The increasing popularity of remote Cloud File Sharing (CFS) has become a major concern for privacy breach of sensitive data. Aiming at this concern, we present a new resource sharing framework by integrating enterprise-side Attribute-Based Access Control/eXtensible Access Control Markup Language (ABAC/XACML) model, client-side Ciphertext-Policy Attribute-Based Encryption (CP-ABE) scheme, and cloud-side CFS service. Moreover, the framework workflow is provided to support the encrypted-file writing and reading algorithms in accordance with ABAC/XACML-based access policy and attribute credentials. However, an actual problem of realizing this framework is that policy matrix, derived from access policy, seriously affects the performance of existing CP-ABE from Lattice (CP-ABE-L) schemes. To end it, we present an optimal generation algorithm of Small Policy Matrix (SPM), which only consists of small elements, and generates an all-one reconstruction vector. Based on such a matrix, the improved CP-ABE-L scheme is proposed to reduce the cumulative errors to the minimum and prevent the enlargement of error bounds. Furthermore, we give the optimal estimation of system parameters to implement a valid Error Proportion Allocation (EPA). Our experimental results indicate that our scheme has short size of parameters and enjoys efficient computation and storage overloads. Thus, our new framework with optimization methods is conducive to enhancing the security and efficiency of remote work on CFS.

The stunning growth of Internet users through Cloud File Sharing (CFS) is raising great concerns about unprecedented cloud security and privacy breach. Also, the recent breakthrough in quantum computing further reinforces this kind of concerns, thus we exploit an efficient solution to guarantee personal privacy and resist quantum attacks in the CFS service. In our solution, we integrate the Attribute-based Access Control/eXtensible Access Control Markup Language (ABAC/XACML) model and the Ciphertext-Policy Attribute-Based Encryption (CP-ABE) into the CFS. To improve the performance of CP-ABE, we make use of an optimization method to convert the ABAC/XACML policy into a Small Policy Matrix (SPM). We further prove that this matrix has small coefficients and generates an all-one reconstruction vector, such that it reduces the cumulative error in lattice cryptosystem to the minimum. By using the SPM, we design a new CP-ABE scheme from Lattice (CP-ABE-L) to prevent the enlargement of error bounds. We also give the optimal estimation of system parameters, which satisfy three lattice-generation conditions to implement a valid Error Proportion Allocation (EPA). Our scheme is proved secure against chosen-plaintext attack with a selective attribute set under the Decision Learning with Errors (DLWE) assumption in the standard model. The performance evaluation and analyses illustrate that our scheme not only has short parameters, but also maintains efficient computation and reasonable storage overloads.