Nonuniform Codes for Correcting Asymmetric Errors in Data Storage

Security-critical products rely on the secrecy and integrity of their cryptographic keys. This is challenging for lowcost resource-constrained embedded devices, with an attacker having physical access to the integrated circuit (IC). Physically unclonable functions (PUFs) are an emerging technology in this market. They extract bits from unavoidable IC manufacturing variations, remarkably analogous to unique human fingerprints. However, post-processing by helper data algorithms is indispensable to meet the stringent key requirements: reproducibility, high-entropy and control. The novelty of our work is threefold. We are the first to provide an in-depth and comprehensive literature overview on HDAs. Second, our analysis does expose new threats regarding helper data leakage and manipulation. Third, we identify several hiatuses in current research.

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“…the following scenarios. First, 1 → 0 and 0 → 1 errors might not be equivalent [47]. Second, t might differ per codeword.…”

Section: Codes In Parallelmentioning

confidence: 99%

Helper Data Algorithms for PUF-Based Key Generation: Overview and Analysis

Delvaux

Schellekens

et al. 2015

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

221

145

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“…This implies that the channel is asymmetric, and the probabilities of error in sending S = 0 or S = 1 are different. The probabilities of error for each of the symbols can be made equal by alternating the assignments of bits in (3) over time, or by applying coding dedicated to asymmetric channels, see [35], [36], [37] and references therein.…”

Section: Transmission Over the Dbmt Channel: ML And Linear Detecmentioning

confidence: 99%

Optimal Detection for One-Shot Transmission Over Diffusion-Based Molecular Timing Channels

Murin

Farsad

Chowdhury

et al. 2018

IEEE Trans. Mol. Biol. Multi-Scale Commun.

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This work studies optimal detection for communication over diffusion-based molecular timing (DBMT) channels. The transmitter simultaneously releases multiple information particles, where the information is encoded in the time of release. The receiver decodes the transmitted information based on the random time of arrival of the information particles, which is modeled as an additive noise channel. For a DBMT channel without flow, this noise follows the Lévy distribution. Under this channel model, the maximum-likelihood (ML) detector is derived and shown to have high computational complexity. It is also shown that under ML detection, releasing multiple particles improves performance, while for any additive channel with αstable noise where α < 1 (such as the DBMT channel), under linear processing at the receiver, releasing multiple particles degrades performance relative to releasing a single particle. Hence, a new low-complexity detector, which is based on the first arrival (FA) among all the transmitted particles, is proposed. It is shown that for a small number of released particles, the performance of the FA detector is very close to that of the ML detector. On the other hand, error exponent analysis shows that the performance of the two detectors differ when the number of released particles is large.

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“…Our model also includes the Z-channel with S(0, 1) = 0 where only false negatives occur, i.e., errors of the form 1 → 0. The Z-channel is particularly important from a practical point of view: it captures the fact that typically in many applications the false-positive probability is insignificant [11,35].…”

Section: Introductionmentioning

confidence: 99%

Distributed Reconstruction of Noisy Pooled Data

Hahn‐Klimroth

Kaaser²

2022

2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)

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In the pooled data problem we are given n agents with hidden state bits, either 0 or 1. The hidden states are unknown and can be seen as the underlying ground truth σ. To uncover that ground truth, we are given a querying method that queries multiple agents at a time. Each query reports the sum of the states of the queried agents. Our goal is to learn the hidden state bits using as few queries as possible.So far, most literature deals with exact reconstruction of all hidden state bits. We study a more relaxed variant in which we allow a small fraction of agents to be classified incorrectly. This becomes particularly relevant in the noisy variant of the pooled data problem where the queries' results are subject to random noise. In this setting, we provide a doubly regular test design that assigns agents to queries. For this design we analyze an approximate reconstruction algorithm that estimates the hidden bits in a greedy fashion. We give a rigorous analysis of the algorithm's performance, its error probability, and its approximation quality. As a main technical novelty, our analysis is uniform in the degree of noise and the sparsity of σ. Finally, simulations back up our theoretical findings and provide strong empirical evidence that our algorithm works well for realistic sample sizes.

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Nonuniform Codes for Correcting Asymmetric Errors in Data Storage

Cited by 11 publications

References 23 publications

Helper Data Algorithms for PUF-Based Key Generation: Overview and Analysis

Helper Data Algorithms for PUF-Based Key Generation: Overview and Analysis

Optimal Detection for One-Shot Transmission Over Diffusion-Based Molecular Timing Channels

Distributed Reconstruction of Noisy Pooled Data

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