Mobile crowdsourcing (MCS) is a promising new paradigm for intelligent data perception in
large-scale sensor applications such as the Industrial Internet of Things (IIoT). This approach …

Well-trained neural network models are deployed on edge servers to provide valuable
inference services for clients. To protect data privacy, a promising way is to exploit various …

We present a method to increase the dependability of cloud-based applications. Traditional
Secret Sharing Schemes (SSSs) typically fail to counter the challenges brought on by …

Mobile edge computing provides relatively rich computation resources for Internet-of-Things
(IoT) task offloading at the edge of networks. As time goes on, user tasks present diverse …

In cloud environment, designing the efficient outsourced calculation protocols to serve the
machine learning or data mining is a hot topic. At the same time, homomorphic encryption …

This study investigates the optimal selection of parameters for collaborative clustering while
ensuring data privacy. We focus on key clustering algorithms within a collaborative …

Secure inference in a deep-learning-as-a-service setting (DLaaS) can effectively protect
sensitive data of the client and server model parameters. However, various nonlinear …

This research aims to determine the direction of research in the analysis of mixed data types.
The world is currently filled with increasingly diverse data, especially in terms of data types …

Pasar tradisional merupakan salah satu pusat kegiatan ekonomi yang penting di Kota
Pasuruan. Bagi pedagang, pemilihan tempat berjualan yang strategis dan sesuai dengan …

Privacy-Preserving Federated Learning (PPFL), a new paradigm for secure and efficient
Federated Learning, has the advantage of security aggregation without losing accuracy …