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Privacy-Preserving Outsourced Support Vector Machine Design for Secure Drug Discovery
Privacy-Preserving Outsourced Support Vector Machine Design for Secure Drug Discovery
Privacy-Preserving Outsourced Support Vector Machine Design for Secure Drug Discovery

Privacy-Preserving Outsourced Support Vector Machine Design for Secure Drug Discovery

₹5,000.00 ₹4,000.00
Product Code: Java - Cloud computing
Availability: In Stock
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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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Package Includes

Software Projects Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. UML Diagrams
  6. SRS
  7. Source Code
  8. Screen Shots
  9. Software Links
  10. Reference Papers
  11. Full Project Documentation
  12. Online support


The Delivery time for software projects is 2 -3 working days. Some of the software projects will require Hardware interface. Please go through the hardware Requirements in the abstract carefully. The Hardware will take 7-8 Working Days

 

Hardware Projects Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. Datasheets
  6. Circuit Diagrams
  7. Source Code
  8. Screen Shots & Photos
  9. Software Links
  10. Reference Papers
  11. Lit survey
  12. Full Project Documentation
  13. Online support


The Delivery time for Hardware projects is 7-8 working days.

   

Mini Projects: Software Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. UML Diagrams
  6. SRS
  7. Source Code
  8. Screen Shots
  9. Software Links
  10. Reference Papers
  11. Full Project Documentation
  12. Online support

 

The Delivery time for software Miniprojects is 2 -3 working days.

 

Mini Projects - Hardware includes

  1. Demo  Video
  2. Abstract
  3. PPT
  4. Datasheets
  5. Circuit Diagrams
  6. Source Code
  7. Screen Shots & Photos
  8. Software Links
  9. Reference Papers
  10. Full Project Documentation
  11. Online support

The Delivery time for Hardware Mini projects is 7-8 working days.