New Publicly Verifiable Data Deletion Supporting Efficient Tracking for Cloud Storage

Changsong Yang ； Xiaoling Tao ； Qiyu Chen

《International Journal of Network Security》 22卷5期 (2020/09) Pp. 885-896

https://doi.org/10.6633/IJNS.202009_22(5).20

摘要

With the rapid development of cloud storage, an increasing number of data owners are willing to outsource their data to cloud server to greatly reduce local storage overhead. However, in cloud storage, the ownership of the outsourced data is disconnected from the management, which makes the outsourced data deletion become a crucial security challenge: the cloud server might reserve the data maliciously for economic interests, and return wrong deletion results to cheat the data owners. To solve this problem, we design a novel outsourced data deletion scheme. If the cloud server does not execute deletion command honestly, the data owner can detect the dishonest data reservation by checking the returned deletion evidence. Additionally, we adopt Merkle hash tree to achieve public verifiability in outsourced data deletion without requiring any trusted third party. Meanwhile, the proposed scheme is able to achieve efficient data leakage source tracking to prevent the data owner and the cloud server from slandering each other. Finally, we prove that our scheme can satisfy the desired security requirements.

關鍵字

Cloud Storage ； Data Deletion ； Efficient Tracking ； Merkle Hash Tree ； Public Verifiability