Manhattan distance is mainly used to calculate the total absolute wheelbase of two points in
the standard coordinate system. The secure computation of Manhattan distance is a new …

At present, society has entered the era of digital finance, and the information management
system (IMS) of financial services has been developing rapidly, so the security of data has …

Incremental learning algorithms have been developed as an efficient solution for fast
remodeling in Broad Learning Systems (BLS) without a retraining process. Even though the …

Efforts to counter COVID-19 reaffirmed the importance of rich medical, behavioral, and
sociological data. To make data available to many researchers who can conduct statistical …

Background: There is a lot of discussion in EU politics about trust in artificial intelligence (AI).
Because it can be used as a lethal weapon we need (EU) regulations that take care of …

Addressing privacy concerns is critical in smart manufacturing where sensitive data is used
for machine learning. Data protection is essential to ensure model accuracy while upholding …

Abstract VANETS (IoVs), banks, and healthcare records are the sensitive information of
vehicles, clients, and patients that is stored and maintained electronically, which has …

Deep learning (DL) has been shown to be very effective for many application domains of
machine learning (ML), including image classification, voice recognition, natural language …

The sensitive information of participants would be leaked to an untrustworthy server through
gradients in federated learning. Encrypted aggregation of uploaded parameters could …

Secure multi-party computation (SMPC) is a crucial technology that supports privacy
preservation, enabling multiple users to perform computations on any function without …