SRAM PUF.
Building hardware security primitives with on-device memory fingerprints is a compelling proposition given the ubiquity of memory in electronic devices, especially for low-end Internet of Things devices for which cryptographic modules are often unavailable. However, the use of fingerprints in security functions is challenged by the small, but unpredictable variations in fingerprint reproductions from the same device due to measurement noise. Our study formulates a novel and pragmatic approach to achieve highly reliable fingerprints from device memories.
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The simplicity of deployment and perpetual operation of energy harvesting devices provides a compelling proposition for a new class of edge devices for the Internet of Things. In particular, Computational Radio Frequency Identification (CRFID) devices are an emerging class of battery-free, computational, sensing enhanced devices that harvest all of their energy for operation.
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