Abstract: Methods, systems, and apparatus, including medium-encoded computer program products for selecting and displaying content in privacy preserving manners are described. A digital component request that includes contextual data related to an environment in which the digital component will be displayed can be received from a client device by a first content platform. Based on the contextual data, the user can be assigned to user attribute buckets, which can be associated with at least one type of user attribute. Based on the contextual data and each user attribute bucket to which the user is assigned, candidate digital components can be selected for distribution to the client device. The application can be provided response data that initiates an update to aggregated user attribute data for the user and enables the application to select a digital component to display to the user based on the aggregated data.

Abstract: The present disclosure describes techniques for training a model using cross-domain adaptation to classify content requests from client devices having unknown attributes. The system can obtain requests for content from client devices of a first domain, and requests for content from client devices of a second domain. The system can train a model by propagating request attributes of the first domain through the model to generate first internal data from an internal layer of the model and a first output vector an output layer of the model. The system can propagate request attributes of the second domain to generate second internal data, and determine a difference between the first internal data and the second internal data. The system can update the model based on the difference and the output vector, and classify a third client device of the second domain using the model.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting and distributing digital components to client devices in ways that protect user privacy and confidential data of content platforms and/or digital component providers are described. In one aspect, a method includes receiving, by a secure distribution system and from a client device of a user, a digital component request that includes, for each of multiple content platforms that distribute digital components to users, a corresponding user embedding comprising weights indicative of the relevance of multiple features to the user. The secure distribution system provides each user embedding as input to a respective isolated execution environment for the content platform corresponding to the user embedding, wherein the secure distribution system hosts each isolated execution environment.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting and distributing digital components to client devices in ways that protect user privacy and confidential data of content platforms and/or digital component providers are described. In one aspect, a method includes receiving, by a secure distribution system and from a client device of a user, a digital component request that includes, for each of multiple content platforms that distribute digital components to users, a corresponding user embedding comprising weights indicative of the relevance of multiple features to the user. The secure distribution system provides each user embedding as input to a respective isolated execution environment for the content platform corresponding to the user embedding, wherein the secure distribution system hosts each isolated execution environment.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for using transfer machine learning to predict attributes are described. In one aspect, a method includes receiving, from a client device of a user, a digital component request that includes at least input contextual information for a display environment in which a selected digital component will be displayed. The contextual information is converted into input data that includes input feature values for a transfer machine learning model trained to output predictions of user attributes of users based on feature values for features representing display environments. The transfer machine learning model is trained using training data for subscriber users obtained from a data pipeline associated with electronic resources to which the subscriber users are subscribed and adapted to predict user attributes of non-subscribing users viewing electronic resources to which the non-subscribing users are not subscribed.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for providing digital components to a client device. Methods can include assigning a temporary group identifier to a client device that identifies a particular group, from among a plurality different groups, that includes the client device based on a current period of user activity on the client device. A training set is generated for training a machine learning model that generates user characteristics. A request for digital component is received from the client device that includes the temporary group identifier currently assigned to the client device, a subset of activity features and one or more additional features that are based on the client device. The machine learning model generates one or more user characteristics based on which one or more digital components are selected and transmitted to the client device.