A scalable SIMD RISC-V based processor with customized vector extensions for CRYSTALS-kyber

This paper uses RISC-V vector extensions to speed up lattice-based operations in architectures based on HW/SW co-design. We analyze the structure of the number-theoretic transform (NTT), inverse NTT (INTT), and coefficient-wise multiplication (CWM) in CRYSTALS-Kyber, a lattice-based key encapsulation mechanism. We propose 12 vector extensions...

Label-Only Membership Inference Attack against \\Node-Level Graph Neural Networks

Graph Neural Networks (GNNs), inspired by Convolutional Neural Networks (CNNs), aggregate the message of nodes' neighbors and structure information to acquire expressive representations of nodes for node classification, graph classification, and link prediction. Previous studies have indicated that node-level GNNs are vulnerable to Membership...

Maximizing the Potential of Custom RISC-V Vector Extensions for Speeding up SHA-3 Hash Functions

SHA-3 is considered to be one of the most secure standardized hash functions. It relies on the Keccak-f[1 600] permutation, which operates on an internal state of 1 600 bits, mostly represented as a 5 x 5 x 64-bit matrix. While existing implementations process the state sequentially in chunks of typically 32 or 64 bits, the Keccak-f[1 600]...

FLAIRS: FPGA-Accelerated Inference-Resistant & Secure Federated Learning

Federated Learning (FL) has become very popular since it enables clients to train a joint model collaboratively without sharing their private data. However, FL has been shown to be susceptible to backdoor and inference attacks. While in the former, the adversary injects manipulated updates into the aggregation process; the latter leverages...