How to Generate Robust Keys from Noisy DRAMs?

Security primitives based on Dynamic Random Access Memory (DRAM) can provide cost-efficient and practical security solutions, especially for resource-constrained devices, such as hardware used in the Internet of Things (IoT), as DRAMs are an intrinsic part of most contemporary computer systems [1]. Over the past few years, DRAM-based physical unclonable functions became very popular among researchers in this field. However, similar to other types of PUFs, DRAM PUF reliability for authentication and key generation is highly dependent on its resistance against the environmental noises such as Temperature variation, Voltage variations, and Device aging. This paper addresses the challenges related to the reliability and robustness of DRAM PUFs under noisy environments. In this paper we apply a new approach (Quantization) that extracts keys from DRAM startup values with a high reliability and stability rate. This quantization technique identifies suitable features from power-up values of DRAM memories and quantize them into binary bits using tunable parameters that control and predict the environmental noises. Our experimental result shows a high reliability and min-entropy rate for relatively large number of DRAM key bits.