U’Dedup: Updatable Block-Level Deduplication Scheme Over Similar Data in Fog-Assisted Cloud Storage
Min He, Xiaoyu Zhang, Liangmin Wang · IEEE Internet of Things Journal · 2025
Fog-assisted cloud storage enables efficient collection and management of Internet of Things data, while large-scale data raise severe requirements for storage space. Deduplication schemes over similar data have been investigated to relieve the storage pressure. However, existing schemes are designed based on an idealized assumption that users can accept a certain degree of data loss or the stored data modification. When the uploaded data reaches the preset similarity threshold, only one copy will be stored, which causes data loss. Meanwhile, different dynamic operations on only one copy will cause the stored data modification. In this paper, we propose U’Dedup to address the above challenge, which is the first block-level deduplication scheme over similar data. The key component of U’Dedup is a self-built tree data structure that supports different update requirements to avoid data modification without duplicating the stored data. U’Dedup ensures the completeness of all unique data to avoid data loss and constructs a dual deduplication architecture to relieve the computing pressure of cloud. Finally, the security analysis proved that U’Dedup is secure in the random oracle model. Experimental results show that U’Dedup achieves 57.3% 90.6% upload computation cost saving, and at least 9.5× retrieval computation cost reduction.