An improved privacy preservation technique in health-cloud

Abstract

Abstract In today’s cloud computing environment, health-cloud preserves the person specific sensitive information for several purposes such as bio-medical research, health insurance companies, medical data analysis, etc. When any authorized person accesses these clouds, the released data should not compromise any individuals’ privacy and it remains useful as well. In the health-cloud system, the data must be released in such a way that any individuals’ identity cannot be revealed. The database management system alone cannot ensure any individual’s privacy. The Access Control (AC) models are also not able to protect the data from indirect access or multiple queries. To remove such issues inference control is one of the techniques which ensures the data confidentiality from indirect data access. In this paper, we have proposed a hybrid technique which includes two different inference control techniques, query set size restriction and k-anonymity to ensure individuals’ privacy. A query set size restriction is used to prevent the sensitive data from inference attacks, whereas k-anonymity is implemented to protect the data from linking attacks. Both these techniques reach a certain privacy level with satisfactory data utilization. We have also generated a rule set to increase the privacy of healthcare data.