Lightweight (Reverse) Fuzzy Extractor With Multiple Reference PUF Responses

Abstract

A Physical unclonable functions (PUF), alike a fingerprint, exploits manufacturing randomness to endow each physical item with a unique identifier. One primary PUF application is the secure derivation of volatile cryptographic keys using a fuzzy extractor comprising of two procedures: i) secure sketch; and ii) entropy extraction. Although the entropy extractor can be lightweight, the overhead of the secure sketch responsible correcting naturally noisy PUF responses is usually costly. We observe that, in general, response unreliability with respect to a enrolled reference measurement increases with increasing differences between the in-the-field PUF operating condition and the operating condition used in evaluating the enrolled reference response. For the first time, we exploit such an important but inadvertent observation. In contrast to the conventional single reference response enrollment, we propose enrolling multiple reference responses (MRR) subject to the same challenge but under multiple distinct operating conditions. The critical observation here is that one of the reference operating conditions is likely to be closer to the operating condition of the field deployed PUF, thus, resulting in minimizing the expected unreliability when compared to the single reference under the nominal condition. Overall, MRR greatly reduces the demand for the expected number of erroneous bits for correction and, subsequently, achieve a significant reduction in the error correction overhead. The significant implementation efficiency gains from the proposed MRR method is demonstrated from software implementations of fuzzy extractors on batteryless resource constraint computational radio frequency identification devices, where realistic PUF data is collected from the embedded intrinsic SRAM PUFs.

Building Secure SRAM PUF Key Generators on Resource Constrained Devices

“…Gao et al recently proposed the multiple reference responses (MRR) enrollment strategy [26]. This method is especially suitable in a reverse fuzzy extractor setting to significantly reduce the Gen() function implementation overhead on a token [26]. The MRR rationale is described below in the context of a reverse fuzzy extractor, dubbed MR 3 FE.…”

“…Our study uses the family of BCH(n, k, t) linear codes with a syndrome based decoding strategy to realize a reverse fuzzy extractor considering its popularity [20], [23] and its security [23], [26], [27]. Here, n is the codeword length, k is the code size, t is the number of errors that can be corrected within this n-bit block.…”

Prototype Device With Lightweight Protocol for Secure RFID Communication Without Reliable Connectivity

“…Since the eHealth scenario is specifically targeted for adverse conditions, we chose not to use PUFs in our design. Current research is showing promise in mitigating this issue [30], [31].…”

“…Presumably, the EEPROM would be ''in the box'' rather than an external device. If not an EEPROM, perhaps a physically unclonable function (PUF) [30], [31], could be used.…”

A Highly Reliable Arbiter PUF With Improved Uniqueness in FPGA Implementation Using Bit-Self-Test

“…After the preselection process, the error rate will decrease, and thus the error correction becomes less complex or even unnecessary [20], [21]. A simple method to determine the stable responses is measuring them multiple times across different operating conditions [22], but this requires significant additional runtime and costs. The more efficient approaches are run test on the PUF cells and the response that has a mismatch exceeding a predefined limit is selected.…”