The compressed sensing matrices based on affine symplectic space are constructed. Meanwhile, a comparison is made with the compressed sensing matrices constructed by DeVore based on polynomials over finite fields. Moreover, we merge our binary matrices with other low coherence matrices such as Hadamard matrices and discrete fourier transform(DFT) matrices using the embedding operation. In the numerical simulations, our matrices and modified matrices are superior to Gaussian matrices and DeVore’s matrices in the performance of recovering original signals.
The leakage of sensitive data occurs on a large scale and with increasingly serious impact. It may cause privacy disclosure or even property damage. Password leakage is one of the fundamental reasons for information leakage, and its importance is must be emphasized because users are likely to use the same passwords for different Web application accounts. Existing approaches use a password manager and encrypted Web application to protect passwords and other sensitive data; however, they may be compromised or lack accessibility. The paper presents SecureWeb, which is a secure, practical, and user-controllable framework for mitigating the leakage of sensitive data. SecureWeb protects users＇ passwords and aims to provide a unified protection solution to diverse sensitive data. The efficiency of the developed schemes is demonstrated and the results indicate that it has a low overhead and are of practical use.
Shuang LiangYue ZhangBo LiXiaojie GuoChunfu JiaZheli Liu
