Systematic Use of Random Self-Reducibility against Physical Attacks

• URL: http://arxiv.org/abs/2405.05193v1
• Date: Wed, 8 May 2024 16:31:41 GMT
• Title: Systematic Use of Random Self-Reducibility against Physical Attacks
• Authors: Ferhat Erata, TingHung Chiu, Anthony Etim, Srilalith Nampally, Tejas Raju, Rajashree Ramu, Ruzica Piskac, Timos Antonopoulos, Wenjie Xiong, Jakub Szefer,
• Abstract summary: This work presents a novel, black-box software-based countermeasure against physical attacks including power side-channel and fault-injection attacks. The approach uses the concept of random self-reducibility and self-correctness to add randomness and redundancy in the execution for protection. An end-to-end implementation of this countermeasure is demonstrated for RSA-CRT signature algorithm and Kyber Key Generation public key cryptosystems.
• Score: 10.581645335323655
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work presents a novel, black-box software-based countermeasure against physical attacks including power side-channel and fault-injection attacks. The approach uses the concept of random self-reducibility and self-correctness to add randomness and redundancy in the execution for protection. Our approach is at the operation level, is not algorithm-specific, and thus, can be applied for protecting a wide range of algorithms. The countermeasure is empirically evaluated against attacks over operations like modular exponentiation, modular multiplication, polynomial multiplication, and number theoretic transforms. An end-to-end implementation of this countermeasure is demonstrated for RSA-CRT signature algorithm and Kyber Key Generation public key cryptosystems. The countermeasure reduced the power side-channel leakage by two orders of magnitude, to an acceptably secure level in TVLA analysis. For fault injection, the countermeasure reduces the number of faults to 95.4% in average.

Related papers

• Information Theoretic Analysis of PUF-Based Tamper Protection [2.447795279790662]
  We take a step back from the implementation to analyze theoretical properties and limits. We apply zero leakage output quantization to existing quantization schemes and the reconstruction error probability under zero leakage. Our results show for example that for a practical scenario one needs at least 459 PUF cells using 3 bit quantization to achieve a security level of 128 bit.
  arXiv  Detail & Related papers  (2025-02-05T14:39:41Z)
• Evaluation of quantum key distribution systems against injection-locking attacks [0.0]
  Current security proofs for decoy-state BB84 protocols assume uniform phase randomization of Alice's signals. This work presents an experimental method to characterize the phase de-randomization from injection locking. The methods presented are source-agnostic and can be used to evaluate general QKD systems against injection-locking attacks.
  arXiv  Detail & Related papers  (2024-12-13T17:21:14Z)
• Privacy-Preserving Distributed Learning for Residential Short-Term Load Forecasting [11.185176107646956]
  Power system load data can inadvertently reveal the daily routines of residential users, posing a risk to their property security. We introduce a Markovian Switching-based distributed training framework, the convergence of which is substantiated through rigorous theoretical analysis. Case studies employing real-world power system load data validate the efficacy of our proposed algorithm.
  arXiv  Detail & Related papers  (2024-02-02T16:39:08Z)
• Carry Your Fault: A Fault Propagation Attack on Side-Channel Protected LWE-based KEM [12.164927192334748]
  We propose a new fault attack on side-channel secure masked implementation of LWE-based key-encapsulation mechanisms. We exploit the data dependency of the adder carry chain in A2B and extract sensitive information. We show key recovery attacks of Kyber, although the leakage also exists for other schemes like Saber.
  arXiv  Detail & Related papers  (2024-01-25T11:18:43Z)
• Versatile Weight Attack via Flipping Limited Bits [68.45224286690932]
  We study a novel attack paradigm, which modifies model parameters in the deployment stage. Considering the effectiveness and stealthiness goals, we provide a general formulation to perform the bit-flip based weight attack. We present two cases of the general formulation with different malicious purposes, i.e., single sample attack (SSA) and triggered samples attack (TSA)
  arXiv  Detail & Related papers  (2022-07-25T03:24:58Z)
• Balancing detectability and performance of attacks on the control channel of Markov Decision Processes [77.66954176188426]
  We investigate the problem of designing optimal stealthy poisoning attacks on the control channel of Markov decision processes (MDPs) This research is motivated by the recent interest of the research community for adversarial and poisoning attacks applied to MDPs, and reinforcement learning (RL) methods.
  arXiv  Detail & Related papers  (2021-09-15T09:13:10Z)
• Sparse and Imperceptible Adversarial Attack via a Homotopy Algorithm [93.80082636284922]
  Sparse adversarial attacks can fool deep networks (DNNs) by only perturbing a few pixels. Recent efforts combine it with another l_infty perturbation on magnitudes. We propose a homotopy algorithm to tackle the sparsity and neural perturbation framework.
  arXiv  Detail & Related papers  (2021-06-10T20:11:36Z)
• Targeted Attack against Deep Neural Networks via Flipping Limited Weight Bits [55.740716446995805]
  We study a novel attack paradigm, which modifies model parameters in the deployment stage for malicious purposes. Our goal is to misclassify a specific sample into a target class without any sample modification. By utilizing the latest technique in integer programming, we equivalently reformulate this BIP problem as a continuous optimization problem.
  arXiv  Detail & Related papers  (2021-02-21T03:13:27Z)
• Covert Model Poisoning Against Federated Learning: Algorithm Design and Optimization [76.51980153902774]
  Federated learning (FL) is vulnerable to external attacks on FL models during parameters transmissions. In this paper, we propose effective MP algorithms to combat state-of-the-art defensive aggregation mechanisms. Our experimental results demonstrate that the proposed CMP algorithms are effective and substantially outperform existing attack mechanisms.
  arXiv  Detail & Related papers  (2021-01-28T03:28:18Z)
• A Self-supervised Approach for Adversarial Robustness [105.88250594033053]
  Adversarial examples can cause catastrophic mistakes in Deep Neural Network (DNNs) based vision systems. This paper proposes a self-supervised adversarial training mechanism in the input space. It provides significant robustness against the textbfunseen adversarial attacks.
  arXiv  Detail & Related papers  (2020-06-08T20:42:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.