A Blind Robust Reversible Watermark Scheme for Textual Relational Databases with Virtual Primary Key

2015 ◽  
pp. 75-89 ◽  
Author(s):  
Chin-Chen Chang ◽  
Thai-Son Nguyen ◽  
Chia-Chen Lin
Keyword(s):  
Relational Databases ◽  
Watermark Scheme ◽  
Primary Key ◽  
Reversible Watermark

A reversible watermark scheme for 2D vector map based on reversible contrast mapping

2012 ◽  
Vol 6 (9) ◽  
pp. 1117-1125 ◽  
Author(s):  
Peng Fei ◽  
Chen Li ◽  
Long Min
Keyword(s):  
Watermark Scheme ◽  
Vector Map ◽  
2D Vector Map ◽  
Reversible Watermark

scholarly journals LRW-CRDB: Lossless Robust Watermarking Scheme for Categorical Relational Databases

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2191
Author(s):  
Chia-Chen Lin ◽  
Thai-Son Nguyen ◽  
Chin-Chen Chang
Keyword(s):  
Data Hiding ◽  
Relational Database ◽  
Relational Databases ◽  
Experimental Results ◽  
Robust Watermarking ◽  
Watermark Embedding ◽  
Lossless Watermarking ◽  
Watermarking Scheme ◽  
Reversible Watermark

In 2002, Agrawal and Kiernan defined six basic requirements, including preventing illegal watermark embedding and authentication, reversibility, robustness, and others, which must be satisfied when a reversible watermark is designed for relational databases. To meet these requirements, in this paper, a lossless watermarking scheme for a categorical relational database called LRW-CRDB (lossless robust watermarking for categorical relational databases) is proposed. In our LRW-CRDB scheme, the database owner needs to generate two secret embedding keys, K1 and K2, in advance. Then, two reference sets are generated based on two different secret embedding keys and a symmetry-based data hiding strategy, and then these are used for the watermark embedding phases. Experimental results confirmed that our LRW-CRDB scheme successfully detects 100% of hidden watermarks, even when more than 95% of the watermarked relational database has been deleted. In other words, the robustness of our proposed LRW-CRDB scheme outperforms other existing schemes under a variety of possible attacks, such as alteration, sorting, deletion, and mix-match attacks.

XML Modeling of Fuzzy Data with Relational Databases

2011 ◽  
Vol 34 (2) ◽  
pp. 291-303 ◽  
Author(s):  
Li YAN ◽  
Zong-Min MA ◽  
Jian LIU ◽  
Fu ZHANG
Keyword(s):  
Relational Databases ◽  
Fuzzy Data

XPERT : An XML Query Processing System using Relational Databases

2006 ◽  
Vol 13D (1) ◽  
pp. 1-10
Author(s):  
Min-Kyoung Jung ◽  
Dong-Kweon Hong
Keyword(s):  
Query Processing ◽  
Relational Databases ◽  
Processing System ◽  
Xml Query Processing

scholarly journals Bloom Filter-Based Parallel Architecture for Accelerating Equi-Join Operation on FPGA

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1778
Author(s):  
Binhao He ◽  
Meiting Xue ◽  
Shubiao Liu ◽  
Wei Luo
Keyword(s):  
Relational Databases ◽  
Parallel Architecture ◽  
Operation Time ◽  
Bloom Filter ◽  
Search Tree ◽  
Binary Search Tree ◽  
Maximum Acceleration ◽  
Data Intensive ◽  
Match Rate ◽  
Field Programmable

As one of the most important operations in relational databases, the join is data-intensive and time-consuming. Thus, offloading this operation using field-programmable gate arrays (FPGAs) has attracted much interest and has been broadly researched in recent years. However, the available SRAM-based join architectures are often resource-intensive, power-consuming, or low-throughput. Besides, a lower match rate does not lead to a shorter operation time. To address these issues, a Bloom filter (BF)-based parallel join architecture is presented in this paper. This architecture first leverages the BF to discard the tuples that are not in the join result and classifies the remaining tuples into different channels. Second, a binary search tree is used to reduce the number of comparisons. The proposed method was implemented on a Xilinx FPGA, and the experimental results show that under a match rate of 50%, our architecture achieved a high join throughput of 145.8 million tuples per second and a maximum acceleration factor of 2.3 compared to the existing SRAM-based join architectures.

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