A Robust SRAM-PUF Key Generation Scheme Based on Polar Codes

Abstract

Physical unclonable functions (PUFs) are relatively new security primitives used for device authentication and device-specific secret key generation. In this paper we focus on SRAM-PUFs. The SRAM-PUFs enjoy uniqueness and randomness properties stemming from the intrinsic randomness of SRAM memory cells, which is a result of manufacturing variations. This randomness can be translated into the cryptographic keys thus avoiding the need to store and manage the device cryptographic keys. Therefore these properties, combined with the fact that SRAM memory can be often found in today's IoT devices, make SRAM-PUFs a promising candidate for securing and authentication of the resource-constrained IoT devices. PUF observations are always effected by noise and environmental changes. Therefore secret-generation schemes with helper data are used to guarantee reliable regeneration of the PUF-based secret keys. Error correction codes (ECCs) are an essential part of these schemes. In this work, we propose a practical error correction construction for PUF-based secret generation that are based on polar codes. The resulting scheme can generate 128-bit keys using 1024 SRAM-PUF bits and 896 helper data bits and achieve a failure probability of 10 −9 or lower for a practical SRAM-PUFs setting with bit error probability of 15%. The method is based on successive cancellation combined with list decoding and hash-based checking that makes use of the hash that is already available at the decoder. In addition, an adaptive list decoder for polar codes is investigated. This decoder increases the list size only if needed. AuthenticationResponse N Y PUF Encoder Enrollment Key Server Helper data PUF Device Authentication N X Database Decoder N Y Helper data PUF DeviceĤ ash( ) S Hash Hash( ) S Hash Key SŜ Hash( ) S Verification

Secret Key Agreement with Physical Unclonable Functions: An Optimality Summary

“…To satisfy the block-error probability requirement for each PUF realization, one could consider using the maximum distortion instead of 0 [6], n = 1408 Prev. Polar Code [44], n = 1024 Code 1, n = 1024 Code 2, n = 2048 Figure 10. Storage-key rates for the GS model with p A = 0.15.…”

“…achievable, n = 1024 FCS/COFE achievable, n = 2048 Best Code in[6], n = 1408 Prev. Polar Code[49], n = 1024 Code 1, n = 1024 Code 2, n = 2048 Storage-key rates for the GS model with p A = 0.15. The (R *…”

Securing Additive Manufacturing with Blockchains and Distributed Physically Unclonable Functions

“…A single-bit mismatch in a cryptographic key is not acceptable for most encryption protocols. Therefore, the use of error correcting methods [44], helper data [45][46][47], and fuzzy extractors [48,49] is needed to achieve the zero error level required. Error correcting schemes burden client devices, as they consume additional computing power to run fuzzy extraction and error correcting codes.…”

An Optimality Summary: Secret Key Agreement with Physical Unclonable Functions

“…We consider three best code constructions proposed for the GS and CS models, which are COFE and the polar code construction in [ 47 ] for the GS model, and FCS for the CS model, in order to compare them with the WZ-coding constructions. The FCS and COFE achieve only a single point on the key-leakage rate region boundary, i.e., and .…”

“…The storage rate of 1 bit/source-bit is decreased by using the polar code construction proposed in [ 47 ]. Nevertheless, this construction cannot achieve the key-leakage-storage region.…”