One problem I've found with differential privacy is that no one talks about how to set \epsilon. I've read this book, and it's quite well written and complete, but as the title says it focuses on the algorithmic foundations.
This paper [1] is much better for practitioners, and actually gives very reasonable values for the privacy guarantee (e.g., (1.2, 1e-9)), and builds on this great paper: [2]. Worth a read if you train neural networks.
This paper [1] is much better for practitioners, and actually gives very reasonable values for the privacy guarantee (e.g., (1.2, 1e-9)), and builds on this great paper: [2]. Worth a read if you train neural networks.
[1]: https://arxiv.org/pdf/1710.06963.pdf [2]: https://arxiv.org/pdf/1607.00133.pdf