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Found 6 papers, 4 papers with code
Causal Discovery Under Local Privacy
It is based on the application of controlled noise at the interface between the server that stores and processes the data, and the data consumers.
On the Utility Gain of Iterative Bayesian Update for Locally Differentially Private Mechanisms
This paper investigates the utility gain of using Iterative Bayesian Update (IBU) for private discrete distribution estimation using data obfuscated with Locally Differentially Private (LDP) mechanisms.
Differentially Private Multivariate Time Series Forecasting of Aggregated Human Mobility With Deep Learning: Input or Gradient Perturbation?
As shown in the results, differentially private deep learning models trained under gradient or input perturbation achieve nearly the same performance as non-private deep learning models, with loss in performance varying between $0. 57\%$ to $2. 8\%$.
Production of Categorical Data Verifying Differential Privacy: Conception and Applications to Machine Learning
The objective of this thesis is thus two-fold: O$_1$) To improve the utility and privacy in multiple frequency estimates under LDP guarantees, which is fundamental to statistical learning.
RISE Controller Tuning and System Identification Through Machine Learning for Human Lower Limb Rehabilitation via Neuromuscular Electrical Stimulation
Regarding the latter, we introduce the use of past rehabilitation data to build more realistic data-driven identified models.
