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Preserving differential privacy in convolutional deep belief networks.

NhatHai Phan¹, Xintao Wu², Dejing Dou³

¹New Jersey Institute of Technology, Newark, NJ, USA.

Machine Learning

|March 15, 2019

Summary

This study introduces a private convolutional deep belief network (pCDBN) that protects sensitive health data using differential privacy. The novel method effectively preserves privacy while maintaining high accuracy in various classification and prediction tasks.

Keywords:

Deep learning Differential privacy Health informatics Human behavior prediction Image classification

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Area of Science:

Computer Science
Machine Learning
Healthcare Informatics

Background:

Deep learning models in healthcare raise significant privacy concerns due to the use of sensitive personal data.
Existing research on privacy preservation in deep learning remains limited.

Purpose of the Study:

To develop a privacy-preserving deep learning model, specifically a private convolutional deep belief network (pCDBN).
To implement differential privacy within the convolutional deep belief network (CDBN) framework.

Main Methods:

The proposed pCDBN leverages a functional mechanism to apply differential privacy to the objective functions of CDBNs.
Chebyshev expansion is utilized to approximate objective functions, enabling theoretical analysis of sensitivity and error bounds.
The model was tested on a health social network (YesiWell) and a handwriting digit dataset (MNIST).

Main Results:

Theoretical analysis confirmed the feasibility of preserving differential privacy in CDBNs.
Experimental results demonstrated the high effectiveness of the pCDBN across human behavior prediction, classification, and handwriting digit recognition.
The pCDBN significantly outperformed existing privacy-preserving solutions.

Conclusions:

The developed pCDBN offers a robust solution for privacy preservation in deep learning applications within healthcare and other sensitive domains.
This approach effectively balances data privacy with model performance, paving the way for more secure AI in sensitive areas.