Better accuracy with quantified privacy: representations learned via reconstructive adversarial network

Understand

The remarkable success of machine learning, especially deep learning, has produced a variety of cloud-based services for mobile users.

• Such services require an end user to send data to the service provider, which presents a serious challenge to end-user privacy.
• To address this concern, prior works either add noise to the data or send features extracted from the raw data.
• They struggle to balance between the utility and privacy because added noise reduces utility and raw data can be reconstructed from extracted features.