Browsing by Author "Yin, Chi-En"
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Item A Group-Based Ring Oscillator Physical Unclonable Function(2012) Yin, Chi-En; Qu, Gang; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Silicon Physical Unclonable Function (PUF) is a physical structure of the chip which has functional characteristics that are hard to predict before fabrication but are expected to be unique after fabrication. This is caused by the random fabrication variations. The secret characteristics can only be extracted through physical measurement and will vanish immediately when the chip is powered down. PUF promises a securer means for cryptographic key generation and storage among many other security applications. However, there are still many practical challenges to cost effectively build secure and reliable PUF secrecy. This dissertation proposes new architectures for ring oscillator (RO) PUFs to answer these challenges. First, our temperature-aware cooperative (TAC) RO PUF can utilize certain ROs that were otherwise discarded due to their instability. Second, our novel group-based algorithm can generate secrecy higher than the theoretical upper bound of the conventional pairwise comparisons approach. Third, we build the first regression-based entropy distiller that can turn the PUF secrecy statistically random and robust, meeting the NIST standards. Fourth, we develop a unique Kendall syndrome coding (KSC) that makes the PUF secrecy error resilient against potential environmental fluctuations. Each of these methods can improve the hardware efficiency of the RO PUF implementation by 1.5X to 8X while improving the security and reliability of the PUF secrecy.Item Kendall Syndrome Coding (KSC) for Group-Based Ring-Oscillator Physical Unclonable Functions(2011-12-17) Yin, Chi-En; Qu, GangItem Obtaining Statistically Random Information from Silicon Physical Unclonable Functions(2013) Yin, Chi-En; Qu, GangSilicon physical unclonable functions (PUF) uti- lize the variation during silicon fabrication process to extract information that will be unique for each chip. There have been many recent approaches to how PUF can be used to improve security related applications. However, it is well-known that the fabrication variation has very strong spatial correlation1 and this has been pointed out as a security threat to silicon PUF. In fact, when we apply NIST’s statistical test suite for randomness [1] against the random sequences generated from a population of 125 ring oscillator (RO) PUFs [2] using classic 1-out-of-8 Coding [3], [4] and Neighbor Coding [5], none of them can pass all the tests. In this paper, we propose to decouple the unwanted systematic variation from the desired random variation through a regression-based distiller, where the basic idea is to build a model for the systematic variation so we can generate the random sequences only from the true random variation. Applying Neighbor Coding to the same benchmark data [2], our experiment shows that 2nd and 3rd order polynomials distill random sequences that pass all the NIST randomness tests. So does 4th order polynomial in the case of 1-out-of-8 Coding. Finally, we introduce two generic random sequence generation methods. The sequences they generate fail all the randomness tests, but with the help of our proposed polynomial distiller, all but one tests are passed. These results demonstrate that our method can provide statistically random PUF information and thus bolster the security characteristics of existing PUF schemes.Item A Regression-Based Entropy Distiller for RO PUFs(2011-12-17) Yin, Chi-En; Qu, Gang