Security of helper data schemes for SRAM-PUF in multiple enrollment scenarios

Kusters, Lieneke; Ignatenko, Tanya; Willems, F.M.J.; Maes, Roel; Sluis, Erik van der; Selimis, Georgios

doi:10.1109/isit.2017.8006840

Cited by 18 publications

(14 citation statements)

References 8 publications

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“…Multiple enrollment scenarios are studied from leakage perspective in [16] and [17]. In these papers, scenarios are considered (e.g., the reverse fuzzy extractor [18]) in which enrollment is repeated multiple times and correspondingly multiple helper data is generated.…”

Section: Related Workmentioning

confidence: 99%

Multiple Observations for Secret-Key Binding with SRAM PUFs

Kusters

Willems

2021

Preprint

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We present a new Multiple-Observations (MO) helper data scheme for secret-key binding to an SRAM PUF. This MO scheme binds a single key to multiple enrollment observations of the SRAM PUF. Performance is improved in comparison to classic schemes which generate helper data based on a single enrollment observation. The performance increase can be explained by the fact that the reliabilities of the different SRAM cells are modeled (implicitly) in the helper data. We prove that the scheme achieves secret-key capacity for any number of enrollment observations, and, therefore it is optimal. We evaluate performance of the scheme using Monte Carlo simulations, where an off-the-shelf LDPC code is used to implement the linear error-correcting code. Another scheme that models the reliabilities of the SRAM cells is the so-called Soft-Decision (SD) helper data scheme. The SD scheme considers the one-probabilities of the SRAM cells as an input, which in practice are not observable. We present a new strategy for the SD scheme that considers the binary SRAM-PUF observations as an input instead and show that the new strategy is optimal and achieves the same reconstruction performance as the MO scheme. Finally, we present a variation on the MO helper data scheme that updates the helper data sequentially after each successful reconstruction of the key. As a result, the error-correcting performance of the scheme is improved over time.

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Section: Related Workmentioning

confidence: 99%

Multiple Observations for Secret-Key Binding with SRAM PUFs

Kusters

Willems

2021

Preprint

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“…This is because a reverse fuzzy extractor can result in unanticipated entropy loss under repeated helper data exposure associated with a given PUF response r ; unless, PUF responses demonstrate a symmetry property. In other words, the one-probability, the probability of a given bit attaining a binary one value, of PUF responses is a symmetric distribution [54]; alternatively, are unbiased. Generally, the extra entropy loss is a result of the leakage of bit-specific reliability information [53].…”

Section: B Entropy Leakagementioning

confidence: 99%

Lightweight (Reverse) Fuzzy Extractor With Multiple Reference PUF Responses

Gao

et al. 2019

IEEE Trans.Inform.Forensic Secur.

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A Physical unclonable functions (PUF), alike a fingerprint, exploits manufacturing randomness to endow each physical item with a unique identifier. One primary PUF application is the secure derivation of volatile cryptographic keys using a fuzzy extractor comprising of two procedures: i) secure sketch; and ii) entropy extraction. Although the entropy extractor can be lightweight, the overhead of the secure sketch responsible correcting naturally noisy PUF responses is usually costly. We observe that, in general, response unreliability with respect to a enrolled reference measurement increases with increasing differences between the in-the-field PUF operating condition and the operating condition used in evaluating the enrolled reference response. For the first time, we exploit such an important but inadvertent observation. In contrast to the conventional single reference response enrollment, we propose enrolling multiple reference responses (MRR) subject to the same challenge but under multiple distinct operating conditions. The critical observation here is that one of the reference operating conditions is likely to be closer to the operating condition of the field deployed PUF, thus, resulting in minimizing the expected unreliability when compared to the single reference under the nominal condition. Overall, MRR greatly reduces the demand for the expected number of erroneous bits for correction and, subsequently, achieve a significant reduction in the error correction overhead. The significant implementation efficiency gains from the proposed MRR method is demonstrated from software implementations of fuzzy extractors on batteryless resource constraint computational radio frequency identification devices, where realistic PUF data is collected from the embedded intrinsic SRAM PUFs.

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“…Boyen showed that the regular fuzzy extractor scheme is not secure in general when the scheme is re-used on multiple observations of the same biometric source. In [17], [18] it was shown that when the density of the oneprobabilities of the SRAM-PUF is symmetric, both the fuzzy commitment scheme and the syndrome method remain secure in the case of repeated enrollments. This result was further extended to the temperature dependent SRAM-PUF model for the unbiased case in [19].…”

Section: A Related Workmentioning

confidence: 99%

Secret-Key Capacity Regions for Multiple Enrollments With an SRAM-PUF

Kusters

Willems

2019

IEEE Trans.Inform.Forensic Secur.

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Security of helper data schemes for SRAM-PUF in multiple enrollment scenarios

Cited by 18 publications

References 8 publications

Multiple Observations for Secret-Key Binding with SRAM PUFs

Multiple Observations for Secret-Key Binding with SRAM PUFs

Lightweight (Reverse) Fuzzy Extractor With Multiple Reference PUF Responses

Secret-Key Capacity Regions for Multiple Enrollments With an SRAM-PUF

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