| Attribute-Based Encryption(ABE),a frontier research of public key cryptography,can safeguard data confidentiality and provide access control.The access policies of many existing ABE schemes are public.How to hide the access policies is an essential problem for the actual scenario with strict privacy protection requirements.In addition,the unreadable nature of encrypted data limits the flexibility and accuracy of data retrieval,and how to realize the search for encrypted data is also a critical problem.In order to solve the above problems,this thesis studies the ABE hidden in the access policy and the expressive asymmetric searchable encryption using the arithmetic span programs(ASPs).The contributions of this thesis are as follows:This thesis proposes an expressive policy partially hidden ABE with ASPs,which can provide fine-grained access control and data privacy protection in cloud-assisted Io T scenarios.Compared with similar ABE schemes,the proposed scheme can more effectively describe complex access control policies through ASPs.In addition,the scheme is adaptively secure and can be formally proved.The proposed scheme eliminates the requirement of defining attribute space in advance during system initialization.Finally,the scheme is deployed and tested in the cloud-assisted Io T environment,and its efficiency and practicability are verified.By utilizing the access structure in ABE,this thesis proposes an asymmetric expressive searchable encryption with ASPs,which supports complex search functions.The search policy is efficiently expressed due to the use of the ASPs.The proposed scheme is unbounded,so keywords do not need predefined parameters during system initialization.Verification of the scheme’s feasibility and efficiency in a cloud-assisted Io T environment is achieved through thorough theoretical analysis and simulations.This thesis formally demonstrates that the Matrix Decisional Diffie-Hellman assumption’s proposed scheme is adaptively secure through dual-system encryption. |