Results 91 to 100 of about 892 (280)

An In-Depth Analysis on Efficiency and Vulnerabilities on a Cloud-Based Searchable Symmetric Encryption Solution

Journal of Informatics and Web Engineering
Searchable Symmetric Encryption (SSE) has come to be as an integral cryptographic approach in a world where digital privacy is essential. The capacity to search through encrypted data whilst maintaining its integrity meets the most important demand for ...
Prithvi Chaudhari, Ji-Jian Chin, Soeheila Moesfa Mohamad   +2 more
doaj   +1 more source

Dynamic Searchable Symmetric Encryption with Physical Deletion and Small Leakage

, 2017
Dynamic Searchable Symmetric Encryption (DSSE) allows a client not only to search over ciphertexts as the traditional search- able symmetric encryption does, but also to update these ciphertexts according to requirements, e.g., adding or deleting some ...
Willy Susilo (19588021), Peng Xu (20118750), Qianhong Wu (20120463), Shuai Liang (1706497), Wei Wang (17594), Hai Jin (507244)   +5 more
core  

Multi-client Cloud-based Symmetric Searchable Encryption

, 2021
We propose a multi-client Symmetric Searchable Encryption (SSE) scheme based on the single-user protocol (Cash et al., CRYPTO 2013). The scheme allows any user to generate a search query by interacting with any θ-1 (θ is a threshold parameter) ‘helping ...
Surya Nepal (9558705), Shabnam Kasra Kermanshahi (20068878), Joseph Liu (440983), Shangqi Lai (20070045), Randolph Loh (20070048), Cong Zuo (20070051), Ron Steinfeld (13505434)   +6 more
core  

FIRE‐GNN: Force‐Informed, Relaxed Equivariance Graph Neural Network for Rapid and Accurate Prediction of Surface Properties

Advanced Intelligent Discovery, EarlyView.
This study introduces FIRE‐GNN, a force‐informed, relaxed equivariant graph neural network for predicting surface work functions and cleavage energies from slab structures. By incorporating surface‐normal symmetry breaking and machine learning interatomic potential‐derived force information, the approach achieves state‐of‐the‐art accuracy and enables ...
Circe Hsu, Claire Schlesinger, Karan Mudaliar, Jordan Leung, Robin Walters, Peter Schindler   +5 more
wiley   +1 more source

SEAC: dynamic searchable symmetric encryption with lightweight update-search permission control

Cybersecurity
Sharing electronic protected health information (ePHI) is highly beneficial in public affairs. Constructing a cloud-assisted ePHI retrieval service represents a modern strategy that enhances cost-effectiveness and efficiency, making it a promising ...
Zhuobin Hu, Jiabei Wang, Zhengkai Chen, Zhaoxuan Ge, Mingyu Bian, Lei Chen, Yongbin Zhou   +6 more
doaj   +1 more source

A Unifying Approach to Self‐Organizing Systems Interacting via Conservation Laws

Advanced Intelligent Discovery, EarlyView.
The article develops a unified way to model and analyze self‐organizing systems whose interactions are constrained by conservation laws. It represents physical/biological/engineered networks as graphs and builds projection operators (from incidence/cycle structure) that enforce those constraints and decompose network variables into constrained versus ...
F. Barrows, G. Zhang, S. Anand, Z. Chen, J. Lin, A. Desai, S. Martiniani, F. Caravelli   +7 more
wiley   +1 more source

Gaussian Process Regression–Neural Network Hybrid with Optimized Redundant Coordinates: A New Simple Yet Potent Tool for Scientist's Machine Learning Toolbox

Advanced Intelligent Discovery, EarlyView.
A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

When Biology Meets Medicine: A Perspective on Foundation Models

Advanced Intelligent Discovery, EarlyView.
Artificial intelligence, and foundation models in particular, are transforming life sciences and medicine. This perspective reviews biological and medical foundation models across scales, highlighting key challenges in data availability, model evaluation, and architectural design.
Kunying Niu, Yuxing Wang, Limin Zou, Jing Huang   +3 more
wiley   +1 more source

A Robust Deep Temporal Causal Discovery Platform for Single‐Cell Gene Regulatory Network Reconstruction

Advanced Intelligent Discovery, EarlyView.
scTIGER2.0 is a deep‐learning framework that infers gene regulatory networks from single‐cell RNA sequencing data. By integrating correlation, pseudotime ordering, deep learning and bootstrap‐based significance testing, it reduces false positives and reveals directional gene interactions.
Nishi Gupta, Madison Dautle, Shaoqiang Zhang, Yong Chen   +3 more
wiley   +1 more source