Google Inc.

Jonathan Michael Krafcik

Gang Wang

Yibo Chen

Ting Liu

Ching Law

Yian Gao

Arthur Asuncion

Yong Sheng

Ian Porteous

Rong Ge

Ruoyun Huang

Xintian Yang

Pei Yin

The present disclosure provides systems and methods for automatically detecting third-party re-identification of anonymized computing devices. Methods include retrieving a log of content items provided to anonymized computing devices identifying a first content item provided to a plurality of anonymized computing devices within a first predetermined time period; for each anonymized computing device, generating a set of identifications of second content items retrieved by the anonymized computing device prior to receiving the first content item within a second predetermined time period; determining that signals or combinations of signals with a highest predictive ability between a first set of identifications and a second set of identifications exceeds a threshold; identifying a provider of the first content item; and if the signals or combinations of signals with the highest predictive ability exceeds the threshold, preventing, transmission of a request of an anonymized computing device for a content item to the identified provider.

Automatically detecting unauthorized re-identification

The present disclosure provides systems and methods for automatically detecting third-party re-identification of anonymized computing devices. The method includes retrieving a log of content items provided to anonymized computing devices identifying a first content item provided to a plurality of anonymized computing devices within a first predetermined time period; for each anonymized computing device of the plurality of anonymized computing devices, generating a set of identifications of second content items retrieved by the anonymized computing device prior to receiving the first content item within a second predetermined time period; determining that signals or combinations of signals with a highest predictive ability between a first set of identifications and a second set of identifications exceeds a threshold; identifying a provider of the first content item; and if the signals or combinations of signals with the highest predictive ability exceeds the threshold, preventing, transmission of a request of an anonymized computing device for a content item to the identified provider.

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

Methods and systems for distributing interest categories within a hierarchical classification, wherein a parent interest category is identified and the hierarchical classification is filtered. A sub-interest category is extracted, based on the filtering, and a weighting associated with the sub-interest category is transferred into the parent interest category. The weighting is distributed in the hierarchical classification.

Distributing interest categories within a hierarchical classification

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Kirkland, WA, United States of America

Jonathan Michael Krafcik