LAUSR

Internet of Things (IoT) sensor nodes are placed ubiquitously to collect information, which is then vulnerable to malicious attacks. For instance, adversaries can perform side channel attack on the sensor nodes to recover the symmetric key for encrypting IoT data. Refreshing the symmetric key frequently can reduce the risk of compromised keys. However, the number of sensor nodes connected to the gateway and cloud server is massive. Refreshed symmetric keys need to be sent to gateway devices and cloud server frequently with a secure key encapsulation mechanism (KEM), which is time-consuming. In this article, novel and efficient implementation techniques are proposed to accelerate Kyber, a post-quantum KEM, on a Graphics Processing Unit (GPU). Fully parallel implementation of number theoretic transform (NTT) with combined levels is presented, which is 2.65× faster than state-of-the-art result on a GPU. Other proposed techniques include parallel rejection sampling, central binomial distribution with coalesced memory access and parallel fine-grain AES-256. These techniques enable high throughput performance with 162760 encapsulations/second and 107631 decapsulations/second on an RTX2060 GPU. This is also the first fine grain implementation of post-quantum KEM (Kyber) on a GPU, which can be used to offer key encapsulation/decapsulation as a service to reduce the burden on IoT systems.