Privacy-preserving Machine Learning through Data Obfuscation

Understand

As machine learning becomes a practice and commodity, numerous cloud-based services and frameworks are provided to help customers develop and deploy machine learning applications.

• While it is prevalent to outsource model training and serving tasks in the cloud, it is important to protect the privacy of sensitive samples in the training dataset and prevent information leakage to untrusted third parties.
• Past work have shown that a malicious machine learning service provider or end user can easily extract critical information about the training samples, from the model parameters or even just model outputs.
• In this paper, we propose a novel and generic methodology to preserve the privacy of training data in machine learning applications.