Enhanced anonymization algorithm to preserve confidentiality of data in public cloud
Amalraj Irudayasamy, Arockiam Lawrence · 2014
Cloud computing offers immense computation control and storing volume which permit users to organize applications. Many confidential and sensitive presentations like health facilities are constructed on cloud for monetary and working expediency. Generally, information in these requests is masked to safeguard the owner's confidential information, but such information can be possibly despoiled when new information is added to it. Preserving the confidentiality over distributed data sets is still a big challenge in the cloud environment because most of this information are huge and ranges through many storage nodes. Prevailing methods undergo reduced scalability and incompetence since information is assimilated and accesses all data repeatedly when apprises is done. In this paper, an efficient hash centered quasi-identifier anonymization method is introduced to confirm the confidentiality of the sensitive information and attain great value over spread data sets on cloud. Quasi-identifiers, which signify the groups of anonymized data, are hashed for adeptness. Consequently, a procedure is framed to fulfill this methodology. By this method, when deployed, results validate the effectiveness of confidential conservation on huge data sets that can be amended considerably over existing methods.