Abstract: Techniques for identity resolution and data enrichment include instantiating, at an account of a data consumer, an identity resolution application using an installer stored procedure. A data object is configured, at the account of the data consumer, to share log data generated by the identity resolution application with an account of a data provider. The identity resolution application is enabled, at the account of the data consumer, for an identity resolution process using source data based on an instruction originating from the account of the data provider. The instruction is based on the configuring of the data object.

Abstract: Systems and methods for managing membership in a private data exchange are provided herein. In one embodiment, the method includes receiving a request for access to a first listing of a data exchange, each listing of the data exchange comprising version metadata. The method further includes, in response to the request, accessing a first version of a data set referenced by the first listing, wherein the first version of the data set comprises a first structure defined by first version metadata, a second listing of the data exchange references a second version of the data set, the second version of the data set comprising a second structure defined by second version metadata, and the second structure is incompatible with the first structure.

Abstract: A method includes installing, in a consumer database account, a shared-instance database that includes a shared instance of a provider-account database that resides in a provider database account. The shared-instance database includes a first schema that includes provider-account training data, provider-account scoring data, a training function, and a scoring function. The method also includes invoking the training function from the consumer database account, which results in creation in the consumer database account of a second schema that includes a machine-learning-model instance of a machine learning model, and which also results in training the machine-learning model instance with at least the provider-account training data. Additionally, the method includes generating consumer-account scoring data by inputting, into the trained machine-learning-model instance, consumer-account input data that is stored in the consumer database account.

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.

Abstract: Systems, methods, and machine-readable storage devices provide for identifying a user dataset on a distributed database. The system includes generating a similarity score dataset that indicates a similarity between the user dataset and a plurality of datasets of other users of the distributed database. The system generates a plurality of overlap queries that are configured to output overlap datasets between the user dataset and one or more of the plurality of datasets. The system further generates a results dataset by applying one or more of the plurality of overlap queries to a joined dataset comprising data from the user dataset and one of the plurality of datasets of other users on the distributed database.

Abstract: A first database account shares, with a second database account, a secure function configured to accept an encrypted dataset and a decryption parameter. The first database account includes a first dataset. The second database account includes a second dataset. The second database account selects one or more rows and one or more columns of the second dataset as a searchable dataset, generates an encrypted searchable dataset by encrypting the searchable dataset with a key, and calls the secure function with the encrypted searchable dataset and the key. The secure function generates results of a query by: generating a decrypted searchable dataset by decrypting the encrypted searchable dataset with the key in a secure environment, obtaining the results by executing the query against a combination of the first dataset and the decrypted dataset in the secure environment to generated query-results data, and outputting the results to the second database account.

Abstract: Disclosed herein are systems and methods for query processing with restrictions in a database clean room. In an embodiment, a system receives a query directed to a combination of a first source dataset from a first database account of a distributed database and a second source dataset from a second database account of the distributed database. The system generates an approved statements table that contains database statement language that can be executed against the combination of the first and second source datasets. Based on determining that the approved statements table includes the query, the system executes the query to produce results data, and stores the results data in the first database account.

Abstract: A method includes performing by at least one hardware processor operations such as configuring at an account of a data consumer, a source table with personally identifiable information (PII) associated with a user. A secure view of the PII is generated based on a view object at the account of the data consumer. The secure view of the PII is shared with an account of a data provider using a share object at the account of the data consumer. The sharing is based on an update of the PII in the source table. A result table is retrieved from the account of the data provider. The result table includes identity resolution information of the user. The identity resolution information is generated based on the update.

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.

Abstract: A method includes generating a defined access clean room in a provider database account, as well as installing, in a consumer account, an application instance that implements the defined access clean room. The method also includes sharing, by the provider database account, source provider data with the defined access clean room, where the sharing makes the source provider data accessible to the consumer database account via the application instance. The method further includes sharing, by the provider database account, a query template with the consumer database account via the application instance. Additionally, the method includes receiving a query generated by the consumer database account based on the query template, validating that the query is consistent with the query template, responsively generating query results by executing the query, as well as storing the query results in the consumer database account.

Abstract: Sharing data in a data exchange across multiple cloud computing platforms and/or cloud computing platform regions is described. An example computer-implemented method can include creating a listing in a data exchange, the listing including a data set hosted by a first cloud computing entity. The data set can be shared with a second cloud computing entity. The method further includes receiving a request associated with a customer account of the second cloud computing entity to access the data set of the listing hosted by the first cloud computing entity and replicating at least a subset of the data set of the listing from the first cloud computing entity to a provider account at the second cloud computing entity to be accessible by the customer account at the second cloud computing entity.

Abstract: A method for identity resolution and data enrichment is performed by at least one hardware processor and includes detecting at an account of a data provider, a shared data object that is shared by an account of a data consumer with the account of the data provider. An application executing at the account of the data consumer is enabled for an identity resolution process based on the detecting of the shared data object. A request for source data received from the application is detected at the account of the data provider. The source data is managed by the account of the data provider. The source data is communicated to the application executing at the account of the data consumer, based on a verification that the application is enabled for the identity resolution process. The identity resolution process is performed at the account of the data consumer using the source data.

Abstract: Embodiments of the present disclosure may provide a data clean room allowing encryption based data analysis across multiple accounts of different database users. The data clean room may also restrict which data may be used in the analysis and may restrict the output. A requesting user's data can be encrypted using a key and a provider user can generate a shareable database function that accepts the key to decrypt the data to generate the results data without exposing each others' data.

Abstract: Disclosed herein are systems and methods for query processing with restrictions in a database clean room. In an embodiment, a system receives a query directed to a combination of a first source dataset from a first database account of a distributed database and a second source dataset from a second database account of the distributed database. The system generates an approved statements table that contains database statement language that can be executed against the combination of the first and second source datasets. Based on determining that the approved statements table includes the query, the system executes the query to produce results data, and stores the results data in the first database account.

Abstract: Provided herein are systems and methods for identity resolution and data enrichment. An example method performed by at least one hardware processor includes detecting at an account of a data provider, an update to personally identifiable information (PII). The PII is stored in a source table managed by an account of a data consumer. An identity resolution process is performed based on detecting the update. The identity resolution process includes generating a secure identifier of a user associated with the PII. The method further includes generating at the account of the data provider, a result table including the secure identifier. The result table is shared with the account of the data consumer.

Abstract: The subject technology receives first party training data provided by an end-user of a baseline machine learning model. The subject technology determines a first set of common features based on the first party training data. The subject technology receives, from at least one data source. The subject technology determines a second set of common features based on the set of datasets. The subject technology trains, using the first set of common features and the second set of common features, a second machine learning model, the second machine learning model incorporating additional training data from the external data supplier during training compared to the baseline machine learning model. The subject technology generates a boosted machine learning model based at least in part on the training, the boosted machine learning model comprising the trained second machine learning model.

Abstract: Techniques for identity resolution and data enrichment include configuring, during an onboarding process at an account of a data provider, at least one parameter associated with access to identity resolution functions by an account of a data consumer. A first shared data object is generated at the account of the data provider. The first shared data object corresponds to a second shared data object at the account of the data consumer. The second shared data object at the account of the data consumer is enabled for sharing of log data associated with an application executing at the account of the data consumer. The application is enabled for an identity resolution process based on the detecting of the second shared data object. Source data associated with the identity resolution functions is encoded for communication to the application at the account of the data consumer based on the enabling.

Abstract: Systems and methods for managing membership in a private data exchange are provided herein. In one embodiment, the method includes receiving a request for access to a first listing of a data exchange, each listing of the data exchange comprising version metadata. The method further includes, in response to the request, accessing a first version of a data set referenced by the first listing, wherein the first version of the data set comprises a first structure defined by first version metadata, a second listing of the data exchange references a second version of the data set, the second version of the data set comprising a second structure defined by second version metadata, and the second structure is incompatible with the first structure.

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.

Abstract: Disclosed herein are methods and systems for secure data comparison using data clean rooms. In an embodiment, a computer system generates a replica database based on a provider database, which stores a cross reference table that cross references a client dataset of a client database and a provider dataset of the provider database. The system receives, at the replica database, a table that is generated by the client database using the cross-reference table. The system transmits, from the replica database, the table to the provider database. The system receives, at the replica database, a results dataset that is generated by the provider database by applying a database statement to the provider database using the table generated by the client database. The system shares, from the replica database, the results dataset with the client database.