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Found 10 papers, 5 papers with code
Accelerating Differentially Private Federated Learning via Adaptive Extrapolation
Most of the existing methods to accelerate DP-FL require 1) additional hyperparameters or 2) additional computational cost for clients, which is not desirable since 1) hyperparameter tuning is computationally expensive and data-dependent choice of hyperparameters raises the risk of privacy leakage, and 2) clients are often resource-constrained.
Scaling Private Deep Learning with Low-Rank and Sparse Gradients
Applying Differentially Private Stochastic Gradient Descent (DPSGD) to training modern, large-scale neural networks such as transformer-based models is a challenging task, as the magnitude of noise added to the gradients at each iteration scales with model dimension, hindering the learning capability significantly.
Shuffle Gaussian Mechanism for Differential Privacy
We study Gaussian mechanism in the shuffle model of differential privacy (DP).
Privacy Amplification via Shuffled Check-Ins
We study a protocol for distributed computation called shuffled check-in, which achieves strong privacy guarantees without requiring any further trust assumptions beyond a trusted shuffler.
Network Shuffling: Privacy Amplification via Random Walks
However, introducing a centralized entity to the originally local privacy model loses some appeals of not having any centralized entity as in local differential privacy.
Universal Adversarial Spoofing Attacks against Face Recognition
We assess the vulnerabilities of deep face recognition systems for images that falsify/spoof multiple identities simultaneously.
PEARL: Data Synthesis via Private Embeddings and Adversarial Reconstruction Learning
We propose a new framework of synthesizing data using deep generative models in a differentially private manner.
Advances in Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider
Between the years 2015 and 2019, members of the Horizon 2020-funded Innovative Training Network named "AMVA4NewPhysics" studied the customization and application of advanced multivariate analysis methods and statistical learning tools to high-energy physics problems, as well as developed entirely new ones.
FaceLeaks: Inference Attacks against Transfer Learning Models via Black-box Queries
We investigate if one can leak or infer such private information without interacting with the teacher model directly.
Dark Matter Benchmark Models for Early LHC Run-2 Searches: Report of the ATLAS/CMS Dark Matter Forum
This document is the final report of the ATLAS-CMS Dark Matter Forum, a forum organized by the ATLAS and CMS collaborations with the participation of experts on theories of Dark Matter, to select a minimal basis set of dark matter simplified models that should support the design of the early LHC Run-2 searches.
High Energy Physics - Experiment High Energy Physics - Phenomenology
