Abstract: Development of machine learning models and applications tends to be iterative and complex, made even harder because most of the necessary tools are not built for the entire machine learning lifecycle. Introduced here is a data platform that is able to accelerate time-to-value by enabling users to utilize applied machine learning prototypes (“AMPs”) made by others. These AMPs may be extendable, by the data platform, to new datasets, allowing machine learning to be developed and deployed more rapidly.

Abstract: Identifying and protecting against a computer security threat while preserving privacy of individual client devices using differential privacy for text documents. In some embodiments, a method may include receiving, at the remote server device, text documents from one or more local client devices, generating, at the remote server device, a differential privacy document vector for each of the text documents, identifying, at the remote server device, a computer security threat to a first one of the one or more local client devices using the differential privacy document vectors, and, in response to identifying the computer security threat, protecting against the computer security threat by directing performance, at the first local client device or the remote server device, of a remedial action to protect the first local client device from the computer security threat.

Abstract: 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.