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Identifying and protecting against a computer security threat while preserving privacy of individual client devices using differential privacy machine learning for streaming data

机译:识别和保护计算机安全威胁,同时使用差分隐私机器学习进行流数据来保护各个客户端设备的隐私

摘要

Identifying and protecting against a computer security threat while preserving privacy of individual client devices using differential privacy machine learning for streaming data. In some embodiments, a method may include receiving first actual data values streamed from one or more first local client devices, generating first perturbed data values by adding noise to the first actual data values using a differential privacy mechanism, storing the first perturbed data values, training a machine learning classifier using the first perturbed data values, receiving a second actual data value streamed from a second local client device, generating a second perturbed data value by adding noise to the second actual data value, storing the second perturbed data value, identifying a computer security threat to the second local client device using the second actual data value as input to the trained machine learning classifier, and protecting against the computer security threat.
机译:针对计算机安全威胁识别和保护,同时使用差分隐私机器学习来保护各个客户端设备的隐私进行流式传输数据。在一些实施例中,一种方法可以包括接收从一个或多个第一本地客户端设备流流的第一实际数据值,通过使用差分隐私机制将噪声添加到第一实际数据值,存储第一扰动数据值,使用第一扰动数据值训练机器学习分类器,接收来自第二本地客户端设备的第二实际数据值,通过向第二实际数据值添加噪声来生成第二扰动数据值,存储第二扰动数据值,识别计算机安全威胁对第二本地客户端设备使用第二实际数据值作为输入到培训的机器学习分类器,并保护计算机安全威胁。

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