Paper Details

Cloud computing is a compilation of existing techniques and technologies, packaged within a newinfrastructure paradigm that offers improved scalability, elasticity, business agility, faster startup time, reducedmanagement costs, and just-in-time availability of resources Also a massive concentration of risk expected loss from asingle breach can be significantly larger concentration of “users” represents a concentration of threats Ultimately,Cloud allows to store sensitive data in which the digital data is stored in logical pools, the physical storage spansmultiple server’s physical environment is typically owned and managed by a hosting company. Privacy is mostimportant sensitive data ,But the privacy requirements can be potentially violated when new data join over time Exitingmethods address this problem via re-anonym zing datasets from scratch and privacy preservation over incrementaldata sets is still challenging in the context of cloud because most data sets are of huge volume and distributed acrossmultiple storage anodes exiting approaches suffer from poor scalability and inefficiency because they are centralizedand access all data frequently when update occurs. In this paper, we propose an efficient quasi-identifierindex based approach to ensure privacy preservation and achieve high data utility over incremental and distributed datasets on cloud. Quasi-identifiers, which represent the groups of anonymized data, are indexed for efficiency.

Rayapati Venkata Sudhakar, Dr.T.CH.Malleswara Rao, “Privacy preservation data sets on cloud in quasi-identifier method”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 3, Issue 9, pp. 235-239, September 2016.