2022-05366 - Post-Doctoral Research Visit F/M Private and Secure Computation on Personal Data
Le descriptif de l’offre ci-dessous est en Anglais

Type de contrat : CDD

Contrat renouvelable : Oui

Niveau de diplôme exigé : Thèse ou équivalent

Fonction : Post-Doctorant

A propos du centre ou de la direction fonctionnelle

The Inria Rennes - Bretagne Atlantique Centre is one of Inria's eight centres and has more than thirty research teams. The Inria Center is a major and recognized player in the field of digital sciences. It is at the heart of a rich R&D and innovation ecosystem: highly innovative PMEs, large industrial groups, competitiveness clusters, research and higher education players, laboratories of excellence, technological research institute, etc.

Contexte et atouts du poste

The selected applicant will work within the WIDE research team at Inria of the University of Rennes (https://team.inria.fr/wide/). WIDE focuses on large-scale distributed systems and applications, as well as on privacy, and distributed machine learning. 

This postdoctoral position is funded by the SOTERIA H2020 project (https://www.soteria-h2020.eu/) which aims to develop a user-centric, citizen-driven tool to help European citizens to manage their personal data. The project therefore involves close collaboration with French international partners such as AriadNext by IDNow, Idiap Switzerland, COSIC Team @ KU Leuven, a complete list is available at https://www.soteria-h2020.eu/partners/. 

The successful applicant will have the opportunity to travel and collaborate with such international partners. All travel expenses will be covered by Inria within the limits set by French regulations. 

Mission confiée

The recruited postdoctoral researcher is expected to work on the design of a distributed data vault that can (i) store personal data while keeping it safe from third parties, (ii) support computation on encrypted or otherwise protected personal data to obtain aggregate statistics while respecting the privacy of the individual data items, (iii) provide means to remunerate users that enable computation on their personal data.  

In particular, we aim to address this challenge by relying on a byzantine-fault tolerant [1,2] decentralized storage platform that can store data reliably in encrypted form. We plan to combine techniques like multiparty computation [3] and homomorphic encryption [4] with technologies like trusted execution environments [5] to enable computation on such encrypted data. This will allow us to address a variety of use cases, such as decentralized [6] and federated machine learning algorithms [7]. The system should also keep track of the usage of personal data to support remuneration schemes. To this end, we plan to leverage our recent theoretical work on lightweight distributed ledgers [8, 9]. 

The postdoctoral researcher will contribute by performing original research on these topics, and will also participate in the supervision of Masters and PhD students. In doing so, he or she will collaborate with Davide Frey and other members of the WIDE team, as well as with international partners within the SOTERIA project and other related projects. Although teaching is not a requirement, the candidate can also choose to teach relevant courses at the University of Rennes 1 and affiliated institutions. 



[1] Leslie Lamport, Robert Shostak, and Marshall Pease. 1982. The Byzantine Generals Problem. ACM Trans. Program. Lang. Syst. 4, 3 (July 1982), 382–401. https://doi.org/10.1145/357172.357176



[2] Miguel Castro and Barbara Liskov. 1999. Practical Byzantine fault tolerance. In Proceedings of the third symposium on Operating systems design and implementation (OSDI '99). USENIX Association, USA, 173–186.

[3] Ran Canetti, Uri Feige, Oded Goldreich, and Moni Naor. 1996. Adaptively secure multi-party computation. In Proceedings of the twenty-eighth annual ACM symposium on Theory of Computing (STOC '96). Association for Computing Machinery, New York, NY, USA, 639–648. https://doi.org/10.1145/237814.238015


[4] Armknecht, F., Boyd, C., Carr, C., Gjøsteen, K., Jäschke, A., Reuter, C. A. & Strand, M. (2015). A Guide to Fully Homomorphic Encryption.. IACR Cryptology ePrint Archive, 2015, 1192.

[5] PieterMaene, Johannes Götzfried, Ruan de Clercq, TiloMüller, Felix Freiling, and Ingrid Verbauwhede. 2018. Hardware-Based Trusted Computing Architectures for Isolation and Attestation. IEEE transactions on computers.

[6] Valentina Zantedeschi, Aurélien Bellet, Marc Tommasi:
Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs. AISTATS 2020

[7] Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth. 2017. Practical Secure Aggregation for Privacy-Preserving Machine Learning. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (CCS '17). Association for Computing Machinery, New York, NY, USA, 1175–1191. https://doi.org/10.1145/3133956.3133982

[8] Davide Frey, Lucie Guillou, Michel Raynal, François Taïani:
Consensus-Free Ledgers When Operations of Distinct Processes are Commutative. PaCT 2021: 359-370

[9] Timothé Albouy, Davide Frey, Michel Raynal and Francois Taiani.
Good-case Early-Stopping Latency of Synchronous Byzantine Reliable Broadcast: The Deterministic Case. DISC 2022. 


Principales activités

Main Activities

  • Carry out theoretical or practical research on privacy preserving decentralized computation
  • Implement solutions into research prototypes
  • Write papers or research reports 
  • Present their work to team members, project partners and at International conferences
  • Attend project meetings and project review meetings

Additional activities:

  • Teach courses at the University of Rennes
  • Co-Supervise research students 




  • Good collaborative and networking skills, excellent written and oral communication in English
  • Good programming skills
  • Strong analytical skills
  • Interest and ability to learn new topics in distributed computing, privacy, security, and machine learning.
  • Prior knowledge on at least some of the following areas: 
    • Secure Multiparty Computation
    • Trusted Execution Environments (SGX, Trustzone, others) 
    • Machine Learning
    • Federated Learning
    • Distributed Systems
    • Privacy


  • Subsidized meals
  • Partial reimbursement of public transport costs
  • Possibility of teleworking ( 90 days per year) and flexible organization of working hours
  • partial payment of insurance costs


Monthly gross salary amounting to 2746 euros