通过联邦学习提高安全性

Hema Priya.N, Adithya Harish S M, S. S, P. Rathika
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引用次数: 2

摘要

数据泄露是指有意或无意地将稳定或个人数据传输给外部接收者。移动社区的这种泄漏增加了编译的机会。因此,安全数据的加密和存储必须通过使用一些技术来完成。联邦学习(FL)属于分布式机器学习,它有助于将客户的私有数据保存在各种设备上,因为集中式模型只接收权重更新。通过使用深度神经网络中开发的权重等技术分析客户提交的属性,敏感的私人数据可以开放访问。为了有效地防止统计数据泄露,本研究分析了一种使用差分隐私(DP)的新框架,其中在聚合之前将合成噪声提供给客户端的参数,即FLAGnoise(带噪声聚合的FL)。系统分析由客户端信息组成的数据集。然后应用高级加密标准(AES)算法和差分隐私的联邦学习。结果表明,联邦学习模型的隐私性优于差分隐私模型,准确率为97.3%。
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Improving Security with Federated Learning
Data leakage is the intentional or unintended transmission of stable or personal data to outside recipient. Such leakage in mobile community increases the chance of compilation. Hence encryption and storage of the secure data must be accomplished by usage of a few techniques. Federated learning (FL), which falls under distributed machine learning, helps preserve clients’ private data on various device as the centralized model receives only weight updates. Sensitive private data is open for access by analyzing submitted attributes from clients using techniques like weights developed in deep neural networks. To effectively preserve statistics from leakage, this study analyzes a novel framework using differential privacy (DP), in which synthetic noises are provided to parameters on the customers' side prior to aggregation, FLAGnoise (FL with noise aggregated).The system analyses the dataset consisting of information about the client. Federated learning with Advanced Encryption Standard (AES) algorithm and Differential privacy is then applied. It is found that the Federated learning model have better privacy than the Differential privacy model and gives the accuracy of 97.3%.
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