Phd Position F/m Privacy On-demand and Security

Le descriptif de l’offre ci-dessous est en Anglais_

**Type de contrat **:CDD**Niveau de diplôme exigé **:Bac + 5 ou équivalent**Fonction **:DoctorantA propos du centre ou de la direction fonctionnelle

Le centre Inria d'Université Côte d'Azur regroupe 37 équipes de recherche et 8 services d’appui. Le personnel du centre (500 personnes environ) est composé de scientifiques de différentes nationalités, d’ingénieurs, de techniciens et d’administratifs. Les équipes sont principalement implantées sur les campus universitaires de Sophia Antipolis et Nice ainsi que Montpellier, en lien étroit avec les laboratoires et les établissements de recherche et d'enseignement supérieur (Université Côte d’Azur, CNRS, INRAE, INSERM...), mais aussi avec les acteurs économiques du territoire.

Présent dans les domaines des neurosciences et biologie computationnelles, la science des données et la modélisation, le génie logiciel et la certification, ainsi que la robotique collaborative, le Centre Inria d’Université Côte d’Azur est un acteur majeur en termes d'excellence scientifique par les résultats obtenus et les collaborations tant au niveau européen qu'international.

Mission confiée

**Context**

Future sixth-generation (6G) networks will be highly heterogeneous, with the massive development of mobile edge computing inside networks. Furthermore, 6G is expected to support dynamic network environments and provide diversified intelligent services with stringent Quality of Service (QoS) re
- ternet of everything (IoE). This will challenge wireless networks in the dimensions of delay, energy consumption, interaction, reliability, and degree of intelligence and knowledge, but also in the dimen
- sion of information and data sharing. In turn, 6G networks will be expected about leveraging data at the next step of the new communication system generation. First of all, they will generate large amounts of data much more data than 5G networks: multiple sources as Core, Radio Access Network, OAM, User Equipments (UEs) but also as private and/or personal devices/machines massively con

Reliable and efficient transmission, data privacy and security are great challenges in data sharing. Specially for 5G advanced and 6G networks data is distributed with the wide deployment of various connected Internet of Thing (IoT) devices, and are generated from many distributed network nodes, e.g., end users, small Base Stations or Distributed Units and the network edge. Also, how to col

Recently, generative models have been demonstrated playing a key role in data sharing while pre

**Challenges and objectives**

We will first compare the above-mentioned existing methods for synthetic dataset generation, in terms of their trade-offs on model accuracy, data similarity, communication cost, model compression and privacy. Then, to expose their privacy vulnerability, we will design computational-efficient attacks, for both passive and active adversary cases. Finally, we will design a framework with better trade-off for the task.

**Teams and supervision**
- INRIA : COATI (Frédéric Giroire, Chuan Xu), EPIONE (Marco Lorenzi)
- NOKIA: Bell Labs Core Research
- 3-years PhD to be hosted in Sophia Antipolis. The doctoral student will be supervised by his academic supervisor and his industrial supervisor.

**Skills**

**References**

[AMR+20] Sean Augenstein, H Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, et al. Generative models for effective ml on private, decentralized datasets. ICLR, 2020.

[CYZF20] Dingfan Chen, Ning Yu, Yang Zhang, and Mario Fritz. Gan-leaks: A taxonomy of member
- ship inference attacks against generative models. In Proceedings of the 2020 ACM SIGSAC conference on computer and communications security, pages 343-362, 2020.

[Dwo06] Cynthia Dwork. Differential privacy. In Automata, Languages and Programming: 33rd International Colloquium, ICALP 2006, Venice, Italy, July 10-14, 2006, Proceedings, Part II 33, pages 1-12. Springer, 2006.

[FJR15] Matt Fredrikson, Somesh Jha, and Thomas Ristenpart. Model inversion attacks that exploit confidence information and basic countermeasures. In Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, pages 1322-1333, 2015.

[HMDC19] Jamie Hayes, Luca Melis, George Danezis, and Emiliano De Cristofaro. LOGAN: mem
- bership inference attacks against generative models. Proc. Priv. Enhancing Technol., 2019(1):133-152, 2019.

[KMA+21] Peter Kairouz, H Brendan McMahan, Brendan Avent, Aur ́elien Bellet, Mehdi Bennis, Ar
- jun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cum
- mings, et al. Advances and open problems in federated learning. Foundations and Trends® in Machine Learning, 14(1-2):1-210, 2021.

[VSV+22] Rohit Venugopal, Noman Shafqat, Ishwar Venugopal, Benjamin Mark John Tillbury, Harry Demetrios Stafford, and Aikaterini Bourazeri. Privacy preservi