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Researcher profile

Denis Renaud

Denis Renaud contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Cryptography and SecurityArtificial Intelligencehep-exMachine Learningphysics.ins-det

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Published work

3 published item(s)

preprint2026arXiv

diffGHOST: Diffusion based Generative Hedged Oblivious Synthetic Trajectories

Trajectories are nowadays valuable information for a wide range of applications. However they are also inherently sensitive, as they contain highly personal information about individuals. Facing this challenge, synthesizing mobility trajectories has emerged as a promising solution to leverage mobility information while preserving privacy. State-of-the-art models, often rely on the false assumptions of generative models implicit privacy and fails to provide privacy guarantees while preserving trajectories utility. Here, we introduce diffGHOST, a conditional diffusion model based on latent space segmentation, designed to answer this challenge. Thus, this paper propose a methodology that identify and mitigate memorization of critical samples using condition segments of a learn latent space.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Differentially Private Multivariate Time Series Forecasting of Aggregated Human Mobility With Deep Learning: Input or Gradient Perturbation?

This paper investigates the problem of forecasting multivariate aggregated human mobility while preserving the privacy of the individuals concerned. Differential privacy, a state-of-the-art formal notion, has been used as the privacy guarantee in two different and independent steps when training deep learning models. On one hand, we considered \textit{gradient perturbation}, which uses the differentially private stochastic gradient descent algorithm to guarantee the privacy of each time series sample in the learning stage. On the other hand, we considered \textit{input perturbation}, which adds differential privacy guarantees in each sample of the series before applying any learning. We compared four state-of-the-art recurrent neural networks: Long Short-Term Memory, Gated Recurrent Unit, and their Bidirectional architectures, i.e., Bidirectional-LSTM and Bidirectional-GRU. Extensive experiments were conducted with a real-world multivariate mobility dataset, which we published openly along with this paper. 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 $2.8\%$. The contribution of this paper is significant for those involved in urban planning and decision-making, providing a solution to the human mobility multivariate forecast problem through differentially private deep learning models.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Microchannel cooling for the LHCb VELO Upgrade I

The LHCb VELO Upgrade I, currently being installed for the 2022 start of LHC Run 3, uses silicon microchannel coolers with internally circulating bi-phase \cotwo for thermal control of hybrid pixel modules operating in vacuum. This is the largest scale application of this technology to date. Production of the microchannel coolers was completed in July 2019 and the assembly into cooling structures was completed in September 2021. This paper describes the R\&D path supporting the microchannel production and assembly and the motivation for the design choices. The microchannel coolers have excellent thermal peformance, low and uniform mass, no thermal expansion mismatch with the ASICs and are radiation hard. The fluidic and thermal performance is presented.

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