Privacy-Preserving Outsourced Support Vector Machine Design for Secure Drug Discovery
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Product Description
Aim
To allow the cloud to securely use multiple drug
formula providers’ drug formulas to train Support Vector Machine (SVM) and
Naïve Bayes (NB) provided by the analytical model provider.
Abstract
In this paper, we propose a framework for
privacy-preserving outsourced drug discovery in the cloud, which we refer to as
POD. Specifically, POD is designed to allow the cloud to securely use multiple
drug formula providers’ drug formulas to train Support Vector Machine (SVM)
provided by the analytical model provider. In our approach, we design secure
computation protocols to allow the cloud server to perform commonly used
integer and fraction computations. To securely train the SVM, we design a
secure SVM parameter selection protocol to select two SVM parameters and
construct a secure sequential minimal optimization protocol to privately
refresh both selected SVM parameters. The trained SVM classifier can be used to
determine whether a drug chemical compound is active or not in a
privacy-preserving way. Lastly, we prove that the proposed POD achieves the
goal of SVM training and chemical compound classification without privacy
leakage to unauthorized parties, as well as demonstrating its utility and
efficiency using three real-world drug datasets.
Proposed System
We propose a Privacy preserving Outsourced Support
Vector Machine Design for Secure Drug discovery in the cloud environment,
hereafter referred to as POD. Unlike existing drug discovery frameworks, our
POD seeks to achieve it efficiently. We are not using three real time datasets
to check the efficiency of potential new drug component. Instead of using existing
datasets we are using another one data mining algorithm Naïve Bayes(NB). This two algorithms are used to train the
uploaded drug dataset (CSV file). In final we will get trained data and
accuracy for that uploaded dataset. Drug tester will check that new drug
component. Drug tester doesn’t know the contents of that file; they will get
the trained data only. Then they let us know the file was active or not. And
finally, admin will approve the drug component.
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