Secure Multi-party Computation (MPC) allows a set of mutually distrusting parties to jointly
evaluate a function on their private inputs while maintaining input privacy. In this work, we …

Multiplication is one of the essential functions in all digital systems. The evaluation of digital
system, have brought out new challenges in VLSI (Very Large Scale Integration) designing …

Privacy-preserving machine learning (PPML) has many applications, from medical image
classification and anomaly detection to financial analysis. nGraph-HE enables data …

Recently, a number of backdoor attacks against Federated Learning (FL) have been
proposed. In such attacks, an adversary injects poisoned model updates into the federated …

The concept of using Lookup Tables (LUTs) instead of Boolean circuits is well-known and
been widely applied in a variety of applications, including FPGAs, image processing, and …

Approximate multipliers are used in error-tolerant applications, sacrificing the accuracy of
results to minimize power or delay. In this paper we investigate approximate multipliers …

Secure Multi-party Computation (MPC) allows to securely compute on private data. To make
MPC practical, logic synthesis can be used to automatically translate a description of the …

Multi-party computation (MPC) allows two or more parties to jointly and securely compute
functions over private inputs. Cryptographic protocols that realize MPC require functions to …

The concept of using Lookup Tables (LUTs) instead of Boolean circuits is well-known and
been widely applied in a variety of applications, including FPGAs, image processing, and …

The primary requirement for multimedia applications is energy efficient design along with
computational efficiency of portable devices. This work proposes an energy efficient MAC …