Abstract

The cloud has become an important platform for data storage and processing. It centralizes essentially unlimited resources (e.g., storage capacity) and delivers elastic services to end users. In proposed method, study the problem of keyword search with access control over encrypted data in cloud computing. First propose a scalable framework where user can use his attribute values and a search query to locally derive a search capability, and a file can be retrieved only when its keywords match the query and the user’s attribute values can pass the policy check. Using this framework, we propose a novel scheme called KSAC, which enables Keyword Search with Access Control over encrypted data. KSAC utilizes a recent cryptographic primitive called HPE to enforce fine-grained access control and perform multi-field query search. Meanwhile, it also supports the search capability deviation, and achieves efficient access policy update as well as keyword update without compromising data privacy. To enhance the privacy, KSAC also plants noises in the query to hide users’ access privileges. Intensive evaluations on real-world dataset are conducted to validate the applicability of the proposed scheme and demonstrate its protection for user’s access privilege. However, the security of the outsourced data has become a major concern. For privacy concerns, searchable encryption, which supports searching over encrypted data, has been proposed and developed rapidly in secure Boolean search and similarity search. However, different users may have different requirements on their queries, which mean different weighted searches. This problem can be solved perfectly in the plaintext domain, but hard to be addressed over encrypted data. In this study, we use locality-sensitive hashing (LSH) and searchable symmetric encryption (SSE) to deal with a privacy preserving weighted similarity search. In the authors’ scheme, data users can generate a search request and set the weight for each attribute according to their requirements. We treat the LSH values as keywords and mix them into the framework of SSE. We use homomorphic encryption to securely address the weight problem and return the top-k data without revealing any weight information of data users. Extensive experiments on actual datasets showed that the scheme is extremely effective and efficient.