Silicon physical random functions

Gassend, Blaise; Clarke, Dwaine; Dijk, Marten van; Devadas, Srinivas

doi:10.1145/586131.586132

Cited by 292 publications

(389 citation statements)

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“…Similarly to the case of the V DD noise, if V N1 is higher or lower than V N2 , it is still higher or lower even when the ground noise is involved. 1 This is a very important advantage over the previous circuit in Fig. 1 where the output voltage can be reversed by the noises on the power supply rails as explained in the previous section.…”

Section: Proposed Puf Circuitmentioning

confidence: 96%

“…However, if the ground noise is modeled as in Fig. 8(c) Proposed random bit extraction circuit using differential amplifiers 1 Even with finite CMRR, the voltage difference between nodes N1 and N2 in Fig. 7 can be reduced, but the polarity cannot be reversed.…”

Section: Simulationsmentioning

confidence: 99%

“…Thus circuits with the same layout may generate outputs with unpredictable deviations. If the outputs of the circuits provide a sufficient level of randomness and they are unique to each of the circuits, then these circuits may be viewed as hardware fingerprints for each chip and they are called physical unclonable functions (PUFs) [1,2]. Because PUFs can play a role of a unique identifier for each chip without any expensive external programming or special manufacturing step [3], they can be used for low-cost identification applications including radio frequency identification (RFID).…”

Section: Introductionmentioning

confidence: 99%

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Integrated circuit design for physical unclonable function using differential amplifiers

Choi

Kim

Lee

et al. 2010

Analog Integr Circ Sig Process

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A physical unclonable function (PUF) based on process variations on silicon wafers is a very promising technology which finds various applications in identification and authentication, but only a few integrated circuits have been reported so far. As those circuits are vulnerable to power supply noises, switching noises and environmental variations, they lead to a reliability issue such as time-varying or metastable responses. To resolve this issue, this letter proposes a new integrated circuit design for PUFs using differential amplifiers. The feasibility of the proposed circuit has been theoretically analyzed and validated through HSPICE simulations for the previous and proposed circuits.

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Section: Proposed Puf Circuitmentioning

confidence: 96%

Section: Simulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Integrated circuit design for physical unclonable function using differential amplifiers

Choi

Kim

Lee

et al. 2010

Analog Integr Circ Sig Process

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“…PUFs are embodied into a device physically [27]. When queried with a challenge c, the PUF generates a response r depending on both the physical properties of the object containing PUF and the challenge c [28,29]. This dependency is generally called as challenge-response behavior of the PUF.…”

Section: Physically Unclonable Functions (Pufs)mentioning

confidence: 99%

Providing destructive privacy and scalability in RFID systems using PUFs

Akgün

Caglayan

2015

Ad Hoc Networks

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“…Physically unclonable functions (PUFs), as introduced by Gassend et al [9], have attracted a great deal of research interest due to their potential enhancement of numerous applications. These applications are ranging from hardware fingerprinting and authentication [28,33] to secure storage for encryption mechanisms [16,30].…”

Section: Introductionmentioning

confidence: 99%

PAC learning of arbiter PUFs

2016

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The general concept of physically unclonable functions (PUFs) has been nowadays widely accepted and adopted to meet the requirements of secure identification and key generation/storage for cryptographic ciphers. However, shattered by different attacks, e.g., modeling attacks, it has been proved that the promised security features of arbiter PUFs, including unclonability and unpredictability, are not supported unconditionally. However, so far the success of existing modeling attacks relies on pure trial and error estimates. This means that neither the probability of obtaining a useful model (confidence), nor the sufficient number of CRPs, nor the probability of correct prediction (accuracy) is guaranteed. To address these issues, this work presents a probably approximately correct (PAC) learning algorithm. Based on a crucial discretization process, we are able to define a Deterministic finite automaton (of polynomial size), which exactly accepts the regular language corresponding to the challenges mapped by the given PUF to one responses.

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Silicon physical random functions

Cited by 292 publications

References 0 publications

Integrated circuit design for physical unclonable function using differential amplifiers

Integrated circuit design for physical unclonable function using differential amplifiers

Providing destructive privacy and scalability in RFID systems using PUFs

PAC learning of arbiter PUFs

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