Notes on Small Private Key Attacks on Common Prime RSA

We point out critical deficiencies in lattice-based cryptanalysis of common prime RSA presented in ``Remarks on the cryptanalysis of common prime RSA for IoT constrained low power devices'' [Information Sciences, 538 (2020) 54--68]. To rectify these flaws, we carefully scrutinize the relevant parameters involved in the analysis during solving a specific trivariate integer polynomial equation. Additionally, we offer a synthesized attack illustration of small private key attacks on common prime RSA.Comment: 15 pages, 1 figur

Improved Results on Factoring General RSA Moduli with Known Bits

We revisit the factoring with known bits problem on general RSA moduli in the forms of $N=p^r q^s$ for $r,s\ge 1$, where two primes $p$ and $q$ are of the same bit-size. The relevant moduli are inclusive of $pq$, $p^r q$ for $r>1$, and $p^r q^s$ for $r,s>1$, which are used in the standard RSA scheme and other RSA-type variants. Previous works acquired the results mainly by solving univariate modular equations. In contrast, we investigate how to efficiently factor $N=p^r q^s$ with given leakage of the primes by the integer method using the lattice-based technique in this paper. More precisely, factoring general RSA moduli with known most significant bits (MSBs) of the primes can be reduced to solving bivariate integer equations, which was first proposed by Coppersmith to factor $N=pq$ with known high bits. Our results provide a unifying solution to the factoring with known bits problem on general RSA moduli. Furthermore, we reveal that there exists an improved factoring attack via the integer method for particular RSA moduli like $p^3 q^2$ and $p^5 q^3$

Improved Factoring Attacks on Multi-Prime RSA with Small Prime Difference

In this paper, we study the security of multi-prime RSA with small prime difference and propose two improved factoring attacks. The modulus involved in this variant is the product of r distinct prime factors of the same bit-size. Zhang and Takagi (ACISP 2013) showed a Fermat-like factoring attack on multi-prime RSA. In order to improve the previous result, we gather more information about the prime factors to derive r simultaneous modular equations. The first attack is to combine all the equations and solve one multivariate equation by generic lattice approaches. Since the equation form is similar to multi-prime Phi-hiding problem, we propose the second attack by applying the optimal linearization technique. We also show that our attacks can achieve better bounds in the experiments

Enhancing the Performance of Practical Profiling Side-Channel Attacks Using Conditional Generative Adversarial Networks

Recently, many profiling side-channel attacks based on Machine Learning and Deep Learning have been proposed. Most of them focus on reducing the number of traces required for successful attacks by optimizing the modeling algorithms. In previous work, relatively sufficient traces need to be used for training a model. However, in the practical profiling phase, it is difficult or impossible to collect sufficient traces due to the constraint of various resources. In this case, the performance of profiling attacks is inefficient even if proper modeling algorithms are used. In this paper, the main problem we consider is how to conduct more efficient profiling attacks when sufficient profiling traces cannot be obtained. To deal with this problem, we first introduce the Conditional Generative Adversarial Network (CGAN) in the context of side-channel attacks. We show that CGAN can generate new traces to enlarge the size of the profiling set, which improves the performance of profiling attacks. For both unprotected and protected cryptographic algorithms, we find that CGAN can effectively learn the leakage of traces collected in their implementations. We also apply it to different modeling algorithms. In our experiments, the model constructed with the augmented profiling set can reduce the required attack traces by more than half, which means the generated traces can provide useful information as the real traces

Revisiting the Polynomial-Time Equivalence of Computing the CRT-RSA Secret Key and Factoring

The Rivest–Shamir–Adleman (RSA) cryptosystem is currently the most influential and commonly used algorithm in public-key cryptography. Whether the security of RSA is equivalent to the intractability of the integer factorization problem is an interesting issue in mathematics and cryptography. Coron and May solved the above most fundamental problem and proved the polynomial-time equivalence of computing the RSA secret key and factoring. They demonstrated that the RSA modulus N=pq can be factored in polynomial time when given RSA key information (N,e,d). The CRT-RSA variant is a fast technical implementation of RSA using the Chinese Remainder Theorem (CRT), which aims to speed up the decryption process. We focus on the polynomial-time equivalence of computing the CRT-RSA secret key and factoring in this paper. With the help of the latest partial key exposure attack on CRT-RSA, we demonstrate that there exists a polynomial-time algorithm outputting the factorization of N=pq for edp,edq<N3/2 when given the CRT-RSA key information (N,e,dp,dq). We apply Coppersmith’s lattice-based method as a basic mathematical tool for finding the small root solutions of modular polynomial equations. Furthermore, we provide validation experiments to illustrate the correctness of the CRT-RSA modulus factorization algorithm, and show that computing the CRT-RSA secret key and factoring its modulus is polynomial-time equivalent by using concrete numerical examples

A Novel Evaluation Metric for Deep Learning-Based Side Channel Analysis and Its Extended Application to Imbalanced Data

Since Kocher (CRYPTO’96) proposed timing attack, side channel analysis (SCA) has shown great potential to break cryptosystems via physical leakage. Recently, deep learning techniques are widely used in SCA and show equivalent and even better performance compared to traditional methods. However, it remains unknown why and when deep learning techniques are effective and efficient for SCA. Masure et al. (IACR TCHES 2020(1):348–375) illustrated that deep learning paradigm is suitable for evaluating implementations against SCA from a worst-case scenario point of view, yet their work is limited to balanced data and a specific loss function. Besides, deep learning metrics are not consistent with side channel metrics. In most cases, they are deceptive in foreseeing the feasibility and complexity of mounting a successful attack, especially for imbalanced data. To mitigate the gap between deep learning metrics and side channel metrics, we propose a novel Cross Entropy Ratio (CER) metric to evaluate the performance of deep learning models for SCA. CER is closely related to traditional side channel metrics Guessing Entropy (GE) and Success Rate (SR) and fits to deep learning scenario. Besides, we show that it works stably while deep learning metrics such as accuracy becomes rather unreliable when the training data tends to be imbalanced. However, estimating CER can be done as easy as natural metrics in deep learning algorithms with low computational complexity. Furthermore, we adapt CER metric to a new kind of loss function, namely CER loss function, designed specifically for deep learning in side channel scenario. In this way, we link directly the SCA objective to deep learning optimization. Our experiments on several datasets show that, for SCA with imbalanced data, CER loss function outperforms Cross Entropy loss function in various conditions