A tamper-proof and lightweight authentication scheme

Hammouri, Ghaith; Öztürk, Erdinç; Sunar, Berk

doi:10.1016/j.pmcj.2008.07.001

Cited by 41 publications

(37 citation statements)

References 20 publications

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“…Similar toÖztürk et al, Hammouri et al [18] employ again two strong PUFs, as shown in Figure 7(e). As before, both PUFs are modeled during enrollment via an auxiliary one-time interface.…”

Section: Hammouri Et Al (December 2008)mentioning

confidence: 78%

“…The server could send a list of true random numbers, requiring no additional IC logic, but resulting in a large communication overhead [18]. A small PRNG, such as a linear feedback shift register (LFSR), is often employed [10,18,31,40,46,59,61]. Challenge c is then used as seed value: r ← SPUF (PRNG(c)).…”

Section: Strong Puf Response Space Expansion (#5)mentioning

confidence: 99%

“…We consider the following list of strong PUF protocols, all described with a unified notation for ease of understanding: basic strong PUF authentication [45], controlled PUFs [15], Bolotnyy et al [4],Öztürk et al [44], Hammouri et al [18], Kulseng et al [33], Sadeghi et al [53], logically reconfigurable PUFs [29], reverse fuzzy extractors [59], the converse protocol [30], Lee et al I [35], Jin et al [23], slender PUFs [40], Xu et al [60], He et al [19], Jung et al [24], Lee et al II [36], noise bifurcation [61] and system of PUFs [31].…”

Section: Introductionmentioning

confidence: 99%

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A Survey on Lightweight Entity Authentication with Strong PUFs

et al. 2015

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Abstract. Physically unclonable functions (PUFs) exploit the unavoidable manufacturing variations of an integrated circuit (IC). Their input-output behavior serves as a unique IC 'fingerprint'. Therefore, they have been envisioned as an IC authentication mechanism, in particular the subclass of so-called strong PUFs. The protocol proposals are typically accompanied with two PUF promises: lightweight and an increased resistance against physical attacks. In this work, we review nineteen proposals in chronological order: from the original strong PUF proposal (2001) to the more complicated noise bifurcation and system of PUF proposals (2014). The assessment is aided by a unified notation and a transparent framework of PUF protocol requirements.

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Section: Hammouri Et Al (December 2008)mentioning

confidence: 78%

Section: Strong Puf Response Space Expansion (#5)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Survey on Lightweight Entity Authentication with Strong PUFs

et al. 2015

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“…The notion of PAC learning has already been used in the context of PUFs by Hammouri et al [45] to assess the security of their proposed scheme. In order to do so, they represent a noiseless Arbiter PUF as an LTF [7], as first proposed in [39,62].…”

Section: Comparison With Related Workmentioning

confidence: 99%

On the Learnability of Physically Unclonable Functions

Ganji¹

2018

T-Labs Series in Telecommunication Services

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Along with the expansion of the application of integrated circuits (ICs), the risk of security vulnerabilities of them has been rising dramatically. Diverse type of successful attacks against ICs have been launched by adversaries taking advantage of those vulnerabilities to expose confidential information, e.g., intellectual property (IP), and customer data. As a remedy for the shortcomings of traditional security measures taken to protect ICs, PUFs appear to be promising candidates that are intended to offer instance-specific functionality. While cryptographic mechanisms enjoying this privilege have been emerging, the vulnerability of PUFs to different types of attacks have been demonstrated. Among these attacks, a great deal of attention has been paid to machine learning (ML) attacks, aiming at modeling the challengeresponse behavior of PUFs. So far the success of ML attacks has relied on trial and error, and consequently, ad hoc attacks and their corresponding countermeasures have been developed.This thesis aims to address this issue by providing the mathematical proofs of the vulnerability of various PUF families, including Arbiter, XOR Arbiter, ring-oscillator, and bistable ring PUFs, to ML attacks. To achieve this goal, for the assessment of these PUFs a generic framework is developed that include two main approaches. First, with regard to the inherent physical characteristics of the PUFs mentioned above, fit-for-purpose mathematical representations of them are established, which adequately reflect the physical behavior of those primitives. To this end, notions and formalizations, being already familiar to the ML theory world, are reintroduced in order to give a better understanding of why, how, and to what extent ML attacks against PUFs can be feasible in practice. Second, polynomial time ML algorithms are explored, which can learn the PUFs under the appropriate representation. More importantly, in contrast to previous ML approaches, not only the accuracy of the model mimicking the behavior of the PUF but also the delivery of such a model is ensured by our framework.Besides off-the-shelf ML algorithms, we apply a set of algorithms originated in property testing field of study that can support the evaluation of the security of PUFs. They serve as a "toolbox", from which PUF designers and manufacturers can choose the indicators being relevant to their requirements. Last but not least, on the basis of learning theory concepts, this thesis explicitly states that the PUF families studied here cannot be considered as an ultimate solution to the problem of insecure ICs. Furthermore, we believe that this thesis can provide an insight into not only the academic research but also the design and manufacturing of PUFs. xi

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“…An excellent recent survey has been published which summarizes the state-of-the-art in PUF-based authentication protocols [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] for resource-constraint devices [29]. The authentication protocols covered by the survey are evaluated according to: (1) resilience to environmental noise; (2) resilience to machine learning attacks; (3) the need to expand the response space of the strong PUF and (4) resilience to protocol attacks.…”

mentioning

confidence: 99%

A Privacy-Preserving, Mutual PUF-Based Authentication Protocol

et al. 2016

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This paper describes an authentication protocol using a Hardware-Embedded Delay PUF called HELP. HELP derives randomness from within-die path delay variations that occur along the paths within a hardware implementation of a cryptographic primitive, such as AES or SHA-3. The digitized timing values which represent the path delays are stored in a database on a secure server (verifier) as an alternative to storing PUF response bitstrings. This enables the development of an efficient authentication protocol that provides both privacy and mutual authentication. The security properties of the protocol are analyzed using data collected from a set of Xilinx Zynq FPGAs.

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A tamper-proof and lightweight authentication scheme

Cited by 41 publications

References 20 publications

A Survey on Lightweight Entity Authentication with Strong PUFs

A Survey on Lightweight Entity Authentication with Strong PUFs

On the Learnability of Physically Unclonable Functions

A Privacy-Preserving, Mutual PUF-Based Authentication Protocol

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