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Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

Serge Vaudenay

The maximal achievable advantage of a (computationally unbounded) distinguisher to determine whether a source

Z

is distributed according to distribution

P_0

P_1

, when given access to one sample of

Z

, is characterized by the statistical distance ...

Springer, Cham2020

Fonction de Pearson

Graph Chatbot

Chattez avec Graph Search

Data-Aware Privacy-Preserving Machine Learning

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

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Stochastic vs. sensitivity-based integral parameter and nuclear data adjustments

Existence and Uniqueness of Load-Flow Solutions in Three-Phase Distribution Networks

Pseudorapidity dependence of long-range two-particle correlations in $p$Pb collisions at $\sqrt {s_{NN}}=$ 5.02 TeV

Data-Aware Privacy-Preserving Machine Learning

Robust Biophysical Parameter Estimation with a Neural Network Enhanced Hamiltonian Markov Chain Monte Carlo Sampler

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

Stochastic vs. sensitivity-based integral parameter and nuclear data adjustments

Reconstruction Methods for Cryo-Electron Microscopy: From Model-based to Data-driven

35 years of photovoltaics: Analysis of the TISO-10-kW solar plant, lessons learnt in safety and performance-Part 1

An Architecture for Load Balance in Computer Cluster Applications

Rock You like a Hurricane: Taming Skew in Large Scale Analytics

Existence and Uniqueness of Load-Flow Solutions in Three-Phase Distribution Networks

Pseudorapidity dependence of long-range two-particle correlations in $p$Pb collisions at $\sqrt {s_{NN}}=$ 5.02 TeV