Towards an Oscillator Based TRNG with a Certified Entropy Rate

Lubicz, David; Bochard, Nathalie

doi:10.1109/tc.2014.2308423

Cited by 24 publications

(9 citation statements)

References 9 publications

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“…7 This gives the ratio (period jitter over the mean period) σ T 0 = σf 0 = 1.6 0 / 00 , which is close to our measurements obtained by other more expensive methods in [19].…”

Section: B Experimental Resultssupporting

confidence: 83%

On the assumption of mutual independence of jitter realizations in P-TRNG stochastic models

Haddad

Téglia

Bernard

et al. 2014

Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE), 2014

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International audienceSecurity in true random number generation in cryptography is based on entropy per bit at the generator output. The entropy is evaluated using stochastic models. Several recent works propose stochastic models based on assumptions related to selected physical analog phenomena such as noise or jittery signal and on the knowledge of the principle of randomness extraction from the obtained analog signal. However, these assumptions simplify often considerably the underlying analog processes, which include several noise sources. In this paper, we present a new comprehensive multilevel approach, which enables to build the stochastic model based on detailed analysis of noise sources starting at transistor level and on conversion of the noise to the clock jitter exploited at the generator level. Using this approach, we can estimate proportion of the jitter coming only from the thermal noise, which is included in the total clock jitter

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“…7 This gives the ratio (period jitter over the mean period) σ T 0 = σf 0 = 1.6 0 / 00 , which is close to our measurements obtained by other more expensive methods in [19].…”

Section: B Experimental Resultssupporting

confidence: 83%

On the assumption of mutual independence of jitter realizations in P-TRNG stochastic models

Haddad

Téglia

Bernard

et al. 2014

Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE), 2014

View full text Add to dashboard Cite

show abstract

“…In our experimental setup, we have f 0 = 103MHz and according to the fit in Fig. 7 This gives the ratio (period jitter over the mean period) σ T 0 = σf 0 = 1.6 0 / 00 , which is close to our measurements obtained by other more expensive methods in [19].…”

Section: B Experimental Resultssupporting

confidence: 77%

On the assumption of mutual independence of jitter realizations in P-TRNG stochastic models

Haddad

Téglia

Bernard

et al. 2014

Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE), 2014

View full text Add to dashboard Cite

Security in true random number generation in cryptography is based on entropy per bit at the generator output. The entropy is evaluated using stochastic models. Several recent works propose stochastic models based on assumptions related to selected physical analog phenomena such as noise or jittery signal and on the knowledge of the principle of randomness extraction from the obtained analog signal. However, these assumptions simplify often considerably the underlying analog processes, which include several noise sources. In this paper, we present a new comprehensive multilevel approach, which enables to build the stochastic model based on detailed analysis of noise sources starting at transistor level and on conversion of the noise to the clock jitter exploited at the generator level. Using this approach, we can estimate proportion of the jitter coming only from the thermal noise, which is included in the total clock jitter.

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“…The single delay line method, doesn't filter out the global noise and thus overestimates the jitter variance by a factor of 5. The counter Another related work is the coherent sampling method for jitter measurement presented in [17]. We don't see any problems with this methodology and the reported results are similar to those obtained by the Differential Delay Line method.…”

Section: B Intel Fpgamentioning

confidence: 66%

On-chip jitter measurement for true random number generators

Yang

Rožić

Grujić

et al. 2017

2017 Asian Hardware Oriented Security and Trust Symposium (AsianHOST)

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Applications of true random number generators (TRNGs) span from art to numerical computing and system security. In cryptographic applications, TRNGs are used for generating new keys, nonces and masks. For this reason, a TRNG is an essential building block and often a point of failure for embedded security systems. One type of primitives that are widely used as source of randomness are ring oscillators. For a ring-oscillator-based TRNG, the true randomness originates from its timing jitter. Therefore, determining the jitter strength is essential to estimate the quality of a TRNG. In this paper, we propose a method to measure the jitter strength of a ring oscillator implemented on an FPGA. The fast tapped delay chain is utilized to perform the on-chip measurement with a high resolution. The proposed method is implemented on both a Xilinx FPGA and an Intel FPGA. Fast carry logic components on different FPGAs are used to implement the fast delay line. This carry logic component is designed to be fast and has dedicated routing, which enables a precise measurement. The differential structure of the delay chain is used to thwart the influence of undesirable noise from the measurement. The proposed methodology can be applied to other FPGA families and ASIC designs.

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Towards an Oscillator Based TRNG with a Certified Entropy Rate

Cited by 24 publications

References 9 publications

On the assumption of mutual independence of jitter realizations in P-TRNG stochastic models

On the assumption of mutual independence of jitter realizations in P-TRNG stochastic models

On the assumption of mutual independence of jitter realizations in P-TRNG stochastic models

On-chip jitter measurement for true random number generators

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