While recent years have witnessed the advancement in big data and artificial intelligence, it is of much importance to safeguard data privacy and security. As an innovative approach, federated learning (FL) addresses these concerns by facilitating collaborative model training across distributed data sourc...
结束那篇巨长的综述后,又开了篇综述坑(悲),但是这次标题顾名思义,是联邦学习安全和隐私方面的综述文章, A Survey on Security and Privacy of Federated Learning,于2020年发表于Future Generation Computer …
A survey on security and privacy of federated learning Authors Viraaji Mothukuri, Reza M. Parizi, Seyedamin Pouriyeh, Yan Huang, Ali Dehghantanha, Gau
Federated learning is a special kind of distributed learning framework, which allows multiple users to participate in model training while ensuring that their privacy is not compromised; however, this paradigm is still vulnerable to security and privacy threats from various attackers. This paper focuses...
论文名称:TAP: Transparent and Privacy-Preserving Data Services 中文:透明和保护隐私的数据服务 国内研究者:西南大学(第三作者) 如今,用户期望处理其数据的服务提供更高的安全性。除了传统的数据隐私和完整性要求外,他们还期望透明度,即服务对数据的处理可由用户和可信的审计员进行验证。研究者的目标是构建一个多用...
论文名称:TAP: Transparent and Privacy-Preserving Data Services 中文:透明和保护隐私的数据服务 国内研究者:西南大学(第三作者) 如今,用户期望处理其数据的服务提供更高的安全性。除了传统的数据隐私和完整性要求外,他们还期望透明度,即服务对数据的处理可由用户和可信的审计员进行验证。研究者的目标是构建一个多用...
While recent years have witnessed the advancement in big data and Artificial Intelligence (AI), it is of much importance to safeguard data privacy and security. As an innovative approach, Federated Learning (FL) addresses these concerns by facilitating collaborative model training across distributed dat...
In a federated learning scenario where multiple parties jointly learn a model from their respective data, there exist two conflicting goals for the choice of appropriate algorithms. On one hand, private and sensitive training data must be kept secure as much as possible in the presence of extit...
In medical domains, FL can be utilized to keep patient data private and enhance ML capabilities in assisting medical practitioners similar to the work in [20] which demonstrates the benefits of FL in the medical domain. As for application use-cases in the medical domain, the attack detection ...
5.1 Privacy Threats in Trustworthy Federated Learning 5.1.1 Data & Label Leakage 数据攻击和标签攻击,就与重建攻击一样常见,即重建数据集和标签域 基于梯度的数据泄露:梯度可以泄露隐私信息,只需要将权重梯度与偏置常数梯度相除即可,也可以用梯度优化新的GAN来生成原始数据,就是需要较长时间的训练 ...