Big Data Management System for Personal Privacy Using SW and SDF
Kashinath Sarjapur, V. Suma, Sharon Christa, Jawahar J. Rao · Advances in intelligent systems and computing · 2016
In this world of internet and social network, privacy is the one word that concerns everyone. All the data concerned to a person will get updated in the web and is available at ease. Hospitals and health centers when computerizing their center will knowingly or unknowingly be a part of this. Health related data is a very sensitive data that people are reluctant to disclose. Hospitals should go an extra mile to preserve the privacy of their clients. The techniques that are available in preserving privacy don’t serve its purpose. Thus a novel method completely new in the field of big data is introduced in privacy preservation namely personalized anonymity. The central idea of this technique can be distributed into two main components. The component one of the workdeal with attributes in the patient data which is used as a flag and can be used to differentiate sensitive attribute. The attributes include sensitive disclosure flag (SDF) as well as sensitive weigh (SW). The second component deals with a new demonstration called Frequency Distribution Block (FDB) and quasi-identifier Distribution Block (QIDB), which uses the SW and SDF for anonymity. The paper provides an overview of personalized anonymity technique in medical big data which in turn enhances the privacy of users.