公开(公告)号：US11727147B2

公开(公告)日：2023-08-15

申请号：US17016788

申请日：2020-09-10

Applicant: Google LLC

Inventor： Alessandro Epasto ,  Hossein Esfandiari ,  Vahab Seyed Mirrokni ,  Andres Munoz Medina ,  Umar Syed ,  Sergei Vassilvitskii

IPC: G06F16/00 ,  G06F21/62 ,  G06N20/00 ,  G06F16/28

CPC classification number: G06F21/6254 ,  G06F16/285 ,  G06N20/00

Abstract: A computer-implemented method for k-anonymizing a dataset to provide privacy guarantees for all columns in the dataset can include obtaining, by a computing system including one or more computing devices, a dataset comprising data indicative of a plurality of entities and at least one data item respective to at least one of the plurality of entities. The computer-implemented method can include clustering, by the computing system, the plurality of entities into at least one entity cluster. The computer-implemented method can include determining, by the computing system, a majority condition for the at least one entity cluster, the majority condition indicating that the at least one data item is respective to at least a majority of the plurality of entities. The computer-implemented method can include assigning, by the computing system, the at least one data item to the plurality of entities in an anonymized dataset based at least in part on the majority condition.

公开(公告)号：US20220075897A1

公开(公告)日：2022-03-10

申请号：US17016788

申请日：2020-09-10

Applicant: Google LLC

Inventor： Alessandro Epasto ,  Hossein Esfandiari ,  Vahab Seyed Mirrokni ,  Andres Munoz Medina ,  Umar Syed ,  Sergei Vassilvitskii

IPC: G06F21/62 ,  G06F16/28 ,  G06N20/00

Abstract: A computer-implemented method for k-anonymizing a dataset to provide privacy guarantees for all columns in the dataset can include obtaining, by a computing system including one or more computing devices, a dataset comprising data indicative of a plurality of entities and at least one data item respective to at least one of the plurality of entities. The computer-implemented method can include clustering, by the computing system, the plurality of entities into at least one entity cluster. The computer-implemented method can include determining, by the computing system, a majority condition for the at least one entity cluster, the majority condition indicating that the at least one data item is respective to at least a majority of the plurality of entities. The computer-implemented method can include assigning, by the computing system, the at least one data item to the plurality of entities in an anonymized dataset based at least in part on the majority condition.

公开(公告)号：US20250077709A1

公开(公告)日：2025-03-06

申请号：US18955530

申请日：2024-11-21

Applicant: Google LLC

Inventor： Alessandro Epasto ,  Hossein Esfandiari ,  Vahab Seyed Mirrokni ,  Andres Munoz Medina ,  Umar Syed ,  Sergei Vassilvitskii

IPC: G06F21/62 ,  G06F16/28 ,  G06N20/00

Abstract: A computer-implemented method for k-anonymizing a dataset to provide privacy guarantees for all columns in the dataset can include obtaining, by a computing system including one or more computing devices, a dataset comprising data indicative of a plurality of entities and at least one data item respective to at least one of the plurality of entities. The computer-implemented method can include clustering, by the computing system, the plurality of entities into at least one entity cluster. The computer-implemented method can include determining, by the computing system, a majority condition for the at least one entity cluster, the majority condition indicating that the at least one data item is respective to at least a majority of the plurality of entities. The computer-implemented method can include assigning, by the computing system, the at least one data item to the plurality of entities in an anonymized dataset based at least in part on the majority condition.

公开(公告)号：US20230297583A1

公开(公告)日：2023-09-21

申请号：US18323766

申请日：2023-05-25

Applicant: Google LLC

Inventor： Xi Cheng ,  Amir H. Hormati ,  Lisa Yin ,  Umar Syed

IPC: G06F16/2458 ,  G06F16/22

CPC classification number: G06F16/2477 ,  G06F16/221 ,  G06F16/2282

Abstract: A method for time series forecasting includes receiving a time series forecasting query from a user requesting the data processing hardware to perform a plurality of time series forecasts. Each time series forecast is a forecast of future data based on respective current data. Simultaneously, for each time series forecast of the plurality of time series forecasts requested by the time series forecasting query, the method includes training a plurality of models for the respective time series forecast. The method also includes determining which model of the plurality of models best fits the respective time series forecast and forecasting the future data based on the determined best fitting model and the respective current data. The method also includes returning, to the user, the forecasted future data for each of the plurality of time series forecasts request by the timer series forecasting query.

公开(公告)号：US20230359769A1

公开(公告)日：2023-11-09

申请号：US18345657

申请日：2023-06-30

Applicant: Google LLC

Inventor： Alessandro Epasto ,  Hossein Esfandiari ,  Vahab Seyed Mirrokni ,  Andres Munoz Medina ,  Umar Syed ,  Sergei Vassilvitskii

IPC: G06F21/62 ,  G06N20/00 ,  G06F16/28

CPC classification number: G06F21/6254 ,  G06N20/00 ,  G06F16/285

Abstract: A computer-implemented method for k-anonymizing a dataset to provide privacy guarantees for all columns in the dataset can include obtaining, by a computing system including one or more computing devices, a dataset comprising data indicative of a plurality of entities and at least one data item respective to at least one of the plurality of entities. The computer-implemented method can include clustering, by the computing system, the plurality of entities into at least one entity cluster. The computer-implemented method can include determining, by the computing system, a majority condition for the at least one entity cluster, the majority condition indicating that the at least one data item is respective to at least a majority of the plurality of entities. The computer-implemented method can include assigning, by the computing system, the at least one data item to the plurality of entities in an anonymized dataset based at least in part on the majority condition.

公开(公告)号：US12164673B2

公开(公告)日：2024-12-10

申请号：US18345657

申请日：2023-06-30

Applicant: Google LLC

Inventor： Alessandro Epasto ,  Hossein Esfandiari ,  Vahab Seyed Mirrokni ,  Andres Munoz Medina ,  Umar Syed ,  Sergei Vassilvitskii

IPC: G06F16/00 ,  G06F16/28 ,  G06F21/62 ,  G06N20/00

Abstract: A computer-implemented method for k-anonymizing a dataset to provide privacy guarantees for all columns in the dataset can include obtaining, by a computing system including one or more computing devices, a dataset comprising data indicative of a plurality of entities and at least one data item respective to at least one of the plurality of entities. The computer-implemented method can include clustering, by the computing system, the plurality of entities into at least one entity cluster. The computer-implemented method can include determining, by the computing system, a majority condition for the at least one entity cluster, the majority condition indicating that the at least one data item is respective to at least a majority of the plurality of entities. The computer-implemented method can include assigning, by the computing system, the at least one data item to the plurality of entities in an anonymized dataset based at least in part on the majority condition.

公开(公告)号：US11693867B2

公开(公告)日：2023-07-04

申请号：US16986861

申请日：2020-08-06

Applicant: Google LLC

Inventor： Xi Cheng ,  Amir H. Hormati ,  Lisa Yin ,  Umar Syed

IPC: G06F16/2458 ,  G06F16/22

CPC classification number: G06F16/2477 ,  G06F16/221 ,  G06F16/2282

Abstract: A method for time series forecasting includes receiving a time series forecasting query from a user requesting the data processing hardware to perform a plurality of time series forecasts. Each time series forecast is a forecast of future data based on respective current data Simultaneously, for each time series forecast of the plurality of time series forecasts requested by the time series forecasting query, the method includes training a plurality of models for the respective time series forecast. The method also includes determining which model of the plurality of models best fits the respective time series forecast and forecasting the future data based on the determined best fitting model and the respective current data. The method also includes returning, to the user, the forecasted future data for each of the plurality of time series forecasts request by the timer series forecasting query.

公开(公告)号：US20210357402A1

公开(公告)日：2021-11-18

申请号：US16986861

申请日：2020-08-06

Applicant: Google LLC

Inventor： Xi Cheng ,  Amir H. Hormati ,  Lisa Yin ,  Umar Syed

IPC: G06F16/2458 ,  G06F16/22

Abstract: A method for time series forecasting includes receiving a time series forecasting query from a user requesting the data processing hardware to perform a plurality of time series forecasts. Each time series forecast is a forecast of future data based on respective current data Simultaneously, for each time series forecast of the plurality of time series forecasts requested by the time series forecasting query, the method includes training a plurality of models for the respective time series forecast. The method also includes determining which model of the plurality of models best fits the respective time series forecast and forecasting the future data based on the determined best fitting model and the respective current data The method also includes returning, to the user, the forecasted future data for each of the plurality of time series forecasts request by the timer series forecasting query.