LWE-based lossless computational fuzzy extractor for the Internet of Things

With the advent of the Internet of Things, lightweight devices necessitate secure and cost-efficient key storage. Since traditional secure key storage is expensive, novel solutions have been developed based on the idea of deriving the key from noisy entropy sources. Such sources when combined with fuzzy extractors allow cryptographically strong key derivation. Information theoretic fuzzy extractors require large amounts of input entropy to account for entropy loss in the key extraction process. It has been shown by Fuller et al. (ASIACRYPT'13) that the entropy loss can be reduced if the security requirement is relaxed to computational security based on the hardness of the Learning with Errors problem. We present the first implementation of a lossless computational fuzzy extractor (CFE) where the entropy of the source equals the entropy of the key. We explore efficiency and complexity design trade-offs for a system based on the implementation of a lossless CFE on a constrained device. To investigate the limits of the construction, we choose as implementation platforms a very constrained 8-bit AVR microcontroller device, as well as a 32-bit ARM Cortex-M3 microcontroller device. The latter speeds up the clients generate procedure from 34.9 to 0.4 seconds. We also show how to reduce the memory footprint of the algorithms proposed by Fuller et al. Our implementation requires only 1.45KB of SRAM and 9.8KB of Flash memory on an 8-bit microcontroller. Our evaluation indicates that it is feasible to implement such CFE schemes in highly constrained environments.