Snowflake Inc.

Google Inc.

David Schultz

Subramanian Muralidhar

Khalid Zaman Bijon

Artin Avanes

Thierry Cruanes

Zheng Mi

Raja Suresh Krishna Balakrishnan

Allison Waingold Lee

Jian J Xu

Yujie Li

Monica J Holboke

Jian Xu

Simon Holm Jensen

Daniel N Meredith

Zixi Zhang

Damien Carru

Eric Robinson

Yossi Richter

Md Ahsan Arefin

Benoit Dageville

Sahaj Saini

Shuang Guan

Srinivas Krishnan

Eran Gal

Doron Shaharabani

Jiaqi Yan

Uday Ramakrishna Naik

Ganeshan Ramachandran Iyer

Jesse Louis Alpert

Raja Balakrishnan

Jennifer Wenjun Bi

A data platform creates an application in a data-provider account, where the application includes one or more application programming interfaces (APIs) corresponding to one or more underlying code blocks. The data platform shares homomorphically encrypted provider data with the application in the data-provider account. The data platform installs, in a data-consumer account, an application instance of the application. The data platform shares homomorphically encrypted consumer data with the application instance in the data-consumer account. The data platform invokes one or more of the APIs of the application instance to execute respective associated underlying code blocks, which are not visible to the data-consumer account, and which operate on the shared homomorphically encrypted provider data and the shared homomorphically encrypted consumer data. The data platform saves homomorphically encrypted output of the one or more respective associated underlying code blocks locally within the data-consumer account.

Data clean rooms using defined access with homomorphic encryption

Aspects of the present disclosure address systems, methods, and devices for tracking object dependencies in a cloud database system. An object dependency created between a referencing object and a referenced object is detected. Based on detecting the object dependency, a dependency record is generated. The dependency record includes dependency information describing the object dependency between the reference object and the referenced object. The dependency record is stored in a database of dependency records.

Management of object dependency records in a cloud system

A database system facilitates secure data sharing by implementing projection constraints within a query processing framework. Upon receiving a query directed to a shared dataset, the system, utilizing hardware processors, identifies a subset of data within the dataset that is subject to a projection constraint policy. The applicability of the projection constraint is determined based on the context of the query, which is derived from a data sharing agreement. The system processes the query by selectively restricting the projection of data values from constrained columns, while allowing specific operations to be performed on the data. The output generated in response to the query is compliant with the projection constraint policy, providing derived data based on the allowed operations without revealing the actual data values. This ensures the confidentiality of sensitive information while enabling collaborative data analysis and sharing among various users of the database system.

Projection constraint policies in a database system

Provided herein are systems and methods for configuring integrity constraints (including a check constraint) and row violation logging using error tables. An example method includes decoding a query received at a network-based database system. The query includes a command to perform an operation on a base table. An integrity constraint associated with the base table is retrieved. The integrity constraint specifies a desired configuration for the base table. A verification of the integrity constraint is performed to detect erroneous data of the base table that violates the desired configuration. The erroneous data is input into an error table that is configured as a nested object of the base table. A notification that the erroneous data is available in the error table is generated and output.

Configuring check constraint and row violation logging using error tables

In an embodiment, an application is created on a data-provider platform. The application includes one or more application programming interfaces (APIs) corresponding to one or more underlying code blocks. Provider data is shared with the application on the data-provider platform. An application instance of the application is installed in a trusted execution environment (TEE). The application instance includes one or more APIs corresponding to the one or more APIs in the application on the data-provider platform. Consumer data is shared with the application instance from a data-consumer platform. One or more of the APIs of the application instance are invoked to execute, on the TEE, respective associated underlying code blocks that are not visible on the TEE. The output of the one or more respective associated underlying code blocks is saved to the data-consumer platform.

Data clean rooms using defined access in trusted execution environment

A method of implementing object tagging framework starts with the processor receiving a tag creation command including a tag name. In response to the tag creation command, the processor creates a current tag. The processor then receives an association command, the tag name and a source object identifier. The processor determines a source object associated with the source object identifier. The source object includes a tag value. The processor associates the current tag with the source object. The processor receives a replication command including the source object and a target object. The processor causes replication of the source object to the target object that comprises replicating the current tag with the tag name and the tag value in the source object to the target object. Other embodiments are also described herein.

Shared tag data system

A method of implementing object tagging framework starts with the processor receiving a tag creation command including a tag name. In response to the tag creation command, the processor creates a current tag. The processor then receives an association command, the tag name and a target object identifier. The processor determines a target object associated with the target object identifier. The target object includes a tag value. The processor associates the current tag with the target object. The processor identifies a first child object of the target object. The target object and the first child object are hierarchical objects. In response to determining that the first child object is tag-unassociated, the processor associates the current tag with the first child object. In response to receiving a query including the tag name, the processor generates an output based on the tag name. Other embodiments are also described herein.

Hierarchical object tagging framework

Techniques for creating, sharing, and using bundles (also referred to as packages) in a multi-tenant database are described herein. A bundle is a schema object with associated hidden schemas. A bundle can be created by a provider user and can be shared with a plurality of consumer users. The bundle can be used to enable code sharing and distribution without losing control while maintaining security protocols.

Bundle creation and distribution

In an embodiment, a data platform creates an application in a data-provider account. The application includes one or more APIs corresponding to one or more underlying code blocks. The data platform shares provider data with the application in the data-provider account, and also installs, in a data-consumer account, an application instance of the application. The application instance includes one or more APIs corresponding to the one or more APIs in the application in the data-provider account. The data platform shares consumer data with the application instance in the data-consumer account, and invokes one or more of the APIs of the application instance to execute respective associated underlying code blocks, which are not visible to the data-consumer account. The data platform also saves output of the one or more respective associated underlying code blocks locally within the data-consumer account.

Data clean rooms using defined access

Row-level security (RLS) may provide fine-grained access control based on flexible, user-defined access policies to databases, tables, objects, and other data structures. A RLS policy may be an entity or object that defines rules for row access. A RLS policy may be decoupled or independent from any specific table. This allows more robust and flexible control. A RLS policy may then be attached to one or more tables. The RLS policy may include a Boolean-valued expression.

Row-level security

Systems and methods for managing column hiding are provided. The systems and methods receive, from a client device, a query associated with a table. The systems and methods determine an access restriction associated with the client device. The systems and methods identify a column of the table that is restricted by the access restriction associated with the client device. In response to identifying the column of the table that is restricted by the access restriction associated with the client device, the systems and methods provide a result of the query that excludes data corresponding to the column.

Column hiding management system

A method includes identifying a middlebox receiving network flow and communicating with one or more backend virtual machines. The method also includes receiving flow statistics corresponding to the network flow of the middlebox and determining whether the flow statistics satisfy an offload rule. The offload rule indicates when to migrate the network flow from the middlebox to an end host. When the flow statistics satisfy the offload rule, the method also includes migrating the network flow from the middlebox to the end host.

Transparent migration of virtual network functions

A system for enforcing projection constraints on data values stored in specified variables of a shared dataset of a cloud data platform. A request is received from a first account of the cloud data platform that identifies a first operation to be performed on the shared dataset. A first set of data, including data accessed from a first variable, is accessed from the shared dataset to use in performing the first operation. A projection constraint policy attached to the first variable of the shared dataset is determined, and the projection constraint policy is further determined to be enforced based on the request. Based on the first set of data and the first operation, an output to the first request is generated.

Projection constraints enforced in a database system

Systems and methods for generating transient object references are provided. The systems and methods perform operations including establishing a session between a first entity and a second entity. The operations include identifying an object that the first entity is authorized to access according to a first set of access privileges. The operations include generating a reference associated with the object. The operations include temporarily authorizing the second entity to access the object using the reference according to a second set of access privileges, the second set of access privileges being derived from the first set of access privileges.

Growing community of inventors

Piedmont, CA, United States of America

David Schultz