Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Privacy Amplification by Mixing and Diffusion Mechanisms
Authors: Borja Balle, Gilles Barthe, Marco Gaboardi, Joseph Geumlek
NeurIPS 2019 | Venue PDF | 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. |