The Wiretap Channel for Capacitive PUF-Based Security Enclosures
In order to protect devices from physical manipulations, protective security enclosures were developed. However, these battery-backed solutions come with a reduced lifetime, and have to be actively and continuously monitored.In order to overcome these drawbacks, batteryless capacitive enclosures based on Physical Unclonable Functions (PUFs) have been developed that generate a keyencryption-key (KEK) for decryption of the key chain. In order to reproduce the PUF-key reliably and to compensate the effect of noise and environmental influences, the key generation includes error correction codes. However, drilling attacks that aim at partially destroying the enclosure also alter the PUF-response and are subjected to the same error correction procedures. Correcting attack effects, however, is highly undesirable as it would destroy the security concept of the enclosure. In general, designing error correction codes such that they provide tamper-sensitivity to attacks, while still correcting noise and environmental effects is a challenging task.We tackle this problem by first analyzing the behavior of the PUF-response under external influences and different post-processing parameters. From this, we derive a system model of the PUF-based enclosure, and construct a wiretap channel implementation from q-ary polar codes. We verify the obtained error correction scheme in a Monte Carlo simulation and demonstrate that our wiretap channel implementation achieves a physical layer security of 100 bits for 306 bits of entropy for the PUF-secret. Through this, we further develop capacitive PUF-based security enclosures and bring them one step closer to their commercial deployment.
Machine Learning of Physical Unclonable Functions using Helper Data: Revealing a Pitfall in the Fuzzy Commitment Scheme 📺
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.