Google Inc.

Matthew Tran Clegg

Chenwei Wang

Pasin Manurangsi

Jiayu Peng

Meng-Hao Li

Michael James Wurm

Adam Benjamin Gelernter Sealfon

Badih Ghazi

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

Methods, systems, and apparatus, including medium-encoded computer program products, for adaptive privacy-preserving information retrieval. An information server can accept from a user a request for privacy sensitive information accessible to the information server. The information server can determine a remaining privacy allocation for the user of the information server and can determine a noise parameter for a response to the request, where application of the noise parameter to the response can decrease a privacy loss associated with the response. The information server can determine a privacy modifier for the response. In response to the information server determining that the remaining privacy allocation satisfies the privacy modifier, the information server can: (i) determining the response to the request; (ii) apply the noise parameter to the response to produce a noised response; (iii) provide the noised response to the user; and (iv) adjust the remaining privacy allocation according to the privacy modifier.

Adaptive privacy-preserving information retrieval

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium. In one aspect, a method includes receiving, from a content distributor, plan data specifying a set of distribution plans that cause distribution of content. Instructions are transmitted to publishers to submit secret shares of a multi-register sketch representing presentations of the content. A notification that the content distributor has requested an analysis of the presentations of the content is sent to a multi-party computing group. A result share of the analysis of the presentation of the content is received from multiple MPC devices in the MPC group. A set of result shares received from the of MPC devices are transmitted to the content distributor.

High speed private and secure cross-entity data processing

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.

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Los Angeles, CA, United States of America

Matthew Tran Clegg