An automatic differentiation system for the age of differential privacy. (arXiv:2109.10573v2 [cs.LG] UPDATED)

We introduce Tritium, an automatic differentiation-based sensitivity analysis
framework for differentially private (DP) machine learning (ML). Optimal noise
calibration in this setting requires efficient Jacobian matrix computations and
tight bounds on the L2-sensitivity. Our framework achieves these objectives by
relying on a functional analysis-based method for sensitivity tracking, which
we briefly outline. This approach interoperates naturally and seamlessly with
static graph-based automatic differentiation, which enables order-of-magnitude
improvements in compilation times compared to previous work. Moreover, we
demonstrate that optimising the …

age differential privacy lg privacy system