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

Kusters, Lieneke; Willems, F.M.J.

doi:10.1109/tifs.2019.2895552

Cited by 25 publications

(14 citation statements)

References 32 publications

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“…Weak PUFs provide fewer CRPs. However, these PUFs have been increasingly popular as internal key generators [28,29]. In this work, we focus on the authentication protocols that are based on strong PUFs.…”

Section: Physical Unclonable Functionmentioning

confidence: 99%

Security Analysis of Machine Learning-Based PUF Enrollment Protocols: A Review

Khalfaoui

Leneutre

Villard³

et al. 2021

Sensors

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The demand for Internet of Things services is increasing exponentially, and consequently a large number of devices are being deployed. To efficiently authenticate these objects, the use of physical unclonable functions (PUFs) has been introduced as a promising solution for the resource-constrained nature of these devices. The use of machine learning PUF models has been recently proposed to authenticate the IoT objects while reducing the storage space requirement for each device. Nonetheless, the use of a mathematically clonable PUFs requires careful design of the enrollment process. Furthermore, the secrecy of the machine learning models used for PUFs and the scenario of leakage of sensitive information to an adversary due to an insider threat within the organization have not been discussed. In this paper, we review the state-of-the-art model-based PUF enrollment protocols. We identity two architectures of enrollment protocols based on the participating entities and the building blocks that are relevant to the security of the authentication procedure. In addition, we discuss their respective weaknesses with respect to insider and outsider threats. Our work serves as a comprehensive overview of the ML PUF-based methods and provides design guidelines for future enrollment protocol designers.

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Section: Physical Unclonable Functionmentioning

confidence: 99%

Security Analysis of Machine Learning-Based PUF Enrollment Protocols: A Review

Khalfaoui

Leneutre

Villard³

et al. 2021

Sensors

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“…Applying multiple observations for key binding (and generation) has been studied from information-theoretic perspective in [13][14][15]. It is shown that the secret-key rate can be improved when multiple observations are used by the encoder or the decoder.…”

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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“…By using an additional private key, user's privacy-leakage can be made negligible [15,16]. Another scenario, that is, the BIS with one user, was extensively examined in [8,[17][18][19][20][21][22]. More precisely, in [17,18], the relation of secret-key and privacy-leakage rates was analyzed.…”

Section: Introductionmentioning

confidence: 99%

“…The works of [8] was extended to constrain the action cost for the decoder in [20], and to consider twoenrollment systems for the same hidden source, where the encoders do not trust each other [21]. Moreover, in [22], the secret-key capacity of a multi-enrollment system, in which the decoder is required to estimate all secret keys generated in the earlier enrollments, was formulated.…”

Section: Introductionmentioning

confidence: 99%

Biometric Identification Systems with Noisy Enrollment for Gaussian Sources and Channels

Yachongka

Yagi

Oohama

2021

Entropy

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In the present paper, we investigate the fundamental trade-off of identification, secret-key, storage, and privacy-leakage rates in biometric identification systems for remote or hidden Gaussian sources. We use a technique of converting the system to one where the data flow is in one-way direction to derive the capacity region of these rates. Also, we provide numerical calculations of three different examples for the system. The numerical results imply that it seems hard to achieve both high secret-key and small privacy-leakage rates simultaneously.

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

Cited by 25 publications

References 32 publications

Security Analysis of Machine Learning-Based PUF Enrollment Protocols: A Review

Security Analysis of Machine Learning-Based PUF Enrollment Protocols: A Review

Multiple Observations for Secret-Key Binding with SRAM PUFs

Biometric Identification Systems with Noisy Enrollment for Gaussian Sources and Channels

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