Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revision Previous revision
Next revision
Previous revision
education [2023/05/09 15:05]
fablpd
education [2023/10/23 14:48]
fablpd
Line 8: Line 8:
 \\ \\
  
-  * [[education/​ca_2021|Concurrent Algorithms]] (theory & practice) +  * [[education/​ca_2023|Concurrent Algorithms]] (theory & practice) 
-  * [[education/​da|Distributed Algorithms]] (theory & practice)+  * [[education/​da_2023|Distributed Algorithms]] (theory & practice)
 \\ \\
 The lab taught in the past the following courses: The lab taught in the past the following courses:
Line 32: Line 32:
   * **Benchmark to certify Byzantine-robustness in ML**: Context: Multiple attacks have been proposed to instantiate a Byzantine adversary in distributed ML [ [[https://​proceedings.neurips.cc/​paper/​2019/​hash/​ec1c59141046cd1866bbbcdfb6ae31d4-Abstract.html|1]],​ [[https://​proceedings.mlr.press/​v115/​xie20a.html|2]] ]. While these attacks have been successful against known defenses, it remains unknown whether stronger attacks exist. As such, a strong benchmark is needed, to go beyond the cat-and-mouse game illustrating the existing research. Ideally, similar to other ML subfields such as privacy-preserving ML or adversarial examples, the desired benchmark should guarantee that no stronger attack exists. Goal: Develop a strong benchmark for attacks in Byzantine ML. Contact [[https://​people.epfl.ch/​youssef.allouah?​lang=en|Youssef Allouah]] for more information.   * **Benchmark to certify Byzantine-robustness in ML**: Context: Multiple attacks have been proposed to instantiate a Byzantine adversary in distributed ML [ [[https://​proceedings.neurips.cc/​paper/​2019/​hash/​ec1c59141046cd1866bbbcdfb6ae31d4-Abstract.html|1]],​ [[https://​proceedings.mlr.press/​v115/​xie20a.html|2]] ]. While these attacks have been successful against known defenses, it remains unknown whether stronger attacks exist. As such, a strong benchmark is needed, to go beyond the cat-and-mouse game illustrating the existing research. Ideally, similar to other ML subfields such as privacy-preserving ML or adversarial examples, the desired benchmark should guarantee that no stronger attack exists. Goal: Develop a strong benchmark for attacks in Byzantine ML. Contact [[https://​people.epfl.ch/​youssef.allouah?​lang=en|Youssef Allouah]] for more information.
  
- 
-  * **Proof systems for Byzantine systems**: Cryptographic proof systems enable the rapid verification of computation between mutually distrustful parties. Recent advances in proof systems include (1) recursive proofs, transition proofs and accumulators which are of prime interest to shrink long chains of computation and/or their associated storage, and (2) zero-knowledge scalable proofs useful for privacy-preserving systems. Motivated by cryptocurrencies,​ the goal of this project is to devise and implement Byzantine-resilient systems that incorporate new cryptographic proof systems for efficiency and/or privacy. Contact Pierre-Louis Roman <​pierre-louis.roman@epfl.ch>​ for more information. 
- 
-  * **Hybrid ordering for cryptocurrencies**:​ Most cryptocurrencies nowadays rely on total order broadcast to maintain a blockchain that represents an agreed-upon log of events. Total order broadcast may be required for some applications,​ such as smart contracts, but the simpler and easy to parallelize reliable broadcast suffices for payments. The goal of this project is to devise and implement Byzantine-resilient broadcast algorithms with hybrid ordering guarantees that only order events when required. Contact Pierre-Louis Roman <​pierre-louis.roman@epfl.ch>​ for more information.