Secure Cloud Data Deduplication with Effective Re-encryption PROJECT TITLE : Secure Cloud Data Deduplication with Efficient Re-encryption ABSTRACT: The data deduplication technique has seen widespread adoption among commercial cloud storage providers, which is both important and necessary in order to keep up with the exponential growth of data. Numerous secure data deduplication schemes have been designed and put into practice in a variety of settings in order to provide an additional layer of protection for the confidentiality of the sensitive data stored by users who make use of the outsourced storage mode. Among these schemes, secure and efficient re-encryption for encrypted data deduplication has attracted the attention of a large number of academics, and a large number of solutions have been designed to support dynamic ownership management. In this paper, we focus on the re-encryption deduplication storage system, and we show that the recently designed lightweight rekeying-aware encrypted deduplication scheme (REED), which is vulnerable to an attack that we call the stub-reserved attack, is vulnerable to that attack. In addition, we suggest a method for the safe deduplication of data that also features an effective re-encryption. This method is founded on the convergent all-or-nothing transform (CAONT), and it uses randomly sampled bits from the Bloom filter. Our system is able to withstand the stub-reserved attack thanks to the inherent property of the one-way hash function, and we can guarantee the data privacy of sensitive information belonging to data owners. In addition, data owners are not required to re-encrypt the entire package through the CAONT; rather, they are only required to re-encrypt a small portion of it. This effectively reduces the computational overhead that the system must deal with. In conclusion, the results of the security analysis and the experiments show that our scheme is both safe and effective when it comes to re-encryption. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Semantics of Cloud Computing Data Mining Services Data Center Networks SDN-based Traffic Matrix Estimation Through Large Size Flow Identification