Privacy-Preserving Diverse Keyword Search and Online Pre-Diagnosis in Cloud Computing

With the development of Mobile Healthcare Monitoring Network (MHMN), patients' data collected by body sensors not only allows patients to monitor their health or make online pre-diagnosis but also enables clinicians to make proper decisions by utilizing data mining technique. However, sensitive data privacy is still a major concern. In this article, we propose practical techniques for searching and making online pre-diagnosis over encrypted data. First, we propose a new Diverse Keyword Searchable Encryption (DKSE) scheme which supports multi-dimension digital vectors range query and textual multi-keyword ranked search to gain a broad range of applications in practice. In addition, a framework called PRIDO based on the DKSE is designed to protect patients' personal data in data mining and online pre-diagnosis. According to the PRIDO framework, we achieve privacy-preserving naive Bayesian and decision tree classifiers and discuss its potential applications in actual deployments. Security analysis proves that patients' data privacy can be well protected without loss of data confidentiality, and performance evaluation demonstrates the efficiency and accuracy in the diverse keyword search, data mining, and disease pre-diagnosis, respectively.

Automated summary

This article presents techniques for encrypted data search and online pre-diagnosis in the context of Mobile Healthcare Monitoring Network. It proposes a new DKSE scheme and a framework called PRIDO to protect patients' personal data while enabling efficient and accurate data mining and disease pre-diagnosis.