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


With the development of the Mobile Healthcare Monitoring Network (MHMN), patients' data collected by body sensors not only enables patients to monitor their own health or make online pre-diagnoses, but it also enables clinicians to make appropriate decisions by utilizing Data Mining technique. Patients can monitor their own health or make online pre-diagnoses. However, maintaining the confidentiality of sensitive data remains a major concern. In this article, we propose practical techniques for searching and making online pre-diagnoses over encrypted data. These techniques can be implemented immediately. To begin, we present a brand new Diverse Keyword Searchable Encryption (DKSE) scheme. This encryption method is designed to support multi-dimension digital vectors range queries in addition to textual multi-keyword ranked searches. This is done in order to gain a wide variety of applications in actual use. In addition, a framework known as PRIDO that is based on the DKSE was developed with the intention of safeguarding the personal information of patients during Data Mining and online pre-diagnosis. We achieve privacy-preserving naive Bayesian and decision tree classifiers using the PRIDO framework, and we discuss the framework's potential applications in actual deployments. The results of the security analysis demonstrate that the patients' data privacy can be effectively protected without compromising the confidentiality of the data. The results of the performance evaluation demonstrate the effectiveness and precision of the diverse keyword search, Data Mining, and disease pre-diagnosis, respectively.

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