Approximate Computing for Privacy in IoT devices

Privacy is an increasingly essential requirement for modern computing systems. Privacy requires true random numbers often generated using dedicated hardware. We show how approximate computing can be used for generating random numbers. We demonstrate a proof of concept using a system with an approximate adder. We show that there is a fundamental synergy between approximate computing and random number generation, allowing other approximate computing techniques to also be used for random number generation on resource constrained devices.