Secure communication is being threatened by the foreseeable breakthrough of quantum computers. When a larger quantum computer is developed, traditional public key cryptography will be broken. Lattice-based cryptography appears as an alternative to protect the communications in the era of quantum computers. However, empowering current electronic devices with these new algorithms poses a challenging problem due to tight performance requirements as well as area and power constraints. Polynomial multiplication is the basic and most computationally intensive operation in lattice-based cryptosystems. The Number Theoretic Transform (NTT) is an attractive technique to perform polynomial multiplication efficiently. So far, previous works have focused on developing fast and compact forward and inverse NTT implementations. However, efficient and low-power NTT design has not been considered before although a low power consumption is crucial for many systems, such as battery-powered Internet of Things (IoT) devices. In this paper, we present the first low-power, fast and secure NTT ASIC design for lattice-based cryptography able to support different NTT parameters. The contribution of this work is three-fold. First, the implementation of a fast NTT through three optimization techniques. Second, utilization of methods for ASIC power minimization in the NTT design. Third, review of previously proposed side-channel attacks and discussion about countermeasures for our design. Our proposed architecture requires only n log(n) clock cycles for the forward and inverse NTT and can be implemented using a cheap single port RAM. The results of our work show that it is possible to decrease the power dissipation by more than 30% at nearly no cost.      «

Secure communication is being threatened by the foreseeable breakthrough of quantum computers. When a larger quantum computer is developed, traditional public key cryptography will be broken. Lattice-based cryptography appears as an alternative to protect the communications in the era of quantum computers. However, empowering current electronic devices with these new algorithms poses a challenging problem due to tight performance requirements as well as area and power constraints. Polynomial mu...     »