臺灣博碩士論文加值系統

本論文主要探討數位浮水印技術於保護關聯式資料庫內容完整性之研究，並且應用支援向量機所具有之優異的機器學習能力，針對關聯式資料庫進行學習與預測分析，以達到資料庫內容完整性驗證之目的。
本論文第三章中提出一個結合碎型浮水印技術及支援向量迴歸學習能力的方法，其中支援向量迴歸是運用來學習資料庫屬性值間高度相關進而產生預測函數。在使用資料庫前得以偵測資料庫內容是否已遭到惡意竄改，以免繼續引用已不具完整性的資料庫，而產生不正確的分析結果。在不影響資料庫資料之可用性的前題下，隨機挑選數值型的屬性欄位，當浮水印位元為1時，預測值加1後修改對應的欄位，反之則預測值減1後修改對應的欄位，循序嵌入一序列的浮水印位元；在偵測時，利用已訓練好的支援向量迴歸函數產生之預測值與受測的屬性值作比較，判斷差值是否等於1，則可達到偵測資料庫內容是否遭到惡意竄改之目的。
在第四章中，在不破壞資料庫原有內容的前題下，提出一個碎型浮水印技術，利用支援向量迴歸學習的預測值，與原值作運算後，產生差值；再以霍夫曼編碼法將差值編碼後產生的差值霍夫曼碼，作為受保護的資料庫之少量重要特徵，利用此特徵資料來達到資料庫內容驗證的目的。
最後，第五章提出一個植基於有預測能力的支援向量迴歸的碎型浮水印技術，該方法不僅能驗證資料庫內容並可還原原值。針對特定數值型的屬性，利用支援向量迴歸預測函數產生的預測值與原值作差值運算，將浮水印嵌入差值，再將嵌入浮水印的差值加上原值，則完成浮水印嵌入流程。當受保護的資料庫，遭到惡意竄改時，利用經過訓練的支援向量迴歸預測函數產生預測值，再與屬性值作差值運算，從差值中取出浮水印來偵測資料是否被竄改；再者，本方法可針對因嵌入浮水印而被修改的屬性值還原成原來的值。所以，提出的方法可以偵測並定位惡意竄改，以達到驗證功能；進而可恢復資料庫內容的完整性。

In this thesis, a study on digital watermarking technology by applying support vector machine (SVM) for relational database integrity authentication is proposed. Owing to the elegant machine learning ability of SVM, SVM is used to learn and predict the correlation for relational database, and then to perform the purpose of the database content integrity.
In Chapter 3, an effective solution based on the fragile watermarking technique is proposed by exploiting the trained SVR predicting function to distribute the digital watermark over the particular numeric attributes. While the watermark bit is equal to 1, add 1 to the predicted value and replace the original attribute value with the new predicted value. Otherwise, while the watermark bit is equal to 0, corresponding original attribute value is replaced by the value which is subtracted 1 from the predicted value. In detection phase, the same SVR predicting function is used to generate predicted value, and if the absolute difference value between predicted value and attribute value is more than the designed fixed value, like one, then the database content is determined to be tampered with.
In Chapter 4, the proposed watermarking scheme based on SVR prediction, which exploits the digital watermarking technology for guaranteeing the database integrity underlying distortion free of database content. The proposed scheme employs SVR predictive function to obtain characteristic of the database and uses Huffman coding to encode the characteristic for compressing important payload information. In detection procedure, minor and necessary additional payload information of the database is used to accomplish tampering detection.
Eventually, Chapter 5 proposed a reversible fragile watermarking based on SVR prediction for authenticating database integrity with original values recovery. While the protected database is modified by malicious users, the trained SVR predicting function is used to generate difference and extract embedded watermark bits to detect modified tuples. Furthermore, the proposed scheme is capable of recovering any original value after a tamper-free recovery procedure where the embedded watermark bits are properly extracted. In other words, the proposed method really has the power of effective detection and locating malicious tampering, achieving database authentication and recovering content integrity.

Abstract in Chinese I
Abstract in English II
Acknowledgement IV
Contents V
List of Tables VIII
List of Figures VIIII
Chapter 1 Introduction 1
1.1 Background and Motivation 1
1.2 Digital Watermarking 2
1.3 Database Watermarking 4
1.4 Thesis Organization 6
Chapter 2 Preliminaries 7
2.1 Support Vector Machine 7
2.2 Fragile Watermarking 11
2.3 Related Work 12
Chapter 3 Fragile Database Watermarking for Malicious Tamper Detection Using Support Vector Regression 16
3.1 The Proposed Scheme 16
3.1.1 Training Phase 18
3.1.2 Embedding Phase 19
3.1.3 Tamper Detection Phase 20
3.2 Experimental Results 20
3.3 Summary 24
Chapter 4 Tamper Detection of Relational Database Based on SVR Prediction and Differential Huffman Code 25
4.1 The Proposed Scheme 25
4.1.1 Training Phase 27
4.1.2 Tamper Detection Phase 28
4.2 Experimental Results 29
4.3 Summary 33
Chapter 5 Reversible Fragile Database Watermarking Based on SVR Prediction 35
5.1 The Proposed Scheme 35
5.1.1 Training and Embedding Phase of SVR Function 37
5.1.2 Tamper Detection and Recovery Phase 39
5.2 Experimental Results 40
5.3 Summary 43
Chapter 6 Conclusions and Future Works 44
6.1 Conclusions 44
6.2 Future Works 46
Bibliography 47

[1]R. Agrawal and J. Kiernan, “Watermarking Rela¬tional Databases,” in Proceedings of the 28th Interna¬tional Conference on Very Large Data¬bases, Hong Kong, China, 2002, pp. 155-166.
[2]R. Agrawal, P. J. Haas, and J. Kiernan, “Watermarking Relational Data: Framework, Algorithms and Analysis,” The VLDB Journal, Vol. 12, No. 2, 2003, pp. 157-169.
[3]R. Agrawal, J. Kiernan, R. Srikant, and Y. Xu, “Order-Preserving Encryption for Numeric Data,” in Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data, Paris, France, 2004, pp. 563-574.
[4]A. Asuncion and D.J. Newman, UCI Machine Learning Repository, Irvine, CA: University of California, School of Information and Computer Science, 2007, available at http://www.ics.uci.edu/~mlearn/MLRepository.html.
[5]E. Bertino, S. Jajodia, and P. Samarati, “Database Security: Research and Practice,” Information Systems, Vol. 20, No. 7, 1995, pp. 537-556.
[6]F. Bao, R. H. Deng, B. C. Ooi, and Y. Yang, “Tailored Reversible Watermarking Schemes for Authentication of Electronic Clinical Atlas,” IEEE Transac¬tions on Information Technology in Biomedicine, Vol. 9, No. 4, 2005, pp. 554-564.
[7]C. C. Chang and C. J. Lin, LIBSVM: a library for support vector machines, 2001, software avail¬able at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
[8]E. C. Chang, M.S. Kankanhalli, X. Guan, Z. Y. Huang, and Y. H. Wu, “Robust Image Authentication Using Content Based Compression,” Multimedia System, Vol. 9, No. 2, pp. 121-130.
[9]I. J. Cox, M. L. Miller, and J. A. Bloom, Digital Watermarking: Principles and Practice, Morgan Kaufmann, 2001.
[10]M. Chen, Y. He, and R. L. Lagendijk, “A Fragile Watermark Error Detection Scheme for Wireless Video Communications,” IEEE Transac¬tions on Multimedia, Vol. 7, No. 2, 2005, pp. 201-211.
[11]G. I. Divada and D. L. Wells, “A Database En¬cryption System with Subkeys,” ACM Transac¬tions on Database Systems, Vol. 6, No. 2, 1981, pp. 312-328.
[12]Harris Drucker, Chris J.C. Burges, Linda Kaufman, Alex Smola, and Vladimir Vapnik, “Support Vector Regression Machines,” in Proceedings of the Neural Information Processing Systems, 1996, pp. 155-161.
[13]R. A. Fisher, “The Use of Multiple Measure¬ments in Taxonomic Problems,” Annual Eugen¬ics, Vol. 7, Part 2, 1936, pp. 179-188.
[14]J. Fridrich, J Goljan, and R. Du, “Invertible Authentication,” in Proceedings of SPIE, Security and Watermarking of Multimedia Contents III, vol. 4314, P. W. Wong and E. J. Delp, Eds., 2001, pp. 197–208.
[15]J. Fridrich and M. Du, “Images with Self-correcting Capabilities,” in Proceedings of the IEEE International Conference on Image Processing, 1999, pp. 792-796.
[16]J. Fridrich, M. Goljan, and A.C. Baldoza, “New Fragile Authentication Watermark for Images,” in Proceedings of IEEE International Conference on Image Processing, Vol. 1, Vancouver, Canada, 2000, pp. 446-449.
[17]J. Fridrich, “Security of Fragile Authentication Watermarks with Localization,” in Proceedings of SPIE Conference on Security and Watermarking of Multimedia Contents, Vol. 4675, San Jose, California, 2002, pp. 691-700.
[18]H. Guo, Y. Li, A. Liu, and S. Jajodia, “A Fragile Watermarking Scheme for Detecting Malicious Modifications of Database Relations,” Informa¬tion Sciences, Vol. 176, No, 10, 2006, pp. 1350-1378.
[19]F. Hartung and M. Kutter, “Multimedia Watermarking Techniques,” in Proceedings of the IEEE : special issue on Protection of Multimedia Content, Vol. 87, No. 7, 1999, pp. 1079-1107.
[20]M. S. Hwang and W. P. Yang, “Multilevel Se¬cure Database Encryption with Subkeys,” Data & Knowledge Engineering, Vol. 22, 1997, pp. 117-131.
[21]N. F. Johnson, Z. Duric, and S. Jajodia, Information Hiding: Steganography and Watermarking- Attacks and Countermeasures, Kluwer Publishers, 2000.
[22]S. Katzenbeisser and F. A. Petitcolas (editors). Information Hiding Techniques for Steganography and Digital Watermarking, Artech House, 2000.
[23]C. Y. Lin and S.F. Chang, “A Robust Image Authentication Method Distinguishing JPEG Compression from Malicious Manipulation,” IEEE Transactions on Circuits System and Video Technology, Vol. 11, No. 2, 2003, pp. 153-168.
[24]Y. Li, H. Guo, and S. Jajodia, “Tamper Detection and Localization for Categorical Data Using Frag¬ile Watermarks,” in Proceedings of the 4th ACM Workshop on Digital Rights Management, Washington DC, USA, 2004, pp. 73-82.
[25]C. Lu, H. Liao, and L. Chen, “Multipurpose Audio Watermarking,” in Proceedings of the 15th International Conference on Pattern Recognition, Beijing, China, 2000, pp. 677-680.
[26]G. Langelaar, I. Setyawan, and R. Lagendijk. “Watermarking Digital Image and Video Data: A State-of-the-Art Overview,” IEEE Signal Proceeding Magazine, Vol. 17, No. 5, 2000, pp. 20-46.
[27]Y. Li, V. Swarup, and S. Jajodia, “Fingerprinting Relational Databases: Schemes and Specialties,” IEEE Transactions on Dependable and Secure Computing, 2005, pp. 34-45.
[28]Z. Ni, Y. Q. Shi, N. Ansari, and W. Su, “Reversible Data Hiding,” IEEE Transac¬tions on Circuits and Systems for Video Technology, Vol. 16, No. 3, 2006, pp. 354-362.
[29]A. H. Paquet, R. K. Ward, and I. Pitas, “Wavelet Packets-based Digital Watermarking for Image Verification and Authentication,” Signal Processing, Vol. 88, No. 9, pp. 2193-2205.
[30]R. M. Shen, Y. G. Fu, and H. T. Lu, “A Novel Image Watermarking Scheme Based on Support Vector Regression,” Journal of Systems and Software, Vol. 78, No. 1, pp. 1-8.
[31]R. Sion, M. Atallah, and S. Prahakar, “Rights Protection for Relational Data,” IEEE Transac¬tions on Knowledge and Data Engineering, Vol. 16, No. 12, 2004, pp. 1509-1525.
[32]R. Sion, M. Atallah, and S. Prahakar, “Rights Protection for Categorical Data,” IEEE Transac¬tions on Knowledge and Data Engineering, Vol. 17, No. 7, 2005, pp. 912-926.
[33]M. Swanson, M Kobayashi, and A. Tewfik, “Multimedia Data-Embedding and Watermarking Technologies,” in Proceedings of the IEEE, Vol. 86, No. 6, 1998, pp.1064-1087.
[34]H. H. Tsai and D. W. Sun, “Color Image Watermark Extraction Based on Support Vector Machines,” Information Sciences, Vol. 177, No. 2, 2007, pp. 550-569.
[35]J. Tian, “Reversible Watermarking by Difference Expansion,” in Proceedings Workshop on Multimedia and Security, Dec. 2002, pp. 19-22.
[36]M. H. Tsai, H. Y. Tseng, and C. Y. Lai, “A Database Watermarking Technique for Tamper Detection,” in Proceedings of the 9th Joint Conference on Information Sciences, Kaohsiung, Taiwan, Oct. 2006, pp. 615-618.
[37]V. Vapnik, The Nature of Statistical Learning Theory, Springer-Verlag, New York, 1995.
[38]V. Vapnik, Statistical Learning Theory, John Wiley, New York, 1998.
[39]H. C. Wu, S. Y. Shih, and Y. H. Lai, “A Dual Database Watermarking Scheme for Malicious Tam¬pering Detection and Copyright Protection,” GESTS International Transactions on Computer Science and Engineering, Vol. 34, No. 1, 2006, pp. 151-163.
[40]P. Wong and N. Memon, “Secret and Public Key Authentication Watermarking Schemes That Resist Vector Quantization Attack,” in Proceedings of SPIE Conference on Security and Watermarking of Multimedia Contents, Vol. 3971, San Jose, California, 2000, pp. 417-427.
[41]X. T. Wang, C. Y. Shao, X. G. Xu, and X. M. Niu, “Reversible Data-Hiding Scheme for 2-D Vector Maps Based on Difference Expansion,” IEEE Transac¬tions on Information Forensics and Security, Vol. 2, No. 3, 2007, pp. 311-320.
[42]X. Y. Wang, H. Y. Yang, and C. Y. Cui, “An SVM-based Robust Digital Image Watermarking Against Desynchronization Attacks,” Signal Processing, Vol. 88, No 9, 2008, pp. 2193-2205.
[43]M. Yeung and F. Mintzer, “An Invisible Watermarking Technique for Image Verification,” in Proceedings of IEEE International Conference on Image Processing, Vol. 2, Santa Barbara, California, 1997, pp. 680-683.
[44]Y. Zhang, X. Niu, D. Zhao, J. Li, and S. Liu, “Re¬lational Databases Watermark Technique Based on Content Characteristic,” in Proceedings of the First International Conference on Innovative Computing, Information and Control (ICICIC 2006), Beijing, China, 2006, pp. 677-680.