Aug 20, 2019 · We propose AdaCliP, a theoretically motivated differentially private SGD algorithm that provably adds less noise compared to the previous methods.

Oct 23, 2019 · Under this framework, we propose AdaCliP, a theoretically-motivated differentially private SGD algorithm that provably adds less noise compared ...

Under this framework, we propose AdaCliP, a theoretically motivated differentially private SGD algorithm that provably adds less noise compared to the previous ...

AdaCliP: Adaptive clipping for private SGD. arXiv:1908.07643. Rajpurkar, P.; Zhang, J.; Lopyrev, K.; and Liang, P. 2016. Squad: 100,000+ questions for ...

AdaCliP: Adaptive Clipping for Private SGD ... Motivated by this, differentially private stochastic gradient descent (SGD) algorithms for training machine ...

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adaclip.py - Implementation of AdaCliP for private Stochastic Gradient Descent (SGD); generate_synth_data.py - Generates synthetic data for private ...

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Differentially Private Stochastic Gradient Descent (DP‐SGD) is a prime method for training machine learning models with rigorous privacy guarantees.