公开(公告)号：US20220129760A1

公开(公告)日：2022-04-28

申请号：US17511448

申请日：2021-10-26

Applicant: Google LLC

Inventor： Shanmugasundaram Ravikumar ,  Badih Ghazi ,  Pasin Manurangsi ,  Chiyuan Zhang ,  Noah Golowich

IPC: G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training neural networks with label differential privacy. One of the methods includes, for each training example: processing the network input in the training example using the neural network in accordance with the values of the network parameters as of the beginning of the training iteration to generate a network output, generating a private network output for the training example from the target output in the training example and the network output for the training example, and generating a modified training example that includes the network input in the training example and the private network output for the training example; and training the neural network on at least the modified training examples to update the values of the network parameters.

公开(公告)号：US11070508B1

公开(公告)日：2021-07-20

申请号：US16871947

申请日：2020-05-11

Applicant: Google LLC

Inventor： Andrew Tomkins ,  Shanmugasundaram Ravikumar ,  Shalini Agarwal ,  MyLinh Yang ,  Bo Pang ,  Mark Yinan Li

IPC: G10L21/00 ,  G10L25/00 ,  H04L12/58

Abstract: Methods and apparatus related to determining an effect on dissemination of information related to an event based on a dynamic confidence level associated with the event. For example, an event and an event confidence level of the event may be determined based on a message of a user. An effect on dissemination of information related to the event may be determined based on the confidence level. A new confidence level may be determined based on additional data associated with the event and the effect on dissemination of information may be adjusted based on the new confidence level. In some implementations, the additional data may be based on a new message that is related to the message, such as a reply to the message.

公开(公告)号：US20210019184A1

公开(公告)日：2021-01-21

申请号：US16932581

申请日：2020-07-17

Applicant: Google LLC

Inventor： Erik Nathan Vee ,  Manish Deepak Purohit ,  Joshua Ruizhi Wang ,  Shanmugasundaram Ravikumar ,  Zoya Svitkina

IPC: G06F9/48 ,  G06N3/02 ,  G06F16/901

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for scheduling operations represented on a computation graph. One of the methods receiving, by a computation graph system, a request to generate a schedule for processing a computation graph, obtaining data representing the computation graph generating a separator of the computation graph; and generating the schedule to perform the operations represented in the computation graph, wherein generating the schedule comprises: initializing the schedule with zero nodes; for each node in the separator: determining whether the node has any predecessor nodes in the computation graph, when the node has any predecessor nodes, adding the predecessor nodes to the schedule, and adding the node in the schedule, and adding to the schedule each node in each subgraph that is not a predecessor to any node in the separator on the computation graph.

公开(公告)号：US20240236052A1

公开(公告)日：2024-07-11

申请号：US18403339

申请日：2024-01-03

Applicant: Google LLC

Inventor： Badih Ghazi ,  Noah Zeger Golowich ,  Shanmugasundaram Ravikumar ,  Pasin Manurangsi ,  Ameya Avinash Velingker ,  Rasmus Pagh

IPC: H04L9/40 ,  G06N5/04 ,  G06N20/00

CPC classification number: H04L63/0428 ,  G06N5/04 ,  G06N20/00

Abstract: An encoding method for enabling privacy-preserving aggregation of private data can include obtaining private data including a private value, determining a probabilistic status defining one of a first condition and a second condition, producing a multiset including a plurality of multiset values, and providing the multiset for aggregation with a plurality of additional multisets respectively generated for a plurality of additional private values. In response to the probabilistic status having the first condition, the plurality of multiset values is based at least in part on the private value, and in response to the probabilistic status having the second condition, the plurality of multiset values is a noise message. The noise message is produced based at least in part on a noise distribution that comprises a discretization of a continuous unimodal distribution supported on a range from zero to a number of multiset values included in the plurality of multiset values.

公开(公告)号：US20240232686A1

公开(公告)日：2024-07-11

申请号：US18012292

申请日：2022-07-29

Applicant: Google LLC

Inventor： Yicheng Fan ,  Jingyue Shen ,  Deqiang Chen ,  Peter Shaosen Young ,  Dana Alon ,  Erik Nathan Vee ,  Shanmugasundaram Ravikumar ,  Andrew Tomkins

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Systems and methods of the present disclosure are directed to portion-specific compression and optimization of machine-learned models. For example, a method for portion-specific compression and optimization of machine-learned models includes obtaining data descriptive of one or more respective sets of compression schemes for one or more model portions of a plurality of model portions of a machine-learned model. The method includes evaluating a cost function to respectively select one or more candidate compression schemes from the one or more sets of compression schemes. The method includes respectively applying the one or more candidate compression schemes to the one or more model portions to obtain a compressed machine-learned model comprising one or more compressed model portions that correspond to the one or more model portions.

公开(公告)号：US20230327850A1

公开(公告)日：2023-10-12

申请号：US18297084

申请日：2023-04-07

Applicant: Google LLC

Inventor： Badih Ghazi ,  Shanmugasundaram Ravikumar ,  Pasin Manurangsi ,  Mariana Petrova Raykova ,  Adrian Gascon ,  James Henry Bell ,  Phillipp Schoppmann

IPC: H04L9/00 ,  H04L9/14 ,  H04L9/06

CPC classification number: H04L9/008 ,  H04L9/14 ,  H04L9/0643 ,  H04L2209/46

Abstract: Provided are systems and methods for the computation of sparse, (ε, δ)-differentially private (DP) histograms in the two-server model of secure multi-party computation (MPC). Example protocols enable two semi-honest non-colluding servers to compute histograms over the data held by multiple users, while only learning a private view of the data.

公开(公告)号：US20230308422A1

公开(公告)日：2023-09-28

申请号：US18011995

申请日：2021-12-20

Applicant: Google LLC

Inventor： Badih Ghazi ,  Shanmugasundaram Ravikumar ,  Alisa Chang ,  Pasin Manurangsi

IPC: H04L9/40 ,  G06F21/60

CPC classification number: H04L63/0428 ,  G06F21/604

Abstract: A computer-implemented method for encoding data for communications with improved privacy includes obtaining, by a computing system comprising one or more computing devices, input data including one or more input data points. The method can include constructing, by the computing system, a net tree including potential representatives of the one or more input data points, the potential representatives arranged in a plurality of levels, the net tree including a hierarchical data structure including a plurality of hierarchically organized nodes. The method can include determining, by the computing system, a representative of each of the one or more input data points from the potential representatives of the net tree, the representative including one of the plurality of hierarchically organized nodes. The method can include encoding, by the computing system, the representative of each of the one or more input data points for communication.

公开(公告)号：US20210216367A1

公开(公告)日：2021-07-15

申请号：US17214699

申请日：2021-03-26

Applicant: Google LLC

Inventor： Erik Nathan Vee ,  Manish Deepak Purohit ,  Joshua Ruizhi Wang ,  Shanmugasundaram Ravikumar ,  Zoya Svitkina

IPC: G06F9/48 ,  G06F16/901 ,  G06N3/02

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for scheduling operations represented on a computation graph. One of the methods receiving, by a computation graph system, a request to generate a schedule for processing a computation graph, obtaining data representing the computation graph generating a separator of the computation graph; and generating the schedule to perform the operations represented in the computation graph, wherein generating the schedule comprises: initializing the schedule with zero nodes; for each node in the separator: determining whether the node has any predecessor nodes in the computation graph, when the node has any predecessor nodes, adding the predecessor nodes to the schedule, and adding the node in the schedule, and adding to the schedule each node in each subgraph that is not a predecessor to any node in the separator on the computation graph.