An Efficient and Fine-grained Big Data Access Control Scheme with Privacypreserving Policy

Abstract

How to management the access of the massive quantity of massive knowledge becomes a awfully difficult
issue, particularly once huge knowledge are keep within the cloud. Cipher text-Policy Attribute primarily based coding
(CP-ABE) may be a promising coding technique permits end-users to inscribe their knowledge below the access policies
outlined over some attributes of knowledge customers and solely allows data customers whose attributes satisfy the
access policies to rewrite the info[1]. In CP-ABE, the access policy is connected to the cipher text in plaintext type,
which can additionally leak some personal info regarding end-users. Existing strategies solely partly hide the attribute
values within the access policies, whereas the attribute names are still unprotected. During this paper, we have a
tendency to propose Associate in Nursing economical and fine-grained huge knowledge access management theme with
privacy-preserving policy[3]. Specifically, we have a tendency to hide the complete attribute (rather than solely its
values) within the access policies. to help knowledge coding, we have a tendency to additionally style a unique Attribute
Bloom Filter to gauge whether or not Associate in Nursing attribute is within the access policy and find the precise
position within the access policy if it's within the access policy. Security analysis and performance analysis show that our
theme will preserve the privacy from any LSSS access policy while not using abundant overhead.