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5 papers · 1 filter
TAPFed: Threshold Secure Aggregation for Privacy-Preserving Federated Learning
Runhua Xu, Bo Li, Chao Li +3
Federated learning is a computing paradigm that enhances privacy by enabling multiple parties to collaboratively train a machine learning model without revealing personal data. How…
Safely Learning with Private Data: A Federated Learning Framework for Large Language Model
JiaYing Zheng, HaiNan Zhang, LingXiang Wang +3
Private data, being larger and quality-higher than public data, can greatly improve large language models (LLM). However, due to privacy concerns, this data is often dispersed in m…
Minotaur: Multi-Resource Blockchain Consensus
Matthias Fitzi, Xuechao Wang, Sreeram Kannan +4
Resource-based consensus is the backbone of permissionless distributed ledger systems. The security of such protocols relies fundamentally on the level of resources actively engage…
Secure Phrase Search for Intelligent Processing of Encrypted Data in Cloud-Based IoT
Meng Shen, Baoli Ma, Liehuang Zhu +2
Phrase search allows retrieval of documents containing an exact phrase, which plays an important role in many machine learning applications for cloud-based IoT, such as intelligent…
LPTD: Achieving Lightweight and Privacy-Preserving Truth Discovery in CIoT
Chuan Zhang, Liehuang Zhu, Chang Xu +3
In recent years, cognitive Internet of Things (CIoT) has received considerable attention because it can extract valuable information from various Internet of Things (IoT) devices.…