to automatic schema matching, VLDB Journal 10 (2001), No. 4, pages 334-350, 2001.
[22]P. Ravikumar, W. Cohen, and S. Fienberg, A secure protocol for computing string distance metrics, In Workshop on Privacy and Security Aspects of Data Mining, Brighton, UK, November 1 2004.
[23]M. Scannapieco, I. Figotin, E. Bertino, and A. Elmagarmid, Privacy preserving schema and data matching, In Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, pages 653-664, June 11-14, 2007, Beijing, China.
[24]S. Verykios, E. Bertino, I. N. Fovino, L. P. Provenza, and Y. Saygin, State-of-the-art in privacy preserving data mining, SIGMOD Record, Vol. 33, No. 1, pages 50–57, March 2004.
[25]S. Yan, D. Lee, M. Y. Kan, and L. C. Giles, Adaptive sorted neighborhood methods for efficient record linkage, In Proceedings of the 7th ACM/IEEE Joint Conference on Digital Libraries, pages 185-194, 2007.
[26]N. Zhang and W. Zhao, Distributed privacy preserving information sharing, In VLDB ’05: Proceedings of the 31st International Conference on Very Large Data Bases, pages 889-900, VLDB Endowment, 2005.