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.
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 our library’s lattice-based primitives in the Cothority framework. Starting from the existing cothority library, the student will extend its interface and internals to support lattice-based primitives in addition to the existing elliptic curve ElGamal implementation.
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.
On the implementation/software side:
- Specify the required changes to the interface and internals of the (core) cothority framework
- Produce a fork of the cothority library implementing those changes
- Provide unit-tests and benchmarks for the relevant functionalities
- Provide a (decent amount of) documentation
On the crypto side:
- Acquire enough understanding of the underlying cryptographic primitives a meaningful design, implementation and optimization
- If taken as a master thesis, this project will include the investigation of which specific lattice-based encryption/signature schemes is to be used
Project type: semester project or master thesis
- Master student in IN or SC
- Strong software engineering skills, and knowledge and use of coding best practices
- Basic knowledge of cryptography (CS-401)
- Interest in security, privacy, cryptography and decentralized systems
Are a plus:
- Knowledge and previous experience with the Go language
- Stronger knowledge in cryptography
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
- 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
 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
 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