Open AccessA Privacy-Preserving Technique for Incremental Dataset on Cloud by Synthetic Data Perturbation
Author(s) -
D. Vigneswari,
Kumar Kumar N,
R. Lakshmi Tulasi
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i3.34.19219
Subject(s) - cloud computing , computer science , masking (illustration) , logarithm , adversary , computer security , health records , electronic health record , obfuscation , data mining , internet privacy , data science , health care , mathematics , art , mathematical analysis , economics , visual arts , economic growth , operating system
Cloud is an impetus technology revolution, allowing data providers to store their privatized Electronic Health Record (EHR) for further analysis and outlook with a compromised privacy, where the shared data being exposed to various adversary attacks and malware threats. There are several masking and randomization techniques that provide hints of privacy. In this paper, the Electronic Health Record (EHR) values are perturbed using logarithmic data perturbation and outsourced to the cloud.Aims.To develop a privacy-preserving technique for effective sharing ofElectronic Health Record (EHR) on a cloud by logarithmic data perturbation.Methods.Synthetic logarithmic transformation is applied to the sensitive values in a record before they are outsourced and for better privacy.Results. Synthetic Logarithmic Transformation along with the incremental anonymization produces effective result compared to classical anonymization..