A Robust and Reversible Watermarking Algorithm for a Relational Database Based on Continuous Columns in Histogram

Due to the discreteness of integer data, there are a large number of gaps and continuous columns in the histogram based on integer data. Aiming at the characteristics, this paper presents a robust and reversible watermarking algorithm for a relational database based on continuous columns in histogram. Firstly, it groups the database tuples according to the watermark length and the grouping key. Secondly, it calculates the prediction errors and uses the absolute values of the prediction errors to construct the histogram. Thirdly, it traverses the histogram to find all the continuous columns and in turn, computes the sum of the height of each continuous column and selects the group of continuous columns that has the largest sum as the positions to embed the watermarks. FCTD (Forest cover type data set) is utilized for experimental verification. A large amount of experimental data shows that the method is effective and robust. Not only does the data distortion caused by shifting histogram columns not exist, but the robustness of the watermark is also greatly improved.

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A reversible database watermarking method non-redundancy shifting-based histogram gaps

International Journal of Distributed Sensor Networks ◽

10.1177/1550147720921769 ◽

2020 ◽

Vol 16 (5) ◽

pp. 155014772092176 ◽

Cited By ~ 1

Author(s):

Yan Li ◽

Junwei Wang ◽

Xiangyang Luo

Keyword(s):

Relational Databases ◽

Forest Cover ◽

Large Data ◽

Histogram Shifting ◽

Data Set ◽

Forest Cover Type ◽

Absolute Value ◽

Data Distortion ◽

Type Data ◽

Distortion Rate

In relational databases, embedding watermarks in integer data using traditional histogram shifting method has the problem of large data distortion. To solve this problem, a reversible database watermarking method without redundant shifting distortion is proposed, taking advantage of a large number of gaps in the integer histogram. This method embeds the watermark bit by bit on the basis of grouping. First, an integer data histogram is constructed with the absolute value of the prediction error of the data as a variable. Second, the positional relationship between each column and the gap in the histogram is analyzed to find out all the columns adjacent to the gap. Third, the highest column is selected as the embedded point. Finally, a watermark bit is embedded on the group by the histogram non-redundant shifting method. Experimental results show that compared with existing reversible database watermarking methods, such as genetic algorithm and histogram shift watermarking and histogram gap–based watermarking, the proposed method has no data distortion caused by the shifting redundant histogram columns after embedding watermarks on forest cover type data set and effectively reduces the data distortion rate after embedding watermarks.

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Experimental Study III

Advances in Data Mining and Database Management - Predictive Analysis on Large Data for Actionable Knowledge ◽

10.4018/978-1-5225-5029-7.ch006 ◽

2018 ◽

pp. 133-150

Keyword(s):

Experimental Study ◽

Forest Cover ◽

Aggregate Data ◽

Data Set ◽

Forest Cover Type ◽

Cover Type ◽

Proposed Model ◽

Type Data ◽

Nominal Variables ◽

Pattern Prediction

This chapter provides implementation of the proposed model on Forest Cover Type data set. The chapter includes the implementation of pattern extraction from this dataset by following a series of steps discussed in the proposed model chapter. It also includes detailed implementation of pattern prediction from Automobile dataset for prediction of numeric variables, nominal variables, and aggregate data. The implementation of pattern prediction is also a series of steps as discussed before.

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A Parabolic Failure Criterion for Transversely Isotropic Rock: Modification and Verification

Mathematical Problems in Engineering ◽

10.1155/2019/8052560 ◽

2019 ◽

Vol 2019 ◽

pp. 1-12

Author(s):

Ding-Jian Wang ◽

Huiming Tang ◽

Peiwu Shen ◽

Yi Cai

Keyword(s):

Experimental Data ◽

Failure Criterion ◽

Triaxial Compression ◽

Transversely Isotropic ◽

Confining Pressure ◽

Prediction Errors ◽

Compression Tests ◽

Predictive Capacity ◽

Data Set ◽

Transversely Isotropic Rock

It is of great significance to develop a failure criterion that can describe the orientation-dependent behavior of transversely isotropic rocks. This paper presents a simplified parabolic model that is successful in predicting the strengths of rocks under different confining pressures and bedding angles. The model is a modified version of the normal parabolic criterion for intact rocks. The two orientation-dependent parameters (σcβ and kβ) in the model show trigonometric relationships with the bedding angle, and they can be readily determined through uniaxial and triaxial compression tests. The shape of the failure envelope is determined by kβ, and σcβ only affects the level of rock strength. With application to 446 experimental data, the predicting results by the parabolic criterion are highly consistent with the experimental data, and the predictive capacity of the proposed criterion is better than those of the McLamore-Gray and Tien-Kuo criteria. Besides, the prediction errors for the high confining pressure condition and the bedding-sliding failure mode are smaller than those for the low confining pressure and the non-bedding-sliding failure. Moreover, the prediction error almost remains steady with the decrease of data set, indicating that the proposed criterion is of high precision even if the experimental data are limited.

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