Author: Laurédane Bonnet

PhD defense Thomas Lebrun : “Health Data: Exploring Emerging Privacy Enhancing Mechanisms”

The defense will take place the 5th december at 9 AM at the library Marie-Curie INSA-Lyon

Title
Health Data: Exploring Emerging Privacy Enhancing Mechanisms

Abstract
Health data represents a large volume of information, generated daily and sensitive by nature. However, sharing this data is essential for advancing research and, ultimately, improving patient care. The use of medical data faces limitations due to its sensitivity and the need to ensure confidentiality, which is governed by current regulations. This
necessitates enhanced protection. Interest in alternatives to sharing raw data, such as pseudonymization or anonymization, is increasing alongside the growing need for access to training data for the use of artificial intelligence, which requires large amounts of data to function effectively as a medical assistant.

In this thesis, we explore new privacy-preserving mechanism made possible by the rapid advancements in artificial intelligence. More specifically, my analysis focuses on improving alternatives to the centralization of sensitive data: federated learning, a decentralized method of training artificial intelligence models that do not need sensitive data sharing, as well as synthetic data generation, which creates artificial data similar statistical properties to real data.
Given the lack of consensus on evaluating the privacy of these new approaches, our work focuses on the systematic measurement of privacy leakage and the balance with the utility of synthetic data or the federated learning model. My contributions include a mechanism to enhance the privacy properties of federated learning, as well as a new method for conditional synthetic data generation. This thesis aims to contribute to the development of more robust frameworks for the secure sharing of health data, in compliance with regulatory requirements, thereby facilitating innovations in healthcare.

Jury
* Sonia BEN MOKHTAR, Directrice de Recherche, CNRS/INSA-Lyon, Examiner,
*Szilvia LESTYAN, Docteure-Ingénieure de Recherche, INRIA, Examiner,
* Jérémie DECOUCHANT, Professeur des universités, Université de Delft, Examiner,
* Benjamin NGUYEN, Professeur des universités, INSA-CVL,Thesis Reviewer,
* Emmanuel VINCENT, Directeur de Recherche, INRIA,Thesis Reviewer,


PhD defense Zhiyi Zhang : “Deployment of mobile base stations in cellular networks”

The defense will take place on January 10th, 2025 at 9am in Amphi Est, Humanités Building, INSA Lyon

Title
Deployment of mobile base stations in cellular networks

Abstract
In current commercial mobile networks, we use fixed base stations (FBS) to provide services to users. However, the first step for deploying fixed base stations requires detailed studies to determine the architecture, the location of the base stations, the capacity, their configurations, etc. With the miniaturization of electronic equipment and network functions virtualization, it is now possible to attempt to embed base stations on movable platforms (e.g., drones) that are able to adjust their position when necessary.

For the ANR DEMON project that funds this thesis, the goal is to establish an adaptive mobile network with movable base stations (MBS). The MBS are thus capable of repositioning themselves to adapt to network changes in real-time. First, we need to understand what the use of MBS in mobile networks can bring to users and for telecommunication operator. Therefore, this thesis explores the advantages and limitations of using MBS in mobile networks. We progressively study the use of MBS in three scenarios: a post-disaster emergency area, an urban neighborhood, and an entire city.

In this thesis, we will show that, MBS can adjust their position in real-time based on the mobility of the users and also considering the user demand, MBS can often outperform FBS in terms of performance, with less base stations deployed. For example, when users form a group, if the MBS and FBS have the same transmit power, the MBS can quadruple the user throughput. In addition, we will highlight when the transmit power of MBS is 50 dBm lower than that of FBS, the performance can be comparable.
Traffic demand in mobile networks is evolving in time and space, when using FBS, we need to respond to the highest demand.
Thanks to the flexibility and mobility of MBS, they can be self-deployed only when needed, which reduces costs by about 20%. The use of MBS allows the concept of self-deployable network to become a reality.

Jury
* André-Luc Beylot, Professeur des Universités (ENSEEIHT), Rapporteur
* Véronique Vèque, Professeure des Universités (Université Paris Saclay), Rapporteure
* Walid Dabbous, Directeur de Recherche (Centre Inria d’Université Côte d’Azur), Examinateur
* Xavier Lagrange, Professeur des Universités (IMT Atlantique), Examinateur
* Razvan Stanica, Maître de Conférences HDR (INSA Lyon), Directeur de thèse
* Fabrice Valois, Professeur des Universités (INSA Lyon), Co-Directeur de thèse


HDR defense Antoine Boutet : “Privacy issues in AI and geolocation: from data protection to user awareness”

The defense will take place on december 10th at 1:30 PM.

Title
Privacy issues in AI and geolocation: from data protection to user awareness

Abstract
The evolution of digital technologies and their increasing adoption have opened major opportunities, highly beneficial for society in general and for individuals in particular. However, it also poses considerable threats to privacy that require appropriate legal and ethical rules. Privacy is essential to protect individuals, for example against possible misuse of personal data. Privacy is also essential to protect society, as shown by the misuse of personal data to influence voters
during elections (e.g., Cambridge Analytica).
In this context of ultra-rapid development of technologies (often deployed before being regulated), my research work is focused on privacy protection. More precisely, I mainly contribute to the field by proposing technical solutions to privacy (by quantifying risks or proposing countermeasures for example), and also through transdisciplinary activities. Indeed, privacy issues cannot be solved by technology alone because they also raise legal, ethical, economic and societal questions that require a dialogue with people from different disciplines.
My main contributions cover 1) issues related to the collection, exploitation and protection of location data, and more recently 2) security and confidentiality of AI. In this second axis, I focused on “privacy considerations in ML”, i.e., the identification of risks related to ML technologies and countermeasures, and “exploiting ML for confidentiality”, using the capabilities of these new tools to protect individuals (with the use of language models for the anonymization of
medical reports for example).
To address these growing privacy issues, it is necessary to quantify the new risks fueled by new technologies and new usages, and to improve the safeguarding of users’ personal information by developing protection mechanisms. Finally, it is also necessary to both raise awareness among end users about the different risks in order to enable them to adapt
their use, and to collaborate with key players in the field to adopt best practices.

Jury
* Pr. Anne-Marie Kermarrec, EPFL
* Pr. Romain Rouvoy, Université de Lille (rapporteur)
* Dr. Aurélien Bellet, Inria (rapporteur)
* Dr. Catusci Palamidessi, Inria (rapporteuse)
* Pr. François Taiani, Université de Rennes 1
* Pr. Sébastien Monnet, Université Savoie Mont-Blanc
* Pr. Eddy Caron, Université Lyon 1
* Dr. Sonia Ben Mokhtar, CNRS, Insa-Lyon


Talk Josiane Kouam : september 13th at 2 PM

Josiane Kouam will give a talk in the meeting room at the 4th floor of the Inria building.

Title
Mobile Human Behavior: Availability, Leveraging, and Addressing Privacy Attacks

Abstract
In an era of ubiquitous mobile connectivity, the security and privacy of users have become paramount concerns. My research delves into the intricate balance between harnessing mobile human behavior data for network optimization and security while safeguarding individual privacy from emerging threats. This talk will cover three key facets of my work in mobile security and privacy, offering insights into the opportunities and challenges presented by the pervasive nature of mobile technologies.
First, I will overview the importance and challenge of ensuring privacy-preserving access to such datasets. Next, I will discuss how behavioral data can be leveraged to enhance network security, including the development of a game-theoretic approach to mitigate international bypass frauds in cellular networks. Finally, I will address privacy risks linked to mobile behavioral data, focusing on vulnerabilities in permission-less sensors like accelerometers when combined with publicly available distinctive network properties.
This discussion will provide insights into balancing the benefits and risks of mobile data in an increasingly connected world.

Biography
Anne Josiane Kouam is a post-doctoral researcher at TU Berlin and member of the Machine Learning and Security team. She obtained her PhD from INRIA Saclay and Ecole Polytechnique, France, in May 2023. With a focus on privacy and security in Mobile and Cellular networks, her research investigates the evolving landscape of threats, particularly those arising from the intersection of Machine Learning and network security.


Chroma project team at the RoboCup ; an international robotics competition

The RoboCup, an international robotics competition, will be held from July 17 to 21 in the Netherlands. The LyonTech team (CPE, INSA Lyon, Inria, PaloIT), some members of which are part of the Chroma project team, will participate in the @Home category (in the Open Platform league). Their goal: to design a companion robot capable of assisting humans in the daily challenges of the home. It is one of the world’s most important technological events for robotics research and education.

Good luck to the team!

More information : https://robocup-lyontech.github.io/opl/