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Portail > Offres > Offre UMR5205-VLANIT-001 - Post-doc on mobile TEEs H/F

Post-doc on ARM TrustZone H/F

This offer is available in the following languages:
Français - Anglais

Date Limite Candidature : vendredi 4 décembre 2020

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General information

Reference : UMR5205-VLANIT-001
Workplace : VILLEURBANNE
Date of publication : Friday, November 13, 2020
Type of Contract : FTC Scientist
Contract Period : 12 months
Expected date of employment : 1 January 2021
Proportion of work : Full time
Remuneration : 2675 euros
Desired level of education : PhD
Experience required : Indifferent

Missions

The IoT vision is characterized by the proliferation of smart connected devices (e.g., smartphones, smart watches and other connected appliances surrounding the users) on top of which run a variety of applications. Examples of such applications include participatory sensing applications where citizens voluntarily use their sensory devices to capture and share sensed data from their surrounding environments to monitor a given phenomenon. In this context, it became a priority to devise mechanisms which allow users to securely access IoT services without fearing that their data will be leaked out from the cloud platforms where it is being stored and processed. A possible solution which is gaining grip in recent years is the principle of "code moving to data". Indeed, data scientists have realized that in many scenarios moving computation close to the data may be cheaper than uploading the data close to the computation, leading to the onset of the Edge Computing paradigm. In the context of Machine Learning (ML), this paradigm was instantiated under the name of Federated Learning (FL) and spearheaded among others by Google. Even if FL claims to revolutionize machine learning and data processing in general, by extracting useful features from the edge-generated data while still providing strong privacy guarantees, its novel architecture exposes two important issues: scalability and robustness against attacks. The purpose of this postdoc is to investigate the potential robustness improvements of the Federated Leaning architecture using the attestation mechanisms of hardware TEEs.

Activities

* research activities towards the improvement of FL robustness
* the implementation of a proof of concept

Skills

* experience with mobile operating systems (e.g. Android) and mobile architectures (e.g. ARM)
* C/C++ programming skils
* the ability to work in an interactive team

Work Context

Unité mixte de recherche (UMR 5205), le Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS) est porté par le CNRS, l'INSA de Lyon, l'Université Claude Bernard Lyon 1, l'Université Lumière Lyon 2 et l'Ecole Centrale de Lyon. Il compte 330 membres, et a pour principal champ scientifique l'Informatique et plus généralement les Sciences et Technologies de l'Information.

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