Efficient Helper Data Reduction in SRAM PUFs via Lossy Compression
Ye Wanga and Michael Orshanskyb
The University of Texas at Austin, Austin, TX, USA,
alhywang@utexas.edu
borshansky@utexas.edu
ABSTRACT
Fuzzy extractors used in PUF‐based key generation require storage of helper data in non‐volatile memory (NVM). The challenge of using SRAM PUF-based key generation on FP‐GAs is that high‐capacity NVM, such as Flash, is not available on chip. Only expensive one‐time‐programmable (OTP) memory with limited capacity, such as e‐fuses, can be utilized to store helper data. Our work allows a significant reduction of helper data size (HDS) through two innovative techniques. The first uses bit‐error‐rate (BER)‐aware lossy compression: by treating a fraction of reliable bits as unreliable, it effectively reduces the size of the reliability mask. Considering practical costs of error characterization, the second technique permits across‐temperature HDS minimization strategies based on bit‐selection (with or without subsequent compression) using room‐temperature only characterization. The method is based on stochastic concentration theory and allows eficiently forming confidence intervals for true worst‐case BER. We use it to enable lossy compression and key reconstruction with success arbitrarily close to certainty. Results show that compared to mask less alternative, the proposed algorithm achieves an up to 4:5X HDS reduction with only 60% raw bits. Compared to lossless compression, we achieve a further 25% total HDS reduction, at the cost of doubling the number of raw PUF bits, for a 128‐bit key. When bit‐specific across‐temperature characterization is not possible, our method achieves a significant 2:4X helper data reduction compared to the maskless alternative for extracting a 128-bit key and a 3X reduction for a 256‐bit key.
