Index based quasi-identifier approach for privacy preservation data sets on cloud

Abstract

Cloud computing is used as a metaphor for the Internet, and internet-based computing that provides shared computer processing resources and the software and hardware pay-for-use to computers and other devices on demand. It is a model for enabling ubiquitous, on-demand access to a shared pool of configurable computing resources. Cloud allows to store sensitive data (Govt, Hospital) 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 nodes. Existing approaches provides very low scalability and in efficiency because they are centralized and access all data frequently when update occurs. In this paper we providing anonymized data index based quasi-identifier for efficiency of privacy preservation on large-volume, incremental data sets can be improved significantly over existing approaches.