An Efficient and Enhance Merkle Tree for Big Data Deduplication in Cloud

Cloud computing, as an emerging computing paradigm, enables users to remotely store their data in a cloud, so as to enjoy services on-demand. With rapid development of cloud computing, more and more enterprises will outsource their sensitive data for sharing in a cloud. To keep the shared data confidential against untrusted cloud service providers (CSPs), a natural way is to store only the encrypted data in a cloud. In previous approaches designed efficient and privacy-preserving big data deduplication in cloud storage achieves both privacy-preserving and data availability, in addition takes accountability into consideration to offer better privacy assurances than existing schemes. Proposed approach is to reduce the communication, storage overheads and duplicates search in cloud storage services previous researchers designed data deduplication schemes to protect from resist brute-force attacks or ensure the efficiency and data availability, but not both conditions. In this work propose block index search approach for privacy-preserving big data deduplication to fully protect the duplicate information from disclosure, even by a malicious CSP, without affecting the capability to perform data deduplication and Merkle Tree over encrypted data, in order to derive a unique identifier of outsourced data, this identifier serves to check the availability of the same data in remote cloud servers and it is used to ensure efficient access control in cloud. Extensive security and performance analysis show that the proposed scheme is highly efficient and provably secure.