- Privacy-preserving AI – Why do we need it? - May 29, 2020.
Various data privacy threats can result from the usual process of building and constructing data and AI-based systems. Avoiding these challenges can be supported by utilizing state-of-the-art technologies in the domain of privacy-preserving AI.
AI, Differential Privacy, Privacy
- PySyft and the Emergence of Private Deep Learning - Jun 27, 2019.
PySyft is an open-source framework that enables secured, private computations in deep learning, by combining federated learning and differential privacy in a single programming model integrated into different deep learning frameworks such as PyTorch, Keras or TensorFlow.
Deep Learning, Differential Privacy, Privacy, Python, Security
- Differential Privacy: How to make Privacy and Data Mining Compatible - Jan 9, 2015.
Can privacy coexist with machine learning and data mining? Differential privacy allows the learning of general characteristics of populations while guaranteeing the privacy of individual records.
arXiv, Big Data, Cynthia Dwork, Data Mining, Differential Privacy, Zachary Lipton
