- 學位論文
支援向量機於資料庫浮水印技術之應用
A Study on Database Watermarking by Applying Support Vector Machine
摘要
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。
並列摘要
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.
參考文獻
延伸閱讀
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