We show that the data classification scheme [IEEE Trans. Sustain. Comput., 2023, 8(4), 652-669)] failed to check the compatibility of encoding algorithm and homomorphic encryption algorithm. Some calculations should be revised to ensure all operands are first encoded using the same scaling factors. The canonical embedding map depending on the natural projection should be explicitly arranged so as to construct an efficient decoding algorithm.

Homomorphic Encryption (HE) technology allows for processing encrypted data, breaking through data isolation barriers and providing a promising solution for privacy-preserving computation. The integration of HE technology into Convolutional Neural Network (CNN) inference shows potential in addressing privacy issues in identity verification, medical imaging diagnosis, and various other applications. The CKKS HE algorithm stands out as a popular option for homomorphic CNN inference due to its...

Side-channel analysis on complex SoC devices with high-frequency microprocessors and multitasking operating systems presents significant challenges in practice due to the high costs of trace acquisition and analysis, generally involving tens of thousands to millions of traces. This work uses a cryptographic execution process on a Broadcom 2837 SoC as a case study to explore ways to reduce costs in electromagnetic side-channel analysis. In the data acquisition phase, we propose an efficient...

Model quantization has become a common practice in machine learning (ML) to improve efficiency and reduce computational/communicational overhead. However, adopting quantization in privacy-preserving machine learning (PPML) remains challenging due to the complex internal structure of quantized operators, which leads to inefficient protocols under the existing PPML frameworks. In this work, we propose a new PPML paradigm that is tailor-made for and can benefit from quantized models. Our...

As we enter 2024, the post-quantum cryptographic algorithm Dilithium, which emerged from the National Institute of Standards and Technology post-quantum cryptography competition, has now reached the deployment stage. This paper focuses on the practical security of Dilithium. We performed practical attacks on Dilithium2 on an STM32F4 platform. Our results indicate that an attack can be executed with just two signatures within five minutes, with a single signature offering a 60% probability of...

The privacy-preserving machine learning (PPML) has gained growing importance over the last few years. One of the biggest challenges is to improve the efficiency of PPML so that the communication and computation costs of PPML are affordable for large machine learning models such as deep learning. As we know, linear algebra such as matrix multiplication occupies a significant part of the computation in deep learning such as deep convolutional neural networks (CNN). Thus, it is desirable to...

Deep learning-based cryptanalysis is one of the emerging trends in recent times. Differential cryptanalysis is one of the most po- tent approaches to classical cryptanalysis. Researchers are now modeling classical differential cryptanalysis by applying deep learning-based tech- niques. In this paper, we report deep learning-based differential distin- guishers for block cipher PRIDE and RC5, utilizing deep learning models: CNN, LGBM and LSTM. We found distinguishers up to 23 rounds...

During the last decade, there has been a stunning progress in the domain of AI with adoption in both safety-critical and security-critical applications. A key requirement for this is highly trained Machine Learning (ML) models, which are valuable Intellectual Property (IP) of the respective organizations. Naturally, these models have become targets for model recovery attacks through side-channel leakage. However, majority of the attacks reported in literature are either on simple embedded...

Secure inference of deep convolutional neural networks (CNNs) under RNS-CKKS involves polynomial approximation of unsupported non-linear activation functions. However, existing approaches have three main limitations: 1) Inflexibility: The polynomial approximation and associated homomorphic evaluation architecture are customized manually for each CNN architecture and do not generalize to other networks. 2) Suboptimal Approximation: Each activation function is approximated instead of the...

The usage of convolutional neural networks (CNNs) to break cryptographic systems through hardware side-channels has enabled fast and adaptable attacks on devices like smart cards and TPMs. Current literature proposes fixed CNN architectures designed by domain experts to break such systems, which is time-consuming and unsuitable for attacking a new system. Recently, an approach using neural architecture search (NAS), which is able to acquire a suitable architecture automatically, has been...

Previous studies on fault analysis have demonstrated promising potential in compromising cryptographic security. However, these fault analysis methods are limited in practical impact due to methodological constraints and the substantial requirement of faulty information such as correct and faulty ciphertexts. Additionally, while deep learning techniques have been widely applied to side-channel analysis (SCA) in recent years and have shown superior performance compared with traditional...

As an emerging joint learning model, federated deep learning is a promising way to combine model parameters of different users for training and inference without collecting users’ original data. However, a practical and efficient solution has not been established in previous work due to the absence of effcient matrix computation and cryptography schemes in the privacy-preserving federated learning model, especially in partially homomorphic cryptosystems. In this paper, we propose a practical...

Deep learning (DL)-based profiled attack has been proved to be a powerful tool in side-channel analysis. A variety of multi-layer perception (MLP) networks and convolutional neural networks (CNN) are thereby applied to cryptographic algorithm implementations for exploiting correct keys with a smaller number of traces and a shorter time. However, most attacks merely focus on small datasets, in which their points of interest are well-trimmed for attacks. Countermeasures applied in embedded...

We propose OmniLytics, a blockchain-based secure data trading marketplace for machine learning applications. Utilizing OmniLytics, many distributed data owners can contribute their private data to collectively train an ML model requested by some model owners, and receive compensation for data contribution. OmniLytics enables such model training while simultaneously providing 1) model security against curious data owners; 2) data security against the curious model and data owners; 3)...

Fully homomorphic encryption (FHE) is one of the prospective tools for privacy-preserving machine learning (PPML), and several PPML models have been proposed based on various FHE schemes and approaches. Although the FHE schemes are known as suitable tools to implement PPML models, previous PPML models on FHE such as CryptoNet, SEALion, and CryptoDL are limited to only simple and non-standard types of machine learning models. These non-standard machine learning models are not proven efficient...

Deep learning techniques with neural networks are developing prominently in recent years and have been deployed in numerous applications. Despite their great success, in many scenarios it is important for the users to validate that the inferences are truly computed by legitimate neural networks with high accuracy, which is referred to as the integrity of machine learning predictions. To address this issue, in this paper, we propose zkCNN, a zero knowledge proof scheme for convolutional...

Binarized Neural Networks (BNN) provide efficient implementations of Convolutional Neural Networks (CNN). This makes them particularly suitable to perform fast and memory-light inference of neural networks running on resource-constrained devices. Motivated by the growing interest in CNN-based biometric recognition on potentially insecure devices, or as part of strong multi-factor authentication for sensitive applications, the protection of BNN inference on edge devices is rendered...

We present TinyGarble2 – a C++ framework for privacy-preserving computation through the Yao’s Garbled Circuit (GC) protocol in both the honest-but-curious and the malicious security models. TinyGarble2 provides a rich library with arithmetic and logic building blocks for developing GC-based secure applications. The framework offers abstractions among three layers: the C++ program, the GC back-end and the Boolean logic representation of the function being computed. TinyGarble2 thus allowing...

Deep learning approaches have become popular for Side-Channel Analysis (SCA) in the recent years. Especially Convolutional Neural Networks (CNN) due to their natural ability to overcome jitter-based as well as masking countermeasures. However, most efforts have focused on finding optimal architecture for a given dataset and bypass the need for trace pre-processing. However, trace pre-processing is a long studied topic and several proven techniques exist in the literature. There is no...

In this paper we show that deep learning can be used to identify the shape of power traces corresponding to the responses of a protected arbiter PUF implemented in FPGAs. To achieve that, we combine power analysis with bitstream modification. We train a CNN classifier on two 28nm XC7 FPGAs implementing 128-stage arbiter PUFs and then classify the responses of PUFs from two other FPGAs. We demonstrate that it is possible to reduce the number of traces required for a successful attack to a...

Recently, research on deep learning based side-channel analysis (DLSCA) has received a lot of attention. Deep learning-based profiling methods similar to template attacks as well as non-profiling-based methods similar to differential power analysis have been proposed. DLSCA methods have been proposed for targets to which masking schemes or jitter-based hiding schemes are applied. However, most of them are methods for finding the secret key, except for methods for preprocessing, and there are...

Code polymorphism is a way to efficiently address the challenge of automatically applying the hiding of sensitive information leakage, as a way to protect cryptographic primitives against side-channel attacks (SCA) involving layman adversaries. Yet, recent improvements in SCA, involving more powerful threat models, e.g., using deep learning, emphasized the weaknesses of some hiding counter-measures. This raises two questions. On the one hand, the security of code polymorphism against more...

The use of deep learning in side-channel analysis has been more and more prominent recently. In particular, Convolution Neural Networks (CNN) are very efficient tools to extract the secret information from side-channel traces. Previous work regarding the use of CNN in side-channel has been mostly proposed through practical results. Zaid et al. have proposed a theoretical methodology in order to better understand the convolutional part of CNN and to understand how to construct an efficient...

With the development of AI systems, services using them expand to various applications. The widespread adoption of AI systems relies substantially on the ability to trust their output. Therefore, it is becoming important for a client to be able to check whether the AI inference services have been correctly calculated. Since the weight value in a CNN model is an asset of service providers, the client should be able to check the correctness of the result without the weight value. Furthermore,...

The majority of recently demonstrated deep-learning side-channel attacks use a single neural network classifier to recover the key. The potential benefits of combining multiple classifiers have not been explored yet in the side-channel attack's context. In this paper, we show that, by combining several CNN classifiers which use different attack points, it is possible to considerably reduce (more than 40% on average) the number of traces required to recover the key from an FPGA implementation...

In side-channel analysis attacks, on-die localized EM monitoring enable high bandwidth measurements of only a relevant part of the Integrated Circuit (IC). This can lead to improved attacks compared to cases where only power consumption is measured. Combined with profiled attacks which utilize a training phase to create precise models of the information leakage, the attacks can become even more powerful. In contrast, localized EM measurements can cause difficulties in applying the learned...

The side-channel community recently investigated a new approach, based on deep learning, to significantly improve profiled attacks against embedded systems. Previous works have shown the benefit of using convolutional neural networks (CNN) to limit the effect of some countermeasures such as desynchronization. Compared with template attacks, deep learning techniques can deal with trace misalignment and the high dimensionality of the data. Pre-processing is no longer mandatory. However, the...

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 captured from a chip with a known key. The trained model is then used to recover the unknown key from a few traces captured from a victim chip. The first successful attacks have been demonstrated recently. However, they typically train and test on power traces captured from the same device. In this...

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 for the potentially sensitive data that they process. A solution to this problem is to combine CNNs with Fully Homomorphic Encryption (FHE) techniques. In this work, we study this approach by focusing on two popular FHE schemes, TFHE and HEAAN, that can work in the approximated computational...

This study compares the experimental results of Template Attacks (TA) and Deep Learning (DL) techniques called Multi Layer Perceptron (MLP) and Convolutional Neural Network (CNN), concurrently in front of classical use cases often encountered in the side-channel analysis of cryptographic devices (restricted to SK). The starting point regards their comparative effectiveness against masked encryption which appears as intrinsically vulnerable. Surprisingly TA improved with Principal Components...

Deep Learning as a Service (DLaaS) stands as a promising solution for cloud-based inference applications. In this setting, the cloud has a pre-learned model whereas the user has samples on which she wants to run the model. The biggest concern with DLaaS is the user privacy if the input samples are sensitive data. We provide here an efficient privacy-preserving system by employing high-end technologies such as Fully Homomorphic Encryption (FHE), Convolutional Neural Networks (CNNs) and...

Homomorphic Encryption (HE) is a powerful cryptographic primitive to address privacy and security issues in outsourcing computation on sensitive data to an untrusted computation environment. Comparing to secure Multi-Party Computation (MPC), HE has advantages in supporting non-interactive operations and saving on communication costs. However, it has not come up with an optimal solution for modern learning frameworks, partially due to a lack of efficient matrix computation mechanisms. In...

The growing popularity of cloud-based machine learning raises a natural question about the privacy guarantees that can be provided in such a setting. Our work tackles this problem in the context where a client wishes to classify private images using a convolutional neural network (CNN) trained by a server. Our goal is to build efficient protocols whereby the client can acquire the classification result without revealing their input to the server, while guaranteeing the privacy of the...

In the context of the security evaluation of cryptographic implementations, profiling attacks (aka Template Attacks) play a fundamental role. Nowadays the most popular Template Attack strategy consists in approximating the information leakages by Gaussian distributions. Nevertheless this approach suffers from the difficulty to deal with both the traces misalignment and the high dimensionality of the data. This forces the attacker to perform critical preprocessing phases, such as the...

This paper proposes the use of Cellular Non-Linear Networks (CNNs) as physical uncloneable functions (PUFs). We argue that analog circuits offer higher security than existing digital PUFs and that the CNN paradigm allows us to build large, unclonable, and scalable analog PUFs, which still show a stable and repeatable input--output behavior. CNNs are dynamical arrays of locally-interconected cells, with a cell dynamics that depends upon the interconnection strengths to their neighbors. ...