摘要
In this paper, we consider the problems of data sharing between multiple distrusted authorities. Prior solutions rely on trusted third parties such as CAs, or are susceptible to collusion between malicious authorities, which can comprise the security of honest ones. In this paper, we propose a new multi-authority data sharing scheme - Decen- tralized Multi-Authority ABE (DMA), which is derived from CP-ABE that is resilient to these types of misbehavior. Our system distin- guishes between a data owner (DO) principal and attribute authorities (AAs): the DO owns the data but allows AAs to arbitrate access by providing attribute labels to users. The data is protected by policy encryption over these attributes. Unlike prior systems, attributes generated by AAs are not user-specific, and neither is the system susceptible to collusion between users who try to escalate their access by sharing keys. We prove our scherne correct under the Decisional Bilinear Diffie-Hellman (DBDH) assumption; we also include a com- plete end-to-end implementation that demon- strates the practical efficacy of our technique.
In this paper, we consider the problems of data sharing between multiple distrusted authorities. Prior solutions rely on trusted third parties such as CAs, or are susceptible to collusion between malicious authorities, which can comprise the security of honest ones. In this paper, we propose a new multi-authority data sharing scheme – Decentralized Multi-Authority ABE(DMA), which is derived from CP-ABE that is resilient to these types of misbehavior. Our system distinguishes between a data owner(DO) principal and attribute authorities(AAs): the DO owns the data but allows AAs to arbitrate access by providing attribute labels to users. The data is protected by policy encryption over these attributes. Unlike prior systems, attributes generated by AAs are not user-specific, and neither is the system susceptible to collusion between users who try to escalate their access by sharing keys. We prove our scheme correct under the Decisional Bilinear Diffie-Hellman(DBDH) assumption; we also include a complete end-to-end implementation that demonstrates the practical efficacy of our technique.
基金
supported by the National Natural Science Foundation of China under grant 61402160
Hunan Provincial Natural Science Foundation of China under grant 2016JJ3043
Open Funding for Universities in Hunan Province under grant 14K023