An unbiased response debiasing structure and generation method based on bit self-test puf

Abstract

The present invention relates to information security technology, in particular to an unbiased response debiasing structure and method based on bit self-checking PUF, including a debiasing PUF coding structure and a debiasing PUF decoding structure, and the debiasing PUF coding structure includes a bit self-checking PUF and a debiased data encoder, the debiased PUF decoding structure includes a bit self-test PUF and a debiased data decoder; the debiased stimulus C generates a response R and a reliable bit flag F through a bit self-test PUF, and the response R and a reliable bit flag F generates an unbiased response Y and auxiliary data D through a debiased data encoder; the auxiliary data D restores an unbiased response Y through a debiased data decoder; the stimulus C generates a noised response R' by inputting a noisy bit self-test PUF , the auxiliary data D and the noise response R' are input to the debiased data decoder, and the debiased response Y is restored. This method eliminates the bias problem existing in the PUF response, enhances the unpredictable characteristics of the PUF, and greatly improves the deviation correction efficiency.

Description

technical field [0001] The invention belongs to the technical field of information security, and in particular relates to an unbiased response debiasing structure and a generating method based on bit self-checking PUF. Background technique [0002] Various cryptographic algorithms are commonly used in embedded systems to encrypt and protect and authenticate data, and the security of keys is particularly important. However, since the key is generally stored in a non-volatile memory, an attacker can use physical detection to directly obtain the key in the memory, which greatly threatens the security of the entire information system. An effective protection method uses a physical unclonable function (PUF) to generate and store keys, and uses its unclonable characteristics to prevent the keys from being stolen. [0003] Reliability and unpredictability are two important properties of PUFs. Reliability indicates the consistency of responses obtained from repeated experiments wi...

AI Technical Summary

Problems solved by technology

Von Neumann random extractor, because on average every four bits get one bit of debiased data, so this kind of random extractor has relatively high overhead in debiased data processing, and the efficiency of obtaining unbiased data is low

And, since PUFs are realized with uncontrollable process variations, it is difficult to fabricate PUFs with fully reliable and uniform response

Therefore, when the original response is unreliable, error correction mechanisms such as fuzzy extractor (FE) (Fuzzy Extractor), index syndrome technology (Index-based syndrome, IBS) or pattern matching (Pattern Matching) are usually used to eliminate noise. This will incur a large error correction overhead

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