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PhD Position F/M Security and Enhancement for Next-gen Trusted RAG Yields using Confidential Computing

Inria -
Rennes (35)

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Détails de l'emploi

Avantages

Qualifications

Informatique
Systèmes d'exploitation
Niveau Doctorat
Compétences rédactionnelles
Anglais
Apache Hive
Master
Équipement informatique
Systèmes distribués
Ingénierie informatique

Description complète du poste

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

Type de contrat : CDD

Niveau de diplôme exigé : Bac + 5 ou équivalent

Fonction : 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

This Phd thesis will be funded by the Cupseli Inria Challenge (Défi Inria) and by the ANRT

The Cupseli Inria Challenge brings together 11 Inria teams distributed over 6 Inria centers and the Hive startup company based in Cannes. Hive offers a highly original data storage architecture in which data is stored in a distributed and secure manner on the spare storage resources of participants, based on a peer-to-peer structure. This structure naturally ensures scalability, resilience and voluntary sharing of data between users. Another advantage of this architecture is its positive impact on carbon emissions by using and enhancing existing resources rather than building new datacenters, which have a very high impact.

This new challenge between Hive and Inria will focus on enabling large-scale computation on the Hive infrastructure. This objective is particularly relevant in the presence of applications that are well suited to distributed execution on interconnected computing resources, even if the network is significantly less powerful than that of a supercomputer. Although the execution of highly coupled numerical simulation codes or kernels is probably beyond the capabilities of the Hive platform, the execution of fine-tuning tasks and even the training of large models are within the scope of this challenge.

The thesis will be hosted by the WIDE team located in Rennes and will be co-supervised by membrers of the Hive startup company located in Cannes.

About WIDE

The WIDE team at the Inria center at Rennes University investigates the key fundamental theoretical and practical questions posed by modern distributed computer systems. This involves exploring the inherent tension between scalability and coordination guarantees and developing novel techniques and paradigms that are adapted to the rapid and profound changes impacting today's distributed systems, both in terms of the application domains they support and the operational constraints they must meet.

About Hive

Hive is shaping the future of cloud computing by leveraging unused computing capacity to provide a decentralized, environmentally friendly, and user-empowered alternative to traditional cloud services. By utilizing distributed peer-to-peer networks, end-to-end encryption, and blockchain technologies, Hive aims to establish a more sovereign and efficient cloud ecosystem.

Regular Meetings

The project involves regular meetings with other teams and with colleagues at Hive. Most of the meeings will be online, but some occasional trips to Paris and Cannes are foreseen. The student will also travel to international conferences to present the results of his/her work.

Mission confiée

This PhD Thesis plans to leverage server-side confidential computing devices to secure the processes of training and inference in decentralized machine learning. Operating on distributed data, be it for inference or for training a distributed model holds the promise for more private forms of machine learning. Instead of having a single or a few service providers that collect user data, data can remain on user devices without being collected in large data silos. In spite of this, decentralized learning does not automatically guarantee privacy. Indeed, in a distributed setting any participant becomes a potential attacker.

In this setting, the integration of confidential computing into artificial intelligence (AI) systems is becoming increasingly important [1] as organizations adopt AI for handling sensitive data in sectors like healthcare, finance, and defense. Retrieval-Augmented Generation (RAG) systems, which combine information retrieval with generative models, such as large language models (LLMs), are a popular solution for tasks like document summarization, chatbot development, and recommendation engines. However, they are often deployed in cloud environments, which introduces security risks, including data leakage, intellectual property (IP) theft, and model tampering.

The brand new Intel TDX CPU or AMD SEV-SNP features provide a promising solution by securing RAG systems through the isolation of both AI models and data within trusted execution environments (TEEs), even when deployed in multi-cloud or untrusted cloud infrastructures. Confidential computing features like TDX or SEV-SNP protect sensitive workloads by encrypting data during processing (data-in-use), while also ensuring model privacy and integrity using cryptographic attestation [2,3]

The student will leverage the Wide team's expertise in Operating Systems and Hypervisors, on trusted execution environments [4], and decentralized machine learning [5,6] to design a Trusted Virtual Machine Monitor (VMM) that will manage and secure Intel TDX-enabled Retrieval-Augmented Generation (RAG) systems, addressing key research objectives. Designing a Trusted VMM presents significant challenges due to the stringent requirements for security, performance, and scalability.

A first challenge lies in ensuring that the VMM operates transparently with the guest operating systems (OS). This entails providing essential security services—such as isolation and resource management—without modifying the guest OS or requiring it to be aware of the underlying TDX-aware VMM. Ensuring this transparency is critical for compatibility with existing systems and workloads, as modifying the guest OS would introduce substantial overhead and deployment complexity.

A second challenge results from the fact that not all available hardware will be equipped with confidential-computing devices. This results from the presence of non-trusted or incompatible server-side devices. To this end, we plan to leverage clustering approaches that can make it possible to leverage groups of devices that can communicate with each other. However, naive clustering would make the system very vulnerable to attacks. As a result, we plan to leverage a hybrid solution in which the data managed by a model is split between a protected and an unprotected part, leading to an inference or training process that is split between trusted and non trusted devices.

Principales activités

The student will work on the design of a Trusted Virtual Machine Monitor (VMM).

To this end, we envision the following high-level work plan.

M0-M3: The PhD student will perform a thorough state-of-the-art of confidential computing, virtual machines and machine-learning workloads.

M4-M12: The student will work on developing novel algorithms and techniques to address the first challenged outlined above, namely, ensuring transparency for the operation of the VMM with respect to the guest operating system. This will involve developing techniques for isolation and resource management that leverage confidential-computing hardware.

M13-M24: They will then explore hybrid approaches that make it possible to combine confidential-computing devices with non-trusted or incompatible ones.

M25-M30: The student will work on combining the results of the first two projects into a scalable and reusable Virtual Machine Monitor and will test its application to machine-learning workloads such as Retrieval-Augmented Generation (RAG) systems.

M31-M36: The final months will be devoted to writing the manuscript and on finalizing the publications of the thesis results.

Compétences

Excellent programming skills and a willingness to learn about new techniques (confidential computing, machine learning, low-level programming) are also crucial, as well as good writing skills and the ability to propose, present, and discuss new ideas in a collaborative setting.

Avantages

Subsidized meals
Partial reimbursement of public transport costs
Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
Possibility of teleworking (after 6 months of employment)
Social, cultural and sports events and activities
Access to vocational training

Rémunération

2 300€ per month

Informations générales

Thème/Domaine : Systèmes distribués et intergiciels
Ville : Rennes
Centre Inria : Centre Inria de l'Université de Rennes
Date de prise de fonction souhaitée : 2026-09-01
Durée de contrat : 3 ans
Date limite pour postuler : 2026-08-31

Attention: Les candidatures doivent être déposées en ligne sur le site Inria. Le traitement des candidatures adressées par d'autres canaux n'est pas garanti.

Consignes pour postuler

Please submit your CV, cover letter, and any recommandations online

Sécurité défense :
Ce poste est susceptible d’être affecté dans une zone à régime restrictif (ZRR), telle que définie dans le décret n°2011-1425 relatif à la protection du potentiel scientifique et technique de la nation (PPST). L’autorisation d’accès à une zone est délivrée par le chef d’établissement, après avis ministériel favorable, tel que défini dans l’arrêté du 03 juillet 2012, relatif à la PPST. Un avis ministériel défavorable pour un poste affecté dans une ZRR aurait pour conséquence l’annulation du recrutement.

Politique de recrutement :
Dans le cadre de sa politique diversité, tous les postes Inria sont accessibles aux personnes en situation de handicap.

Contacts

Équipe Inria : WIDE
Directeur de thèse :
Frey Davide / [email protected]

L'essentiel pour réussir

The candidate recruited for this Ph.D. should have a Master’s Degree in Computer Science or equivalent, with a solid background in distributed and operating systems. Knowledge and expertise in confidential-computing hardware, and/or machine learning is a plus.

A propos d'Inria

Inria est l’institut national de recherche dédié aux sciences et technologies du numérique. Il emploie 2600 personnes. Ses 215 équipes-projets agiles, en général communes avec des partenaires académiques, impliquent plus de 3900 scientifiques pour relever les défis du numérique, souvent à l’interface d’autres disciplines. L’institut fait appel à de nombreux talents dans plus d’une quarantaine de métiers différents. 900 personnels d’appui à la recherche et à l’innovation contribuent à faire émerger et grandir des projets scientifiques ou entrepreneuriaux qui impactent le monde. Inria travaille avec de nombreuses entreprises et a accompagné la création de plus de 200 start-up. L'institut s'eﬀorce ainsi de répondre aux enjeux de la transformation numérique de la science, de la société et de l'économie.

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