Abstract:
With the development of Mobile Healthcare Monitoring Network (MHMN), patients can use body sensor data to monitor their health or pre-diagnose online, and clinicians can use data mining to make the right decisions. Sensitive data privacy remains a major issue.
This article presents methods for searching and pre-diagnosing encrypted data online. First, we propose a Diverse Keyword Searchable Encryption (DKSE) scheme that supports multi-dimension digital vectors range query and textual multi-keyword ranked search for a wide range of applications. PRIDO, based on the DKSE, protects patient data in data mining and online pre-diagnosis.
We achieve privacy-preserving naïve Bayesian and decision tree classifiers using the PRIDO framework and discuss its potential deployments.
Security analysis shows that patients’ data privacy can be protected without compromising confidentiality, and performance evaluation shows the efficiency and accuracy of diverse keyword search, data mining, and disease pre-diagnosis.
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