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At CRYPTO 2017, Rosca et al. introduce a new variant of the Learning With Errors (LWE) problem, called the Middle-Product LWE (MP-LWE). The hardness of this new assumption is based on the hardness of ...
The adoption of deep neural networks for profiled side-channel attacks provides different capabilities for leakage detection of secure products. Research papers provide a variety of arguments with res...
Machine learning is nowadays supplanting or extending human expertise in many domains ranging from board games to text translation. Correspondingly, the use of such tools is also on the rise in comput...
This article, for the first time, demonstrates Cross-device Deep Learning Side-Channel Attack (X-DeepSCA), achieving an accuracy of >99.9%>99.9%, even in presence of significantly higher inter-device ...
Physical Unclonable Functions (PUFs) and, in particular, XOR Arbiter PUFs have gained much research interest as an authentication mechanism for embedded systems. One of the biggest problems of (strong...
We present a framework for the study of a learning problem over abstract groups, and introduce a new technique which allows for public-key encryption using generic groups. We proved, however, that in ...
The Learning with Errors (LWE) problem is the fundamental backbone of modern lattice based cryptography, allowing one to establish cryptography on the hardness of well-studied computational problems. ...
Deep learning side-channel attacks are an emerging threat to the security of implementations of cryptographic algorithms. The attacker first trains a model on a large set of side-channel traces captur...
At CRYPTO 2018 Cramer et al. presented SPDZ2k, a new secret-sharing based protocol for actively secure multi-party computation against a dishonest majority, that works over rings instead of fields. Th...
Convolutional neural networks (CNNs) is a category of deep neural networks that are primarily used for classifying image data. Yet, their continuous gain in popularity poses important privacy concerns...
A recent line of research has investigated a new profiling technique based on deep learning as an alternative to the well-known template attack. The advantage of this new profiling approach is twofold...
Robustness to modeling attacks is an important requirement for PUF circuits. Several reported Arbiter PUF com- positions have resisted modeling attacks. and often require huge computational resources ...
Machine learning techniques represent a powerful option in profiling side-channel analysis. Still, there are many settings where their performance is far from expected. In such occasions, it is very i...
In recent years, deep learning has become an attractive ingredient to side-channel analysis (SCA) due to its potential to improve the success probability or enhance the performance of certain frequent...
In Side Channel Analysis, masking is known to be a reliable and robust counter-measure. Recently, several papers have focused on the application of the Deep Learning (DL) theory to improve the efficie...

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