保护隐私数据访问的联邦学习

Małgorzata Śmietanka, Hirsh Pithadia, P. Treleaven
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引用次数: 7

摘要

联邦学习是一种开创性的隐私保护数据技术,也是一种基于分布式数据集训练的新型机器学习模型。公司收集大量的历史和实时数据来推动他们的业务并与其他组织合作。然而,由于法规(例如欧盟GDPR)以及保护其敏感和个人数据的需要,数据隐私变得越来越重要。公司需要管理数据访问:首先是在组织内部(这样他们就可以控制员工访问),其次是在与第三方合作时保护原始数据。更重要的是,越来越多的公司希望将他们收集的数据“货币化”。然而,在新的立法下,利用不同组织的数据变得越来越困难(Yu, 2016)。由Google首创的联邦学习是一种新兴的隐私保护数据技术,也是一种新型的分布式机器学习模型。本文讨论了联邦学习作为隐私保护数据访问和分布式机器学习应用于分布式数据集的解决方案。它还提供了一个保护隐私的联邦学习基础设施。
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Federated Learning for Privacy-Preserving Data Access
Federated learning is a pioneering privacy-preserving data technology and also a new machine learning model trained on distributed data sets. Companies collect huge amounts of historic and real-time data to drive their business and collaborate with other organisations. However, data privacy is becoming increasingly important because of regulations (e.g. EU GDPR) and the need to protect their sensitive and personal data. Companies need to manage data access: firstly within their organizations (so they can control staff access), and secondly protecting raw data when collaborating with third parties. What is more, companies are increasingly looking to ‘monetize’ the data they’ve collected. However, under new legislations, utilising data by different organization is becoming increasingly difficult (Yu, 2016). Federated learning pioneered by Google is the emerging privacy- preserving data technology and also a new class of distributed machine learning models. This paper discusses federated learning as a solution for privacy-preserving data access and distributed machine learning applied to distributed data sets. It also presents a privacy-preserving federated learning infrastructure.
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