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
Program slicing is a well-known program analysis technique that extracts the elements of a program related to a particular computation. The current slicing methods, however, are singular （mainly based on a program or...Program slicing is a well-known program analysis technique that extracts the elements of a program related to a particular computation. The current slicing methods, however, are singular （mainly based on a program or system dependence graph）, and lack good reusability and flexibility. In this paper, we present a novel formal method for program slicing, modular monadic program slicing, which abstracts the computation of program slicing as a slice monad transformer, and applies it to semantic descriptions of the program analyzed in a modular way, forming the corresponding monadic slicing algorithms. The modular abstraction mechanism allows our slicing method to possess excellent modularity and language-flexibility properties. We also give the related axioms of our slice monad transformer, the proof of the correctness and the implementation of monadic slicing algorithms. We reveal the relations of our algorithms and graph-reachable slicing algorithms.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。
基金Supported by the Natural Science Research Plan for High School of Jiangsu Province (Grant No. 05KJD520151)
文摘Program slicing is a well-known program analysis technique that extracts the elements of a program related to a particular computation. The current slicing methods, however, are singular （mainly based on a program or system dependence graph）, and lack good reusability and flexibility. In this paper, we present a novel formal method for program slicing, modular monadic program slicing, which abstracts the computation of program slicing as a slice monad transformer, and applies it to semantic descriptions of the program analyzed in a modular way, forming the corresponding monadic slicing algorithms. The modular abstraction mechanism allows our slicing method to possess excellent modularity and language-flexibility properties. We also give the related axioms of our slice monad transformer, the proof of the correctness and the implementation of monadic slicing algorithms. We reveal the relations of our algorithms and graph-reachable slicing algorithms.
