公开(公告)号：US20230004670A1

公开(公告)日：2023-01-05

申请号：US17664135

申请日：2022-05-19

Applicant: Snowflake Inc.

Inventor： Justin Langseth ,  Michael Earle Rainey ,  William L. Stratton, JR.

IPC: G06F21/62 ,  G06F16/9535 ,  G06F16/27 ,  H04L9/40

Abstract: Embodiments of the present disclosure may provide a data clean room allowing encryption based data analysis across multiple accounts, including different provider database user accounts that provide user data and a network service and a requesting user that generates one or more clean room requests. The data clean room may also restrict which data may be used in the analysis and may restrict the output.

公开(公告)号：US20230169407A1

公开(公告)日：2023-06-01

申请号：US18162697

申请日：2023-01-31

Applicant: Snowflake Inc.

Inventor： Monica J. Holboke ,  Justin Langseth ,  Stuart Ozer ,  William L. Stratton, JR.

IPC: G06N20/00 ,  G06F21/62 ,  G06F16/25 ,  G06F16/28 ,  G06F18/214

CPC classification number: G06N20/00 ,  G06F21/6227 ,  G06F16/256 ,  G06F16/283 ,  G06F18/214

Abstract: A system for providing access to a database management system (DBMS) to a first user of a cloud data platform, the DBMS being generated by a second user. A machine learning model for training on a training dataset is included in the DBMS. The training dataset includes a first training dataset that is encrypted in the DBMS and a second training dataset that includes non-overlapping features with the first training dataset. A request, from the second user, to train the machine learning model on the first and second training datasets is identified. A trained machine learning model is generated by training the machine learning model on a joined dataset according to the request. One or more outputs from the trained machine learning model are generated by applying the trained machine learning model on new data.

公开(公告)号：US20230169212A1

公开(公告)日：2023-06-01

申请号：US18104281

申请日：2023-01-31

Applicant: Snowflake Inc.

Inventor： Rachel Frances Blum ,  Joshua James Chacona ,  Christian Kleinerman ,  Justin Langseth ,  William L. Stratton, JR.

IPC: G06F21/62 ,  G06F16/2455 ,  G06F16/22

CPC classification number: G06F21/6254 ,  G06F21/6263 ,  G06F16/2455 ,  G06F16/2282

Abstract: Embodiments of the present disclosure may provide a data clean room allowing secure data analysis across multiple accounts, without the use of third parties. Each account may be associated with a different company or party. The data clean room may provide security functions to safeguard sensitive information. For example, the data clean room may restrict access to data in other accounts. The data clean room may also restrict which data may be used in the analysis and may restrict the output. The overlap data may be anonymized to prevent sensitive information from being revealed.

公开(公告)号：US20250086319A1

公开(公告)日：2025-03-13

申请号：US18958256

申请日：2024-11-25

Applicant: Snowflake Inc.

Inventor： Rachel Frances Blum ,  Joshua James Chacona ,  Christian Kleinerman ,  Justin Langseth ,  William L. Stratton, JR.

IPC: G06F21/62 ,  G06F16/22 ,  G06F16/2455

Abstract: Embodiments of the present disclosure may provide a data clean room allowing secure data analysis across multiple accounts, without the use of third parties. Each account may be associated with a different company or party. The data clean room may provide security functions to safeguard sensitive information. For example, the data clean room may restrict access to data in other accounts. The data clean room may also restrict which data may be used in the analysis and may restrict the output. The overlap data may be anonymized to prevent sensitive information from being revealed.

公开(公告)号：US20220035949A1

公开(公告)日：2022-02-03

申请号：US17160924

申请日：2021-01-28

Applicant: Snowflake Inc.

Inventor： Rachel Frances Blum ,  Joshua James Chacona ,  Christian Kleinerman ,  Justin Langseth ,  William L. Stratton, JR.

IPC: G06F21/62 ,  G06F16/22 ,  G06F16/2455

Abstract: Embodiments of the present disclosure may provide a data clean room allowing secure data analysis across multiple accounts, without the use of third parties. Each account may be associated with a different company or party. The data clean room may provide security functions to safeguard sensitive information. For example, the data clean room may restrict access to data in other accounts. The data clean room may also restrict which data may be used in the analysis and may restrict the output. The overlap data may be anonymized to prevent sensitive information from being revealed.

公开(公告)号：US20240020416A1

公开(公告)日：2024-01-18

申请号：US18475610

申请日：2023-09-27

Applicant: Snowflake Inc.

Inventor： Rachel Frances Blum ,  Joshua James Chacona ,  Christian Kleinerman ,  Justin Langseth ,  William L. Stratton, JR.

IPC: G06F21/62 ,  G06F16/2455 ,  G06F16/22

CPC classification number: G06F21/6254 ,  G06F21/6263 ,  G06F16/2455 ,  G06F16/2282

Abstract: Embodiments of the present disclosure may provide a data clean room allowing secure data analysis across multiple accounts, without the use of third parties. Each account may be associated with a different company or party. The data clean room may provide security functions to safeguard sensitive information. For example, the data clean room may restrict access to data in other accounts. The data clean room may also restrict which data may be used in the analysis and may restrict the output. The overlap data may be anonymized to prevent sensitive information from being revealed.

公开(公告)号：US20230186160A1

公开(公告)日：2023-06-15

申请号：US18055248

申请日：2022-11-14

Applicant: Snowflake Inc.

Inventor： Monica J. Holboke ,  Justin Langseth ,  Stuart Ozer ,  William L. Stratton, JR.

IPC: G06N20/00 ,  G06F21/62 ,  G06F16/25 ,  G06F16/28 ,  G06F18/214

CPC classification number: G06N20/00 ,  G06F21/6227 ,  G06F16/256 ,  G06F16/283 ,  G06F18/214

Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for sharing, on a distributed database, a database application to a first user of the distributed database, the database application generated by a second user of the distributed database. The training dataset includes a first database training dataset from the first user of the distributed database and a second database training dataset from the second user of the distributed database, the first database training dataset and the second database training dataset including non-overlapping dataset features. The database application further identifies a query from the second user to train the machine learning model on the training dataset and generates a trained machine learning model by training the machine learning model on a joined dataset according to the query. The database application generates outputs from the trained machine learning model by applying the trained machine learning model on new data.

公开(公告)号：US20230169203A1

公开(公告)日：2023-06-01

申请号：US18162709

申请日：2023-01-31

Applicant: Snowflake Inc.

Inventor： Justin Langseth ,  Michael Earle Rainey ,  William L. Stratton, JR.

IPC: G06F21/62 ,  H04L9/40 ,  G06F16/9535 ,  G06F16/27

CPC classification number: G06F21/6227 ,  H04L63/0421 ,  G06F16/9535 ,  G06F16/27 ,  H04L9/0643

Abstract: Embodiments of the present disclosure may provide a data clean room allowing encryption based data analysis across multiple accounts, including different provider database user accounts that provide user data and a network service and a requesting user that generates one or more clean room requests. The data clean room may also restrict which data may be used in the analysis and may restrict the output.

公开(公告)号：US20220292213A1

公开(公告)日：2022-09-15

申请号：US17644732

申请日：2021-12-16

Applicant: Snowflake Inc.

Inventor： Monica J. Holboke ,  Justin Langseth ,  Stuart Ozer ,  William L. Stratton, JR.

IPC: G06F21/62 ,  G06N20/00 ,  G06F16/25 ,  G06F16/28 ,  G06K9/62

Abstract: A secure machine learning system of a database system can be implemented to use secure shared data to train a machine learning model. To manage the model, a first user of the database can share data in an encrypted view with a second user of the database, and further share one or more functions of an application that accesses the data while the data is encrypted. The second user can access functions of the application and can call the functions to generate a trained machine learning model and further generate machine learning outputs (e.g., predictions) from the trained model.