Secure Data Transformation and Remove the Anomalies in Cloud Computing

Abstract

In this article, I focus on the problem of verifiable outsourced data deletion, insert and update in cloud computing. If the cloud server does not honestly maintain/delete the data and generate corresponding evidences, users can easily detect the cloud server's malicious behaviours with an overwhelming probability. I propose an efficient fine-grained outsourced data anomalies scheme based on a Bloom filter that can also achieve public and private verifiability of the storage and deletion results. Cloud storage, one of the most attractive services offered by cloud computing, can provide users with boundless storage capacity.