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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Lightweight and Secure PUF Key Storage Using Limits of Machine Learning
verfasst von : Meng-Day (Mandel) Yu, David M’Raihi, Richard Sowell, Srinivas Devadas
Erschienen in: Cryptographic Hardware and Embedded Systems – CHES 2011
Verlag: Springer Berlin Heidelberg
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A lightweight and secure key storage scheme using silicon Physical Unclonable Functions (PUFs) is described. To derive stable PUF bits from chip manufacturing variations, a lightweight error correction code (ECC) encoder / decoder is used. With a register count of 69, this codec core does not use any traditional error correction techniques and is 75% smaller than a previous provably secure implementation, and yet achieves robust environmental performance in 65
nm
FPGA and 0.13
μ
ASIC implementations. The security of the syndrome bits uses a new security argument that relies on what
cannot
be learned from a machine learning perspective. The number of
Leaked Bits
is determined for each Syndrome Word, reducible using
Syndrome Distribution Shaping
. The design is secure from a min-entropy standpoint against a machine-learning-equipped adversary that, given a ceiling of leaked bits, has a classification error bounded by
ε
. Numerical examples are given using latest machine learning results.