Further Analysis of PRNG-Based Key Derivation Functions
The Internet of Things (IoT) is growing at a rapid pace. With everyday applications and services becoming wirelessly networked, security still is a major concern. Many of these sensors and devices have limitations, such as low power consumption, reduced memory storage, and reduced fixed point processing capabilities. Therefore, it is imperative that high-performance security primitives are used to maximize the lifetime of these devices while minimally impacting memory storage and timing requirements. Previous work presented a residue number system (RNS)-based pseudorandom number generator (PRNG)-based key derivation function (KDF) (PKDF) that showed good initial energy-efficient performance for the IoT devices. This paper provides additional analysis on the PRNG-based security and draws a comparison to a current industry-standard KDF. Subsequently, embedded software implementations were performed on an MSP430 and MSP432 and compared with the transport layer security (TLS) 1.3 hash-based message authentication code (HMAC) key derivation function (HKDF); these results demonstrate substantial computational savings for the PKDF approach, while both pass the NIST randomness quality tests. Finally, hardware translation for the PKDF is evaluated through the Mathworks' HDL Coder toolchain and mapping for throughput and die area approximation on an Intel (R) Arria 10 FPGA.