Mohamed Maouche

Dr. Mohamed Maouche

Researcher at Inria in Privatics Team

PhD from INSA-Lyon with LIRIS Lab

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About Me

I'm Mohamed Maouche a Research Scientist (permanent position - ISFP) at Inria (Privatics Team). Previously, I was working in the DSVD Chaire in partnership with Renault Group. I also worked on private machine learning for speech processing at Inria in the Magnet Team (MAchine learninG in information NETworks). Before that I receivied my PhD in computer science from INSA-Lyon France in 2019 (working on location privacy).

My main interest is to build machine learning systems that manages a good trade-off between privacy and utility. I explore anonymization techniques and re-identification threats on different data types and applications.

Professional Experience

Inria

Research Scientist - Inria
2022 - présent

Inria permanent position within Privatics Team (ISFP equivalent to a tenured Associate Professor).

Inria

Post-doc - Inria
2021 - 2022

Working on privacy preserving federated learning along side Sonia Ben Mokhtar, Antoine Boutet and Jérémie Decouchant. in the DSVD Chaire in partnership with Renault Group. Focusing on health data in the context of car fleets.

Univ-Lille

Teacher - Université de Lille
2021

Teaching Dimension Reduction for the 1st year students of the machine learning master.

Inria

Post-doc - Inria
2019 - 2021

Working on private machine learning for speech processing along side Aurélien Bellet, Marc Tommasi and Emmanuel Vincent.

INSA-Lyon

Teacher - INSA-Lyon
2016 - 2019

Teaches computer science in the computer science department of INSA-Lyon (Dept. IF) and in the first cycle department (Dept PC).

INSA-Lyon

PhD Student - INSA-Lyon with LIRIS Lab
2016 - 2019

In the fields of Data Science, Security and Privacy. Working on Location Privacy and more precisely on re-identification attacks and obfuscation techniques.

INSA-Lyon

Research Intern - Université de Technologie de Compiègne - UTC with Heudiasyc Lab
January 2016 - June 2016

In the field of Optimization in Operations research. Working on the Vehicle Routing Problem (VRP) and the Robust VRP with Time windows constraints.

CDTA

Intern - Centre de Développement des Technologies Avancées of Algiers - CDTA
June 2014 - August 2014

Study of a formal method designed by a research team in the University of Queensland (Australia) which purpose was to transform a BPMN model into a Petri network.

Publications

GRANITE: a Byzantine-Resilient Dynamic Gossip Learning Framework.
Preprint
Y. Belal, S. Ben Mokhtar, M. Maouche, A. Simonet-Boulogne.

Secure Federated Graph-Filtering for Recommender Systems.
Preprint
J Nicolas, C Sabater, M Maouche, S Ben Mokhtar, M Coates.

Differentially Private and Decentralized Randomized Power Method.
Preprint
J. Nicolas, C. Sabater, M. Maouche, S. Ben Mokhtar, M. Coates.

Inferring Communities of Interest in Collaborative Learning-based Recommender Systems.
ICDCS 25
Y. Belal, S. Ben Mokhtar, M. Maouche, A. Simonet-Boulogne.

Scrutinizing the Vulnerability of Decentralized Learning to Membership Inference Attacks.
Preprint
O Touat, J Brunon, Y Belal, J Nicolas, M Maouche, C Sabater, S Ben Mokhtar.

Synthetic Data: Generate Avatar Data on Demand.
WISE 24
T Lebrun, L Béziaud, T Allard, A Boutet, S Gambs, M Maouche.

Towards an evolution in the characterization of the risk of re-identification of medical images.
Big Data 23
A Boutet, C Frindel, M Maouche.

Enhancing Speech Privacy with Slicing.
INTERSPEECH 22
M. Maouche, B. Srivastava, N. Vauquier, A. Bellet, M. Tommasi, E. Vincent.

Privacy and utility of x-vector based speaker anonymization.
Transactions on Audio, Speech and Language Processing 2022
B. Srivastava, M. Maouche, Md. Sahidullah, E. Vincent, A. Bellet, M. Tommasi, N. Tomashenko, X. Wang, E. Vincent, J. Yamagishi.

Differentially Private Speaker Anonymization.
PETS 2023
A. Shamsabadi, B. Srivastava, A. Bellet, N. Vauquier, E. Vincent, M. Maouche, M. Tommasi, N. Papernot.

The VoicePrivacy 2020 Challenge: Results and findings.
Computer Speech and Language 2022
N. Tomashenko, X. Wang, E. Vincent, J. Patino, B. Srivastava, PG. Noé, A. Nautsch, N. Evans, J. Yamagishi, B. O'Brien, A. Chanclu, JF. Bonastre, M. Todisco, M. Maouche.

A comparative study of speech anonymization metrics.
INTERSPEECH 2020
M. Maouche, B. Srivastava, N. Vauquier, A. Bellet, M. Tommasi, E. Vincent.

Design Choices for X-vector Based Speaker Anonymization.
INTERSPEECH 2020
B. Srivastava, N. Tomashenko, X. Wang, E. Vincent, J. Yamagishi, M. Maouche, A. Bellet, M. Tommasi.

MooD: MObility Data Privacy as Orphan Disease -Experimentation and Deployment Paper.
Middleware 2019
B. Khalfoun, M. Maouche, S. Ben Mokhtar and S. Bouchenak.

HMC: Robust Privacy Protection of Mobility Data against Multiple Re-Identification Attacks.
IMWUT/Ubicomp 2018
M. Maouche, S. Ben Mokhtar and S. Bouchenak.

ACCIO: How to Make Location Privacy Experimentation Open and Easy.
ICDCS 2018
V. Primault, M. Maouche, A. Boutet, S. Ben Mokhtar, S. Bouchenak, L. Brunie.

AP-Attack: A novel Re-identification Attack On Mobility Datasets.
MobiQuitous 2017
M. Maouche, S. Ben Mokhtar and S. Bouchenak.