A Physically Unclonable Function (PUF) is a unique and stable physical characteristic of a piece of hardware, which emerges due to variations in the hardware fabrication processes. Prior works have demonstrated that PUFs are a promising cryptographic primitive that can enable secure key storage, hardware-based device authentication and identification. So far, most PUF constructions have required an addition of new hardware or an FPGA implementation for their operation. Recently, intrinsic PUFs, which can be found in commodity devices, have been investigated. Unfortunately, most of them suffer from the drawback that they can only be accessed at boot time. This paper focuses on a new class of run-time accessible, decay-based, intrinsic DRAM PUFs in commercial off-the-shelf systems, which requires no additional hardware or FPGAs. In order to enable secure key storage using DRAM PUFs, this work presents a new Helper Data System (HDS) specifically tailored to the properties of the decay process inherent to DRAM cells. The decay-based DRAM PUF and the new HDS are evaluated on commodity off-the-shelf devices to demonstrate their practicality. Furthermore, a novel lightweight protocol is presented that allows for mutual authentication.

Quantum readout of physical unclonable functions (PUFs) is a recently introduced method for remote authentication of objects. We present an extension of the protocol to enable the authentication of data: A verifier can check if received classical data were sent by the PUF holder. We call this modification QR-d or, in the case of the optical-PUF implementation, QSA-d. We discuss how QSA-d can be operated in a parallel way. We also present a protocol for authenticating quantum states.

In recent years, physically unclonable functions (PUFs) have been proposed as a promising building block for key storage and device authentication. PUFs are physical systems, and as such, their responses are inherently noisy, precluding a straightforward derivation of cryptographic key material from raw PUF measurements. To overcome this drawback, fuzzy extractors are used to eliminate the noise and guarantee robust outputs. A special type is reverse fuzzy extractors, shifting the computational load of error correction toward a computationally powerful verifier. However, the reverse fuzzy extractor reveals error patterns to any eavesdropper, which may cause privacy issues (due to a systematic drift of the PUF responses, the error pattern is linkable to the identity) and even security problems (if the noise is data-dependent). In this paper, we quantify the issue of leakage due to asymmetry of noise, leveraging the binary asymmetric channel (BAC) model. We further propose to concatenate two BACs to form a symmetric channel, as a solution that is able to eliminate such noise. Finally, we propose a modified reverse fuzzy extractor that does not leak via the error patterns even in the case of systematic drift of the PUF responses.

We study the probability distribution of user accusations in the q-ary Tardos fingerprinting system under the Marking Assumption, in the restricted digit model. In particular, we look at the applicability of the so-called Gaussian approximation, which states that accusation probabilities tend to the normal distribution when the fingerprinting code is long. We introduce a novel parametrization of the attack strategy which enables a significant speedup of numerical evaluations. We set up a method, based on power series expansions, to systematically compute the probability of accusing innocent users. The ‘small parameter’ in the power series is 1/m, where m is the code length. We use our method to semi-analytically study the performance of the Tardos code against majority voting and interleaving attacks. The bias function ‘shape’ parameter ${{\kappa}}$ strongly influences the distance between the actual probabilities and the asymptotic Gaussian curve. The impact on the collusion-resilience of the code is shown. For some realistic parameter values, the false accusation probability is even lower than the Gaussian approximation predicts.

We introduce a debiasing scheme that solves the more noise than entropy problem which can occur in Helper Data Systems when the source is very biased. We perform a condensing step, similar to Index-Based Syndrome coding, that reduces the size of the source space in such a way that some source entropy is lost, while the noise entropy is greatly reduced. In addition, our method allows for even more entropy extraction by means of a ‘spamming’ technique. Our method outperforms solutions based on the one-pass and two-pass von Neumann algorithms.