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cs.CR2024
Privately Counting Partially Ordered Data
Matthew Joseph, Mónica Ribero, Alexander Yu
We consider differentially private counting when each data point consists of bits satisfying a partial order. Our main technical contribution is a problem-specific -norm mec…
cs.LG2024
Privacy of the last iterate in cyclically-sampled DP-SGD on nonconvex composite losses
Weiwei Kong, Mónica Ribero
Differentially-private stochastic gradient descent (DP-SGD) is a family of iterative machine learning training algorithms that privatize gradients to generate a sequence of differe…