Cloud file sharing (CFS) has become one of the important tools for enterprises to reduce technology operating costs and improve their competitiveness. Due to the untrustworthy cloud service provider, access control and security issues for sensitive data have been key problems to be addressed. Current solutions to these issues are largely related to the traditional public key cryptography, access control encryption or attribute-based encryption based on the bilinear mapping. The rapid technological advances in quantum algorithms and quantum computers make us consider the transition from the tradtional cryptographic primitives to the post-quantum counterparts. In response to these problems, we propose a lattice-based Ciphertext-Policy Attribute-Based Encryption(CP-ABE) scheme, which is designed based on the ring learing with error problem, so it is more efficient than that designed based on the learing with error problem. In our scheme, the indirect revocation and binary tree-based data structure are introduced to achieve efficient user revocation and dynamic management of user groups. At the same time, in order to further improve the efficiency of the scheme and realize file sharing across enterprises, the scheme also allows multiple authorities to jointly set up system parameters and manage distribute keys. Furthermore, by re-randomizing the user’s private key and update key, we achieve decryption key exposure resistance(DKER) in the scheme. We provide a formal security model and a series of security experiments, which show that our scheme is secure under chosen-plaintext attacks. Experimental simulations and evaluation analyses demonstrate the high efficiency and practicality of our scheme.
StOMP algorithm is well suited to large-scale underdetermined applications in sparse vector estimations. It can reduce computation complexity and has some attractive asymptotical statistical properties. However, the estimation speed is at the cost of accuracy violation. This paper suggests an improvement on the StOMP algorithm that is more efficient in finding a sparse solution to the large-scale underdetermined problems. Also, compared with StOMP, this modified algorithm can not only more accurately estimate parameters for the distribution of matched filter coefficients, but also improve estimation accuracy for the sparse vector itself. Theoretical success boundary is provided based on a large-system limit for approximate recovery of sparse vector by modified algorithm, which validates that the modified algorithm is more efficient than StOMP. Actual computations with simulated data show that without significant increment in computation time, the proposed algorithm can greatly improve the estimation accuracy.
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