Privacy Amplification by Mixing and Diffusion Mechanisms
Authors: Borja Balle, Gilles Barthe, Marco Gaboardi, Joseph Geumlek
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we initiate a systematic study of privacy amplification by stochastic post-processing. Specifically, given a DP mechanism M producing (probabilistic) outputs in X and a Markov operator K defining a stochastic transition between X and Y, we are interested in measuring the privacy of the post-processed mechanism K M producing outputs in Y. |
| Researcher Affiliation | Academia | Borja Balle Gilles Barthe MPI for Security and Privacy IMDEA Software Institute Marco Gaboardi Boston University Joseph Geumlek University of California, San Diego |
| Pseudocode | Yes | Algorithm 1: Noisy Projected Stochastic Gradient Descent. Input: Dataset D = (z1, . . . , zn), loss function ℓ: K D R, learning rate η, noise parameter σ, initial distribution ξ0 P(K) Sample x0 ξ0 for i [n] do xi ΠK (xi 1 η( xℓ(xi 1, zi) + Z)) with Z N(0, σ2I) return xn |
| Open Source Code | No | The paper does not provide any statement about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | No | The paper is a theoretical work focusing on mathematical analysis and proofs of privacy amplification mechanisms. It does not involve empirical training on datasets. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical experiments with dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any empirical experiments that would require specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical analysis; thus, it does not describe any software implementations or list software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup, hyperparameters, or training configurations. |