Abstract: Privacy-preserving Kernel Density Estimations (KDEs) may be provided. A data set with data subject to privacy restrictions may be sampled to generate a set of linear measurements. An amount of noise may be added to the set of linear measurements to ensure privacy. Then when shared with a recipient, the noise-modified set of linear measurements can be used to generate privacy-preserving KDE values for features in the data set using a corresponding estimation function with the same values of parameters in the linear measurement function. Privacy-preserving KDE values may then be used to generate synthetic data sets that do not violate privacy restrictions on the source data set.

Abstract: A service may generate keywords to produce synthetic documents, while maintaining data privacy for the original documents. A client may extract keyword sequences from locally stored documents, embed the keyword sequences into vectors, and generate a DP-KDE distribution based on the vectors. The DP-KDE distribution preserves data privacy for the original documents. The service receives the DP-KDE distribution, obtains a particular vector from the DP-KDE (e.g., based on a calculated score for the DP-KDE using random Gaussian completions), decodes the particular vector into a sequence of synthetic keywords, and uses the sequence of synthetic keywords to prompt an LLM to produce one or more synthetic documents.

Abstract: Examples of techniques for anomaly detection with feedback are described. An instance includes a technique is receiving a plurality of unlabeled data points from an input stream; performing anomaly detection on a point of the unlabeled data points using an anomaly detection engine; pre-processing the unlabeled data point that was subjected to anomaly detection; classifying the pre-processed unlabeled data point; determining the anomaly detection was not proper based on a comparison of a result of the anomaly detection and a result of the classifying of the pre-processed unlabeled data point; and in response to determining the anomaly detection was not proper, providing feedback to the anomaly detection engine to change at least one emphasis used in anomaly detection.