Enabling Secure and Efficient Data Deduplication in Cloud Storage Systems Using Convergent Encryption and Bloom Filters

Abstract

Data deduplication is a crucial technique employed in cloud storage systems to eliminate redundant
data and optimize storage utilization. However, the process of identifying and removing duplicate
data raises significant security and privacy concerns, as it may expose sensitive information to
unauthorized parties. This research paper presents a novel approach to enable secure and efficient
data deduplication in cloud storage systems using convergent encryption and Bloom filters. The
proposed framework ensures data confidentiality while facilitating the identification and
elimination of duplicate data across multiple users. The research methodology involves the
development of a secure data deduplication protocol that combines convergent encryption and
Bloom filters. Convergent encryption is employed to generate identical ciphertext for identical
plaintext data, enabling the identification of duplicates without compromising data confidentiality.
Bloom filters, on the other hand, are used to efficiently represent the set of encrypted data blocks
and facilitate fast duplicate checks. The proposed approach is designed to handle both file-level
and block-level deduplication, providing flexibility and granularity in eliminating redundant data.
The framework is evaluated through extensive security analysis and performance measurements,
demonstrating its effectiveness in preserving data confidentiality while achieving high
deduplication ratios and minimizing storage overhead. The study presents a detailed comparison
with existing data deduplication techniques, highlighting the advantages of the proposed approach
in terms of security, efficiency, and scalability. The findings of this research have significant
implications for cloud storage providers and users seeking to optimize storage utilization while
maintaining data security and privacy. By enabling secure and efficient data deduplication, the
proposed framework helps reduce storage costs, improve data management, and enhance the
overall performance of cloud storage systems. This research contributes to the advancement of
secure and privacy-preserving data deduplication techniques in the era of cloud computing,
addressing the critical challenges of data confidentiality and storage efficiency in multi-tenant
environments. [