21 citations · 53 across the 14 of their papers we have counts for
12 papers · 1 filter
The Power of Bamboo: On the Post-Compromise Security for Searchable Symmetric Encryption
Tianyang Chen, Peng Xu, Stjepan Picek +4
Dynamic searchable symmetric encryption (DSSE) enables users to delegate the keyword search over dynamically updated encrypted databases to an honest-but-curious server without los…
High Recovery with Fewer Injections: Practical Binary Volumetric Injection Attacks against Dynamic Searchable Encryption
Xianglong Zhang, Wei Wang, Peng Xu +2
Searchable symmetric encryption enables private queries over an encrypted database, but it also yields information leakages. Adversaries can exploit these leakages to launch inject…
MUDGUARD: Taming Malicious Majorities in Federated Learning using Privacy-Preserving Byzantine-Robust Clustering
Rui Wang, Xingkai Wang, Huanhuan Chen +4
Byzantine-robust Federated Learning (FL) aims to counter malicious clients and train an accurate global model while maintaining an extremely low attack success rate. Most existing…
ID-based self-encryption via Hyperledger Fabric based smart contract
Ilya Grishkov, Roland Kromes, Thanassis Giannetsos +1
This paper offers a prototype of a Hyperledger Fabric-IPFS based network architecture including a smart contract based encryption scheme that meant to improve the security of user'…
Combining ID's, Attributes, and Policies in Hyperledger Fabric
Daan Gordijn, Roland Kromes, Thanassis Giannetsos +1
This work aims to provide a more secure access control in Hyperledger Fabric blockchain by combining multiple ID's, attributes, and policies with the components that regulate acces…
Effect of Homomorphic Encryption on the Performance of Training Federated Learning Generative Adversarial Networks
Ignjat Pejic, Rui Wang, Kaitai Liang
A Generative Adversarial Network (GAN) is a deep-learning generative model in the field of Machine Learning (ML) that involves training two Neural Networks (NN) using a sizable dat…
FLVoogd: Robust And Privacy Preserving Federated Learning
Yuhang Tian, Rui Wang, Yanqi Qiao +2
In this work, we propose FLVoogd, an updated federated learning method in which servers and clients collaboratively eliminate Byzantine attacks while preserving privacy. In particu…
More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networks
Jing Xu, Rui Wang, Stefanos Koffas +2
Graph Neural Networks (GNNs) are a class of deep learning-based methods for processing graph domain information. GNNs have recently become a widely used graph analysis method due t…
HoneyCar: A Framework to Configure Honeypot Vulnerabilities on the Internet of Vehicles
Sakshyam Panda, Stefan Rass, Sotiris Moschoyiannis +3
The Internet of Vehicles (IoV), whereby interconnected vehicles communicate with each other and with road infrastructure on a common network, has promising socio-economic benefits…
PIVODL: Privacy-preserving vertical federated learning over distributed labels
Hangyu Zhu, Rui Wang, Yaochu Jin +1
Federated learning (FL) is an emerging privacy preserving machine learning protocol that allows multiple devices to collaboratively train a shared global model without revealing th…
Distributed Additive Encryption and Quantization for Privacy Preserving Federated Deep Learning
Hangyu Zhu, Rui Wang, Yaochu Jin +2
Homomorphic encryption is a very useful gradient protection technique used in privacy preserving federated learning. However, existing encrypted federated learning systems need a t…
Privacy-Preserving Genetic Relatedness Test
Emiliano De Cristofaro, Kaitai Liang, Yuruo Zhang
An increasing number of individuals are turning to Direct-To-Consumer (DTC) genetic testing to learn about their predisposition to diseases, traits, and/or ancestry. DTC companies…