Privacy Preserving Machine Learning: Threats and Solutions
arXiv:1804.11238v1408 citations
Originality Synthesis-oriented
AI Analysis
It addresses privacy issues for users and practitioners in machine learning, but is incremental as it provides an introductory overview rather than new solutions.
The paper tackles the challenge of addressing privacy concerns in machine learning systems by bridging the knowledge gap between the machine learning and privacy communities, focusing on techniques to protect data.
For privacy concerns to be addressed adequately in current machine learning systems, the knowledge gap between the machine learning and privacy communities must be bridged. This article aims to provide an introduction to the intersection of both fields with special emphasis on the techniques used to protect the data.