International Association for Cryptologic Research

International Association
for Cryptologic Research


Emanuele Strieder


Machine Learning of Physical Unclonable Functions using Helper Data: Revealing a Pitfall in the Fuzzy Commitment Scheme 📺
Emanuele Strieder Christoph Frisch Michael Pehl
Physical Unclonable Functions (PUFs) are used in various key-generation schemes and protocols. Such schemes are deemed to be secure even for PUFs with challenge-response behavior, as long as no responses and no reliability information about the PUF are exposed. This work, however, reveals a pitfall in these constructions: When using state-of-the-art helper data algorithms to correct noisy PUF responses, an attacker can exploit the publicly accessible helper data and challenges. We show that with this public information and the knowledge of the underlying error correcting code, an attacker can break the security of the system: The redundancy in the error correcting code reveals machine learnable features and labels. Learning these features and labels results in a predictive model for the dependencies between different challenge-response pairs (CRPs) without direct access to the actual PUF response. We provide results based on simulated data of a k-SUM PUF model and an Arbiter PUF model. We also demonstrate the attack for a k-SUM PUF model generated from real data and discuss the impact on more recent PUF constructions such as the Multiplexer PUF and the Interpose PUF. The analysis reveals that especially the frequently used repetition code is vulnerable: For a SUM-PUF in combination with a repetition code, e.g., already the observation of 800 challenges and helper data bits suffices to reduce the entropy of the key down to one bit. The analysis also shows that even other linear block codes like the BCH, the Reed-Muller, or the Single Parity Check code are affected by the problem. The code-dependent insights we gain from the analysis allow us to suggest mitigation strategies for the identified attack. While the shown vulnerability advances Machine Learning (ML) towards realistic attacks on key-storage systems with PUFs, our analysis also facilitates a better understanding and evaluation of existing approaches and protocols with PUFs. Therefore, it brings the community one step closer to a more complete leakage assessment of PUFs.
Chosen Ciphertext k-Trace Attacks on Masked CCA2 Secure Kyber 📺
Single-trace attacks are a considerable threat to implementations of classic public-key schemes, and their implications on newer lattice-based schemes are still not well understood. Two recent works have presented successful single-trace attacks targeting the Number Theoretic Transform (NTT), which is at the heart of many lattice-based schemes. However, these attacks either require a quite powerful side-channel adversary or are restricted to specific scenarios such as the encryption of ephemeral secrets. It is still an open question if such attacks can be performed by simpler adversaries while targeting more common public-key scenarios. In this paper, we answer this question positively. First, we present a method for crafting ring/module-LWE ciphertexts that result in sparse polynomials at the input of inverse NTT computations, independent of the used private key. We then demonstrate how this sparseness can be incorporated into a side-channel attack, thereby significantly improving noise resistance of the attack compared to previous works. The effectiveness of our attack is shown on the use-case of CCA2 secure Kyber k-module-LWE, where k ∈ {2, 3, 4}. Our k-trace attack on the long-term secret can handle noise up to a σ ≤ 1.2 in the noisy Hamming weight leakage model, also for masked implementations. A 2k-trace variant for Kyber1024 even allows noise σ ≤ 2.2 also in the masked case, with more traces allowing us to recover keys up to σ ≤ 2.7. Single-trace attack variants have a noise tolerance depending on the Kyber parameter set, ranging from σ ≤ 0.5 to σ ≤ 0.7. As a comparison, similar previous attacks in the masked setting were only successful with σ ≤ 0.5.