Oblivious pseudorandom function (OPRF) is a two-party cryptographic protocol that allows the receiver to input $x$ and learn $F(x)$ for some PRF $F$, only known to the sender. For private set intersection (PSI) applications, OPRF protocols have evolved to enhance efficiency, primarily using symmetric key cryptography. Current state-of-the-art protocols, such as those by Rindal and Schoppmann (Eurocrypt '21), leverage vector oblivious linear evaluation (VOLE) and oblivious key-value store...

Deep learning-based profiling side-channel analysis has gained widespread adoption in academia and industry due to its ability to uncover secrets protected by countermeasures. However, to exploit this capability, an adversary must have access to a clone of the targeted device to obtain profiling measurements and know secret information to label these measurements. Non-profiling attacks avoid these constraints by not relying on secret information for labeled data. Instead, they attempt all...

Privacy-Preserving Machine Learning (PPML) provides protocols for learning and statistical analysis of data that may be distributed amongst multiple data owners (e.g., hospitals that own proprietary healthcare data), while preserving data privacy. The PPML literature includes protocols for various learning methods, including ridge regression. Ridge regression controls the $L_2$ norm of the model, but does not aim to strictly reduce the number of non-zero coefficients, namely the $L_0$ norm...

Despite considerable achievements of deep learning-based side-channel analysis, overfitting represents a significant obstacle in finding optimized neural network models. This issue is not unique to the side-channel domain. Regularization techniques are popular solutions to overfitting and have long been used in various domains. At the same time, the works in the side-channel domain show sporadic utilization of regularization techniques. What is more, no systematic study investigates these...

Profiling side-channel analysis allows evaluators to estimate the worst-case security of a target. When security evaluations relax the assumptions about the adversary's knowledge, profiling models may easily be sub-optimal due to the inability to extract the most informative points of interest from the side-channel measurements. When used for profiling attacks, deep neural networks can learn strong models without feature selection with the drawback of expensive hyperparameter tuning....

Cybersecurity has become one of the focuses of organisations. The number of cyberattacks keeps increasing as Internet usage continues to grow. An intrusion detection system (IDS) is an alarm system that helps to detect cyberattacks. As new types of cyberattacks continue to emerge, researchers focus on developing machine learning (ML)-based IDS to detect zero-day attacks. Researchers usually remove some or all attack samples from the training dataset and only include them in the testing...

The adoption of deep neural networks for profiling side-channel attacks (SCA) opened new perspectives for leakage detection. Recent publications showed that cryptographic implementations featuring different countermeasures could be broken without feature selection or trace preprocessing. This success comes with a high price: extensive hyperparameter search to find optimal deep learning models. As deep learning models usually suffer from overfitting due to their high fitting capacity, it is...

The use of deep learning techniques to perform side-channel analysis attracted the attention of many researchers as they obtained good performances with them. Unfortunately, the understanding of the neural networks used to perform side-channel attacks is not very advanced yet. In this paper, we propose to contribute to this direction by studying the impact of some particular deep learning techniques for tackling side-channel attack problems. More precisely, we propose to focus on three...

Deep learning-based side-channel analysis (SCA) represents a strong approach for profiling attacks. Still, this does not mean it is trivial to find neural networks that perform well for any setting. Based on the developed neural network architectures, we can distinguish between small neural networks that are easier to tune and less prone to overfitting but could have insufficient capacity to model the data. On the other hand, large neural networks have sufficient capacity but can overfit and...

Today, deep neural networks are a common choice for conducting the profiled side-channel analysis. Such techniques commonly do not require pre-processing, and yet, they can break targets protected with countermeasures. Unfortunately, it is not trivial to find neural network hyper-parameters that would result in such top-performing attacks. The hyper-parameter leading the training process is the number of epochs during which the training happens. If the training is too short, the network does...

Deep learning based side-channel analysis has seen a rise in popularity over the last few years. A lot of work is done to understand the inner workings of the neural networks used to perform the attacks and a lot is still left to do. However, finding a metric suitable for evaluating the capacity of the neural networks is an open problem that is discussed in many articles. We propose an answer to this problem by introducing an online evaluation metric dedicated to the context of side-channel...

In this paper, we suggest a new format for converting side channel traces to fully utilize the deep learning schemes. Due to the fact that many deep learning schemes have been advanced based on MNIST style datasets, we convert from raw-trace based on float or byte data to picture-formatted trace based on position. This is induced that the best performance can be acquired from deep learning schemes. Although the overfitting cannot be avoided in our suggestion, the accuracy for validation...

Profiled side-channel attacks represent a practical threat to digital devices, thereby having the potential to disrupt the foundation of e-commerce, Internet-of-Things (IoT), and smart cities. In the profiled side-channel attack, adversary gains knowledge about the target device by getting access to a cloned device. Though these two devices are different in real-world scenarios, yet, unfortunately, a large part of research works simplifies the setting by using only a single device for both...