student projects

The lab accepts motivated master’s and bachelor’s students to carry out their master’s thesis research or semester projects in the group. We provide a list of currently open project proposals. Please read through the proposals and contact us if you are interested.

proposed projects

We will soon publish the project proposals for next semester.

ongoing projects

Network layer for lattice-based cryptographic protocols

As a part of our current research in the field of privacy-enhancing technologies and secure multi-party computation, we are implementing a Lattice-based cryptographic library in the Go language.
Lattice-based cryptography is a very hot topic in research thanks to a number of attractive features it provides such as resistance against quantum attacks, algorithmic simplicity and versatility of its constructions.

The collective authority (cothority) project provides a framework for development, analysis, and deployment of decentralized, distributed (cryptographic) protocols. It is developed and maintained by the DEDIS lab at EPFL. It currently supports elliptic curve-based protocols only.

This project consists in the integration of the lattice-based primitives of the Lattigo library in the Onet framework. Starting from the existing Onet library, the student will extend its interface and internals to support lattice-based primitives in addition to the existing elliptic curve ElGamal implementation. This includes the implementation of the NewHope asymmetric encryption scheme and its integration in the Onet authentication mechanism, providing Onet users with post-quantum security.

This project features a close collaboration between LDS and DEDIS, and will permit the student to work together with security, privacy and decentralization researchers on very “hot” application topics.


Björn Gudmundsson


Christian Mouchet

Distributed Privacy-preserving Machine Learning

Statistical and machine-learning analyses require large amounts of data in order to produce meaningful results and are often collected by multiple entities. In many domains such as medicine and user-behavior analysis, these data are personal and sensitive and cannot be shared due to privacy/ethical/legal concerns. In this context, decentralized data-sharing systems [1,2] became key enablers for big-data analysis while protecting individuals’ privacy by distributing the storage and the computation, thus avoiding single points of failure.

This distribution or decentralization of both data and computations can enable analysis on sensitive data, e.g. training of machine learning models on medical data to predict diseases or heart issues. However, the high sensitivity of the data creates multiple challenges, such as how to securely store the data in a decentralized manner and how to compute on these data while maintaining individuals’ privacy.

In this project, the student(s) will tackle these challenges by working on the design, implementation and evaluation of a new solution for privacy-preserving machine learning. 

Type: Semester project and bachelor-/master- thesis

Required skills:

  • Good programming skills (knowledge of Go language is a plus)
  • Familiarity with development tools (e.g. Git) and at ease with reviewing code
  • Some background in security and cryptography
  • Knowledge of homomorphic encryption, secure multiparty computation and/or decentralized databases is a plus

Related work:

[1] David Froelicher, Patricia Egger, João Sá Sousa, Jean Louis Raisaro, Zhicong Huang, Christian Mouchet, Bryan Ford, and Jean-Pierre Hubaux. Unlynx: A Decentralized System for Privacy-Conscious Data Sharing, Privacy Enhancing Technologies Symposium, Minneapolis, MN, USA, July 18–21, 2017.  Details

[2] David Froelicher, Juan R. Troncoso-Pastoriza, João Sá Sousa and Jean-Pierre Hubaux. Drynx: Decentralized, Secure, Verifiable System for Statistical Queries and Machine Learning on Distributed Datasets Details


Point of contact David


completed projects

The list of project completed before January 2019 can be found at