Abstract
The computing expedient and indulgence are made available in cloud servers by redistributing innumerable resources over the cyberspace. The utmost hefty on-demand services in cloud are data storage. In the world of technocrats, there is a colossal use of national information infrastructure from where an immense amount of data is produced in day-to-day life. To handle those prodigious data on demand is a challenging chore for current data storage systems. The prominence of data deduplication (dedupe) is pointed out by data explosion and colossal slew in redundant data. In the proposed scheme, source-based deduplication is used to eliminate duplicate data, where the client check for the unique data in local (or) remote index through the backup of lower network bandwidth with fast and lower computation overhead. Firstly, in source-based deduplication the data are stored in the physical memory and the fragments of the data are cuckoo hashed before storing the data their physical memory. Secondly, the cloud correctness of data and security is a prime concern, and it is achieved by signing the data block before sending it to the server. And the proposed scheme guarantees the data integrity by fine-grained auditing using Boneh–Lynn–Shacham (BLS) algorithm for signing process, which is one of the secured algorithms. The homomorphic authentication with random masking technique is used to attain privacy-preserving and public auditing.
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Karthika, R., Valliyammai, C., Abisha, D. (2018). Data Deduplication and Fine-Grained Auditing on Big Data in Cloud Storage. In: Reddy Edla, D., Lingras, P., Venkatanareshbabu K. (eds) Advances in Machine Learning and Data Science. Advances in Intelligent Systems and Computing, vol 705. Springer, Singapore. https://doi.org/10.1007/978-981-10-8569-7_38
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DOI: https://doi.org/10.1007/978-981-10-8569-7_38
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