Context: Machine learning plays a central role in many applications, and the increasing adoption of decentralized solutions, combined with dependability requirements, necessitates that learning tasks be carried out in a decentralized manner. In such settings, nodes in the system can assume multiple roles: performing learning on their local data while also aggregating the computational results of other nodes.

In this context, we aim to achieve confidentiality, ensuring that private local data is not leaked while providing a practical solution.

Objective: The goal of this internship is to explore the combination of Multi-Party Computation (MPC) and Differential Privacy (DP) to assess the feasibility and effectiveness of these approaches in ensuring confidentiality.

Our team has previously conducted an exploratory study on MPC in the Federated Learning context, which provides a strong foundation for studying the integration of Differential Privacy techniques.

The successful candidate will join the Laboratory for Trustworthy, Smart, and Self-Organizing Information Systems (LICIA) at CEA LIST, working in a multicultural, multidisciplinary environment with the opportunity to collaborate with external researchers.

Methodology: The intern will be responsible for the following tasks:

Become familiar with the Differential Privacy principles.
Conduct a state-of-the-art review of Differential Privacy in Federated Learning and its combination with Multi-Party Computation.
Become familiar with the MPC solution developed in the laboratory.
Select a Differential Privacy approach and design a solution to integrate it with the existing MPC solution.
Implement the integrated solution.
Evaluate the performance of the solution.

Requirements:

Background in computer science or a related field, with a focus or strong interest in distributed systems, cryptography, and machine learning.
Programming skills in languages commonly used for cryptographic or machine learning tasks (e.g., Python, C++, or Rust).
Comfortable working in English, both for communication and documentation purposes.