624 results
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On MILP modelisations
Speaker : Christina Boura (UVSQ, CNRS, LMV)
Modelizing a problem using linear constraints and solving it by some Mixed Integer Linear Programming (MILP) solver is a popular approach in many domains of computer science. In this talk we present and compare different new techniques to modelize any subset of {0,1}^n for MILP. We then discuss the efficiency of our models by applying them to the search of differential paths, a classical problem[…] -
Traceable Constant-Size Multi-Authority Credentials
Speaker : Chloé Hébant - ENS
Many attribute-based anonymous credential (ABC) schemes have been proposed allowing a user to prove the possession of some attributes, anonymously. They became more and more practical with, for the most recent papers, a constant-size credential to show a subset of attributes issued by a unique credential issuer. However, proving possession of attributes coming from K different credential issuers[…] -
Bridging Deep Learning and Classical Profiled Side-Channel Attacks
Speaker : Gabriel Zaid
Over the recent years, the cryptanalysis community leveraged the potential of research on Deep Learning to enhance attacks. In particular, several studies have recently highlighted the benefits of Deep Learning based Side-Channel Attacks (DLSCA) to target real-world cryptographic implementations. While this new research area on applied cryptography provides impressive result to recover a secret[…] -
Constant time implementation of rank based cryptography
Speaker : Nicolas Aragon - IRISA
Since the start of the NIST standardization project for post-quantum cryptography in 2017, rank metric based cryptography is becoming more popular as an alternative to code-based cryptography in the Hamming metric.<br/> While rank based cryptography has always been competitive in terms of keys and ciphertexts sizes, the lack of maturity in the implementations of these cryptosystems made them[…] -
Built on sand: on the security of Collaborative Machine Learning
Speaker : Dario Pasquini (EPFL)
This talk is about inaccurate assumptions, unrealistic trust models, and flawed methodologies affecting current collaborative machine learning techniques. In the presentation, we cover different security issues concerning both emerging approaches and well-established solutions in privacy-preserving collaborative machine learning. We start by discussing the inherent insecurity of Split Learning and[…] -
Public Key Encryption with Flexible Pattern Matching
Speaker : Elie Bouscatié - Orange
Many interesting applications of pattern matching (e.g. deep-packet inspection or medical data analysis) target very sensitive data. In particular, spotting illegal behaviour in internet traffic conflicts with legitimate privacy requirements, which usually forces users to blindly trust an entity that fully decrypts their traffic in the name of security. The compromise between traffic analysis and[…]