科技行业的快速发展带来信息量的暴增,各行各业都需要收集和应用大量的数据,海量数据在发挥价值的同时,给数据安全领域带来了史无前例的挑战。关系型数据库作为数据的底层存储载体之一,其存储的数据规模大、数据内容丰富、数据隐私度高...科技行业的快速发展带来信息量的暴增,各行各业都需要收集和应用大量的数据,海量数据在发挥价值的同时,给数据安全领域带来了史无前例的挑战。关系型数据库作为数据的底层存储载体之一,其存储的数据规模大、数据内容丰富、数据隐私度高。数据库的数据一旦泄露将会造成巨大的损失,保护数据库的所有权,确认数据的归属刻不容缓。对于现有的数据库水印技术来说,提高水印嵌入容量和减小数据失真之间存在固有矛盾问题,为了缓解此问题且进一步提高水印的鲁棒性,提出了一种基于动态差分扩展的强鲁棒数据库水印算法。该算法选取QR码作为水印,利用经过Haar小波变换的图像低频部分进行奇异值分解(SVD,singular value decomposition),提取部分特征值,用取余后的特征值作为待嵌入的水印序列,使得相同长度的水印序列包含更多信息,缩短了嵌入水印的长度。该算法结合自适应差分进化算法和最小差值算法选择最佳嵌入属性位,以缓解传统差分扩展技术在嵌入水印时计算效率低、数据失真大、鲁棒性差的问题,提高水印嵌入容量的同时减少了数据的失真。实验结果表明,该算法保证高水印嵌入率的同时数据失真较低,能够抵御多种攻击,具有良好的鲁棒性,追踪溯源的能力强,且与现有的算法对比优势明显,在数据安全领域具有广阔的应用前景。展开更多
位置图压缩是空间域水印嵌入算法中的关键问题。为此,以提高嵌入容量为目标,提出一种基于差值位置图调整的差分扩展优化算法。考虑可扩展差值的位置分布,通过修改短连续零组提高无损压缩率,从而扩大水印净荷嵌入空间,减少嵌入总量,提高...位置图压缩是空间域水印嵌入算法中的关键问题。为此,以提高嵌入容量为目标,提出一种基于差值位置图调整的差分扩展优化算法。考虑可扩展差值的位置分布,通过修改短连续零组提高无损压缩率,从而扩大水印净荷嵌入空间,减少嵌入总量,提高峰值信噪比。仿真实验结果表明,该算法可通过选择最佳或优选调整强度增强差分扩展算法的水印嵌入性能,水印净荷嵌入容量可提升2.48倍~5.26倍,峰值信噪比可提升4.4 d B^8 d B。展开更多
The mechanism of transport of chemicals in soil is an important research topic of environmental science and engineering, and some models and methods for a variety of solute transport problems have been done. Howeve...The mechanism of transport of chemicals in soil is an important research topic of environmental science and engineering, and some models and methods for a variety of solute transport problems have been done. However. most of previous works are usually for a soil column of infinite dimension. Starting from the one-dimension transient solute transport equation and its boundary and initial condition for a solute transport problem of soil column of finite length, this work has successfully applied a variable transformation to simplify the partial differential equation of solute transport problem. And an analytical serial solution for the simplified equation is then established by the so-called separated variable method and the superposition method. Compared with numerical methods such as finite different method and finite element method, this analytical solution is more accurate and of higher computation efficiency. In addition, the solution procedure presented could be extended for applications such as quality analysis, design of physical experimentation, or parameter estimation and measurement of solute transport problems.展开更多
文摘科技行业的快速发展带来信息量的暴增,各行各业都需要收集和应用大量的数据,海量数据在发挥价值的同时,给数据安全领域带来了史无前例的挑战。关系型数据库作为数据的底层存储载体之一,其存储的数据规模大、数据内容丰富、数据隐私度高。数据库的数据一旦泄露将会造成巨大的损失,保护数据库的所有权,确认数据的归属刻不容缓。对于现有的数据库水印技术来说,提高水印嵌入容量和减小数据失真之间存在固有矛盾问题,为了缓解此问题且进一步提高水印的鲁棒性,提出了一种基于动态差分扩展的强鲁棒数据库水印算法。该算法选取QR码作为水印,利用经过Haar小波变换的图像低频部分进行奇异值分解(SVD,singular value decomposition),提取部分特征值,用取余后的特征值作为待嵌入的水印序列,使得相同长度的水印序列包含更多信息,缩短了嵌入水印的长度。该算法结合自适应差分进化算法和最小差值算法选择最佳嵌入属性位,以缓解传统差分扩展技术在嵌入水印时计算效率低、数据失真大、鲁棒性差的问题,提高水印嵌入容量的同时减少了数据的失真。实验结果表明,该算法保证高水印嵌入率的同时数据失真较低,能够抵御多种攻击,具有良好的鲁棒性,追踪溯源的能力强,且与现有的算法对比优势明显,在数据安全领域具有广阔的应用前景。
文摘位置图压缩是空间域水印嵌入算法中的关键问题。为此,以提高嵌入容量为目标,提出一种基于差值位置图调整的差分扩展优化算法。考虑可扩展差值的位置分布,通过修改短连续零组提高无损压缩率,从而扩大水印净荷嵌入空间,减少嵌入总量,提高峰值信噪比。仿真实验结果表明,该算法可通过选择最佳或优选调整强度增强差分扩展算法的水印嵌入性能,水印净荷嵌入容量可提升2.48倍~5.26倍,峰值信噪比可提升4.4 d B^8 d B。
基金Acknowledgements： The work was supported by the National Natural Science Foundation of China （No. 90502006/D0123）, Hunan provincial Natural Science Foundation of China （No. 06JJ3020） and Scientific Research Fund of Hunan Provincial Education Department （No. 06C500）.
文摘The mechanism of transport of chemicals in soil is an important research topic of environmental science and engineering, and some models and methods for a variety of solute transport problems have been done. However. most of previous works are usually for a soil column of infinite dimension. Starting from the one-dimension transient solute transport equation and its boundary and initial condition for a solute transport problem of soil column of finite length, this work has successfully applied a variable transformation to simplify the partial differential equation of solute transport problem. And an analytical serial solution for the simplified equation is then established by the so-called separated variable method and the superposition method. Compared with numerical methods such as finite different method and finite element method, this analytical solution is more accurate and of higher computation efficiency. In addition, the solution procedure presented could be extended for applications such as quality analysis, design of physical experimentation, or parameter estimation and measurement of solute transport problems.
