14 citations · 36 across the 7 of their papers we have counts for
9 papers · 1 filter
A Data Augmentation-based Defense Method Against Adversarial Attacks in Neural Networks
Yi Zeng, Han Qiu, Gerard Memmi +1
Deep Neural Networks (DNNs) in Computer Vision (CV) are well-known to be vulnerable to Adversarial Examples (AEs), namely imperceptible perturbations added maliciously to cause wro…
Mitigating Advanced Adversarial Attacks with More Advanced Gradient Obfuscation Techniques
Han Qiu, Yi Zeng, Qinkai Zheng +3
Deep Neural Networks (DNNs) are well-known to be vulnerable to Adversarial Examples (AEs). A large amount of efforts have been spent to launch and heat the arms race between the at…
Privacy-preserving Health Data Sharing for Medical Cyber-Physical Systems
Han Qiu, Meikang Qiu, Meiqin Liu +1
The recent spades of cyber security attacks have compromised end users' data safety and privacy in Medical Cyber-Physical Systems (MCPS). Traditional standard encryption algorithms…
Asymptotic Performance Analysis of Blockchain Protocols
Antoine Durand, Elyes Ben-Hamida, David Leporini +1
In the light of the recent fame of Blockchain technologies, numerous proposals and projects aiming at better practical viability have emerged. However, formally assessing their par…
Revisiting Shared Data Protection Against Key Exposure
Katarzyna Kapusta, Gerard Memmi, Matthieu Rambaud
This paper puts a new light on secure data storage inside distributed systems. Specifically, it revisits computational secret sharing in a situation where the encryption key is exp…
PE-AONT: Partial Encryption combined with an All-or-Nothing Transform
Katarzyna Kapusta, Gerard Memmi
In this report, we introduce PE-AONT: a novel algorithm for fast and secure data fragmentation. Initial data are fragmented and only a selected subset of the fragments is encrypted…