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Erschienen in:
Pine: Enabling Privacy-Preserving Deep Packet Inspection on TLS with Rule-Hiding and Fast Connection Establishment Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

PrivColl: Practical Privacy-Preserving Collaborative Machine Learning

verfasst von : Yanjun Zhang, Guangdong Bai, Xue Li, Caitlin Curtis, Chen Chen, Ryan K. L. Ko

Erschienen in: Computer Security – ESORICS 2020

Verlag: Springer International Publishing

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Abstract

Collaborative learning enables two or more participants, each with their own training dataset, to collaboratively learn a joint model. It is desirable that the collaboration should not cause the disclosure of either the raw datasets of each individual owner or the local model parameters trained on them. This privacy-preservation requirement has been approached through differential privacy mechanisms, homomorphic encryption (HE) and secure multiparty computation (MPC), but existing attempts may either introduce the loss of model accuracy or imply significant computational and/or communicational overhead.
In this work, we address this problem with the lightweight additive secret sharing technique. We propose PrivColl, a framework for protecting local data and local models while ensuring the correctness of training processes. PrivColl  employs secret sharing technique for securely evaluating addition operations in a multiparty computation environment, and achieves practicability by employing only the homomorphic addition operations. We formally prove that it guarantees privacy preservation even though the majority (\(n-2\) out of n) of participants are corrupted. With experiments on real-world datasets, we further demonstrate that PrivColl  retains high efficiency. It achieves a speedup of more than 45X over the state-of-the-art MPC-/HE-based schemes for training linear/logistic regression, and 216X faster for training neural network.

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Vorheriges Kapitel LegIoT: Ledgered Trust Management Platform for IoT
Nächstes Kapitel An Efficient 3-Party Framework for Privacy-Preserving Neural Network Inference
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Metadaten
Titel
PrivColl: Practical Privacy-Preserving Collaborative Machine Learning
verfasst von
Yanjun Zhang
Guangdong Bai
Xue Li
Caitlin Curtis
Chen Chen
Ryan K. L. Ko
Copyright-Jahr
2020
Buch
Computer Security – ESORICS 2020

Print ISBN: 978-3-030-58950-9

Electronic ISBN: 978-3-030-58951-6

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-58951-6_20

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