Google Inc.

Arthur Asuncion

Sheng Ma

Evgeny Skvortsov

James Robert Koehler

Benjamin Kreuter

Craig Wright

Laura Grace Book

Jiayu Peng

Xichen Huang

Yong Sheng

Ruoyun Huang

Kai Chen

Gregory Sean Corrado

Jonathan Michael Krafcik

Ian Porteous

Rong Ge

Xintian Yang

Pei Yin

Johannes Christian Schuler

Benjamin R Kreuter

Systems and methods for generating min-increment counting bloom filters to determine count and frequency of device identifiers and attributes in a networking environment are disclosed. The system can maintain a set of data records including device identifiers and attributes associated with device in a network. The system can generate a vector comprising coordinates corresponding to counter registers. The system can identify hash functions to update a counting bloom filter. The system can hash the data records to extract index values pointing to a set of counter registers. The system can increment the positions in the min-increment counting bloom filter corresponding to the minimum values of the counter registers. The system can obtain an aggregated public key comprising a public key. The system can encrypt the counter registers using the aggregated shared key to generate an encrypted vector. The system can transmit the encrypted vector to a networked worker computing device.

Secure multi-party reach and frequency estimation

Methods, systems, and apparatus include computer programs encoded on a computer-readable storage medium for labeling user identifiers. A method includes: identifying a set of unlabeled identifiers, wherein an unlabeled identifier has an unknown classification as to a particular class in a multi-class demographic characteristic; determining for each unlabeled identifier a probability as to inclusion in a class of the multi-class demographic characteristic based on known user behavior producing a distribution of probabilities for the unlabeled identifier; for a given unlabeled identifier, adjusting the probability based on a known internet distribution of entities with respect to a given class in the multi-class demographic characteristic and distribution of the probabilities among the unlabeled identifiers; and assigning a label for a particular class in the multi-class demographic characteristic to the unlabeled identifier in accordance with the adjusting.

Demographic inference calibration

Systems and methods of determining computing device characteristics from computer network activity are provided. A data processing system can obtain data identifying a global cluster that indicates an interest category and can create a sub-cluster of the global cluster based on a characteristic common to content access computing devices. A weight indicating a correlation between the characteristic common to content access computing devices and the interest category can be assigned to the sub-cluster. Responsive to a communication between a first content access computing device and a content publisher computing device, the data processing system can identify a characteristic. The data processing system can associate the first content access computing device with the sub-cluster based on the characteristic of the first content access computing device and the characteristic common to the content access computing devices, and based on the weight can determine a status of the first content access computing device.

Determining computing device characteristics from computer network activity

A computer-implemented method for determining an attribute for an online user of a candidate computing device is provided. The method implemented uses a host computing device. The method includes identifying a first set of model data including device data from a plurality of model computing devices including location data and access data, and a plurality of categories for an attribute of a population segment including an online user. Each category defines a segment of the attribute. The method further includes training a classification model by the host computing device with at least the first set of model data and the plurality of categories. The method also includes identifying device data associated with the candidate computing device. The method further includes applying the device data of the candidate computing device to the classification model to determine a category of the plurality of categories for the online user.

Determining an attribute of an online user using user device data

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Bothell, WA, United States of America

Arthur Asuncion