DESCRIPTION :
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.
Code d'emploi : Thésard (h/f)
Niveau de formation : Bac+5
Temps partiel / Temps plein : Plein temps
Type de contrat : Contrat à durée indéterminée (CDI)
Compétences : Intelligence Artificielle, Big Data, Cloud Computing, Simulation Informatique, Programmation Informatique, Prévention des Fuites d'Information, Base de Données Distribuée, Informatique Distribuée, Apache Hive, Hyperviseur, Information Retrieval, Machine Learning, Peer-To-Peer, KS1LZHDEF56H9FUSRPTQ, Blockchain, Machines Virtuelles, Technologies de Stockage de Données, Large Language Models, Technologies Informatiques, Système d'Opérations, Sens de la Communication, Persévérance, Convivialité, Curiosité, Algorithmes, Architecture, Cryptographie, Secteur Financier, Soins de Santé, Scalabilité, Gestion des Infrastructures, Propriété Intellectuelle, KS13QGAHYN4TSDRJGLWZ, Gestion des Ressources, Architecture Matérielle, Applications des Règles et Consignes de Sécurité
Courriel :
davide.frey@inria.fr
Téléphone :
0299847100
Type d'annonceur : Employeur direct
