Lightweight and secure PUF key storage using limits of machine learning

• Main Authors: Yu, Meng-Day (Mandel) (Author), M'Raihi, David (Author), Sowell, Richard (Author), Devadas, Srinivas (Contributor)
• Other Authors: Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor)
• Format: Article
• Language: English
• Published: Springer Berlin / Heidelberg, 2012-10-10T18:38:19Z.
• Subjects: Article
• Online Access: Get fulltext

13th International Workshop, Nara, Japan, September 28 - October 1, 2011. Proceedings
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 65nm FPGA and 0.13μ 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.