Abstract
The advancements in communication technologies and a rapid increase in the usage of IoT devices have resulted in an increased data generation rate. Storing, managing, and processing large quantities of unstructured data generated by IoT devices remain a huge challenge to cloud service providers (CSP). To reduce the storage overhead, CSPs implement deduplication algorithms on the cloud storage servers. It identifies and eliminates the redundant data blocks. However, implementing post-progress deduplication schemes does not address the bandwidth issues. Also, existing convergent key-based deduplication schemes are highly vulnerable to confirmation of file attacks (CFA) and can leak confidential information. To overcome these issues, FogDedupe, a fog-centric deduplication framework, is proposed. It performs source-level deduplication on the fog nodes to reduce the bandwidth usage and post-progress deduplication to improve the cloud storage efficiency. To perform source-level deduplication, a distributed index table is created and maintained in the fog nodes, and post-progress deduplication is performed using a multi-key homomorphic encryption technique. To evaluate the proposed FogDedupe framework, a testbed environment is created using the open-source Eucalyptus v.4.2.0 software and fog project v1.5.9 package. The proposed scheme tightens the security against CFA attacks and improves the storage overhead by 27% and reduces the deduplication latency by 12%.