Google Inc.

Chenwei Wang

Matthew Tran Clegg

Jiayu Peng

Meng-Hao Li

Pasin Manurangsi

Michael James Wurm

Adam Benjamin Gelernter Sealfon

Jakub Tětek

Systems and methods for generating and maintaining differential privacy while providing accurate values can include obtaining a plurality of noise-added values, processing the plurality of noise-added values to determine a predicted value. The plurality of noise-added value may be utilized to determine one or more accuracy values that can be compared to a threshold to determine if more data is to be obtained and processed before providing a predicted value.

Adaptive privacy budgeting and adaptive sampling value prediction

In one aspect, there is provided a method that includes: obtaining multiple input frequency histograms that each correspond to a respective transmission commitment, where a transmission commitment corresponds to a subset of publishers from a set of publishers; generating a frequency model based on the input frequency histograms, where the frequency model is a parametric model parameterized by a set of model parameters that include a correlation matrix with a respective correlation value for each pair of publishers from the set of publishers; receiving a request to predict a frequency histogram for a target transmission commitment corresponding to a target subset of publishers; generating a predicted frequency histogram for the target transmission commitment using the frequency model; and generating one or more predictions characterizing the target transmission commitment using the predicted frequency histogram.

Reach and frequency prediction for digital component transmissions

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Mountain View, CA, United States of America

Chenwei Wang