Decision Tree Evaluation on Sensitive Datasets for Secure e-Healthcare Systems

By collecting and analyzing patients' e-healthcare data in Medical Internet-of-Things (MIOT), e-Healthcare providers can offer alternative and helpful evaluation services of the risk of diseases to patients. However, e-Healthcare providers cannot cope with the huge volumes of data and respond to this online service. Providers typically outsource medical data to powerful medical cloud servers. Since outsourced servers are not fully trusted, a direct evaluation service will inevitably result in privacy risks concerning the patient's identity or original medical data. It is hard to hide the results of an evaluation from the single-server model unless a fully homomorphic cryptosystem is used or the patients must communicate online with the cloud multiple times in an inefficient manner. With regards to these issues, this article proposes a
S
ecure and
P
rivacy-
P
reserving
D
ecision
T
ree
E
valuation scheme (namely SPP-DTE) to achieve
secure disease diagnosis classification under e-Healthcare systems without revealing the sensitive information of patients such as physiological data or the private data of medical providers such as the structure of decision trees.
Our proposed scheme uses
modified KNN computation to match the similarity and preserve the confidentiality of raw data
and also applies
matrix randomization and monotonically increasing and one-way functions to confuse the intermediate results
. The experiment is conducted in data sets from UCI machine learning repository of medical health data. Our analysis indicates that the proposed SPP-DTE scheme is efficient in terms of computational cost and communication overhead that is practical and efficient for privacy protection in e-Healthcare classification and diagnosis system.