Privacy preservation data sets on cloud in quasi-identifier method

Abstract

Cloud computing is a compilation of existing techniques and technologies, packaged within a new
infrastructure paradigm that offers improved scalability, elasticity, business agility, faster startup time, reduced
management costs, and just-in-time availability of resources Also a massive concentration of risk expected loss from a
single 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 spans
multiple server’s physical environment is typically owned and managed by a hosting company. Privacy is most
important sensitive data ,But the privacy requirements can be potentially violated when new data join over time Exiting
methods address this problem via re-anonym zing datasets from scratch and privacy preservation over incremental
data sets is still challenging in the context of cloud because most data sets are of huge volume and distributed across
multiple storage anodes exiting approaches suffer from poor scalability and inefficiency because they are centralized
and access all data frequently when update occurs. In this paper, we propose an efficient quasi-identifier
index based approach to ensure privacy preservation and achieve high data utility over incremental and distributed data
sets on cloud. Quasi-identifiers, which represent the groups of anonymized data, are indexed for efficiency.