Google Inc.

Ardian Poernomo

Gang Wang

Bharath Pattabiraman

Marcel M Moti Yung

Oren Eli Zamir

Alok Aggarwal

Yijian Bai

Andres Munoz Medina

Qing Xu

Ali Nasiri Amini

Alireza Darvish

Eu-Jin Goh

Jingjing Wang

Systems and methods for automatically generating remarketing lists based on client sessions are provided. The system can include a visitor management module to identify client sessions established at a website. The visitor management module can determine node pairs including destination nodes and referrer nodes. The system can include a graph construction module to construct a graph including the node pairs and edges between the node pairs representing click-through rates. The system can include a graph clustering module to assign information resources to a first group based on one of i) a number of edges between the nodes and a conversion node or ii) click-through rates of node pairs including. The system includes a remarketing list generation module to assign the first group of information resources to a remarketing list. The remarketing list generation module can assign client identifiers that accessed the first group of information resources to the remarketing list.

Systems and methods for automatically generating remarketing lists

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for using additive and subtractive noise for preserving the privacy of users. In one aspect, a method includes obtaining a first set of genuine user group identifiers that identify user groups that include a user as a member. A second set of user group identifiers is generated for the user by removing zero or more genuine user group identifiers from the first set to generate the second set and adding, to the second set, one or more fake user group identifiers for user groups that do not include the user as a member. A probabilistic data structure is generated based on the second set of user group identifiers. The probabilistic data structure is transmitted. Data indicating a set of digital components including at least one digital component selected based on the probabilistic data structure is received.

Additive and subtractive noise for privacy protection

This disclosure relates to protecting the security of information in content selection and distribution. In one aspect, a method includes receiving, from a client device and by a first computing system of multi-party computation (MPC) systems, a digital component request including first secret shares of data identifying user groups that include a user of the client device as a member. The first computing system transmits a contextual digital component request to a content platform. The first computing system receives, from the content platform, selection data for multiple digital components. The selection data includes first vector data defining a contextual-based vector of values selected based in part on the set of contextual signals. The first computing system obtains, for each digital component, second vector data defining a user group-based vector of values selected based in part on a respective user group corresponding to the digital component.

Using secure MPC and vector computations to protect access to information in content distribution

This document describes techniques that prevent the sharing or leakage of user information. In one aspect, a method includes receiving, by a first MPC server, a request for a selection criterion of at least one interest group to which a user of a client device belongs. The received request does not reveal an identifier of the client device to the first MPC server. In response to receiving the request, the first MPC server determines a set of ordered selection criterion of the at least one interest group retrieved from a cache of the first MPC server. The set of ordered selection criterion is transformed into a set of key/value pairs secured from being revealed by the second MPC server. The first MPC server transmits the set of key/value pairs to the second MPC server with data that enables the second MPC server to identify a key having a highest value.

Online privacy preserving techniques

Methods, systems, and apparatus for content item auction bidding. In one aspect, a method includes receiving a request for a content item, the request including request feature values and a device identifier, the device identifier being included in a remarketing list; obtaining a predicted performance measure for a remarketing content item associated with the remarketing list based on a first timestamp and a second timestamp, the first timestamp being included in the remarketing list and associated with the device identifier included in the request, and the second timestamp being for the request; determining a bid adjustment value based on the first timestamp and the second timestamp; obtaining a remarketing bid for the remarketing content item, the remarketing bid specifying an amount a content item provider is willing to pay for distribution of the remarketing content item; and adjusting the remarketing bid based on the bid adjustment value.

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Santa Clara, CA, United States of America

Ardian Poernomo