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: 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: 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.

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 vehicle component balancing robot apparatus, for on vehicle balancing of one or more of a tire, a wheel, bearings, brake components, and vehicle components that impart vibrations to the vehicle. The apparatus includes a frame arranged so as to connect with the vehicle. A robot of the apparatus moves relative to the frame, and is configured so that the move, relative to the frame, resolves a predetermined location of a tire-wheel assembly relative to a reference frame of the robot. The robot has at least one end effector arranged to interface the tire-wheel assembly and the robot moves the at least one end effector to other predetermined locations on a wheel rim of the tire-wheel assembly, determined based on resolution of the predetermined location of the tire-wheel assembly relative to the reference frame.

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: Embodiments of the present disclosure may provide a data sharing system implemented as a local application of a distributed database. A query from a query template can be validated and executed against shared dataset that comprises portions of data from the database dataset and additional portions of data from another database of the distributed database.

Abstract: First and second database accounts respectively share first and second source datasets with one another in a data clean room. The first database account stores a first approved-statements table that contains database statements that are permitted by the first database account to be executed against a combination of the shared first source dataset and the shared second source dataset. The second database account stores a second approved-statements table that contains database statements that are permitted by the second database account to be executed against the combination of the shared first source dataset and the shared second source dataset. One or more queries received from the first database account that are included in the second approved-statements table are processed, as are one or more queries received from the second database account that are included in the first approved-statements table.

Abstract: A distributed database generates a cross reference table that cross references a first dataset from a first database account and a second dataset from a second database account. The distributed database receives a query directed to a combination of the first and second datasets, and generates an interim table in the first database account by applying the query to the cross reference table and the first dataset. The distributed database generates results data in the second database account by applying the query to the interim table and the second dataset, and stores the results data in the first database account.

Abstract: First and second database accounts respectively share first and second source datasets with one another in a data clean room. The first database account stores a first approved-statements table that contains database statements that are permitted by the first database account to be executed against a combination of the shared first source dataset and the shared second source dataset. The second database account stores a second approved-statements table that contains database statements that are permitted by the second database account to be executed against the combination of the shared first source dataset and the shared second source dataset. One or more queries received from the first database account that are included in the second approved-statements table are processed, as are one or more queries received from the second database account that are included in the first approved-statements table.

Abstract: A data compression system can include a neural compression artifact removal module (NCARM) is arranged to receive compressible data and output data with compression artifacts removed. A lossy compression module can be arranged to at least one of receive and send data to the NCARM and a decompression module arranged to at least one of receive and send data to the NCARM. In some embodiments, the NCARM sends data to the lossy compression module. Alternatively, the NCARM can receive data from the decompression module and/or data from the lossy compression module. Many lossy data compression schemes, including commonly available audio and video compression methods, can benefit from artifact removal.

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 distributed database generates a cross reference table that cross references a first dataset from a first database account and a second dataset from a second account. The distributed database receives a query directed to a combination of the first and second datasets, and generates an interim table in the first database account by applying the query to the cross reference table and the first dataset. The distributed database generates results data in the second database account by applying the query to the interim table and the second dataset, and stores the results data in the first database account.

Abstract: Embodiments of the present disclosure may provide a data clean room architecture that dynamically restricts data included in the clean room. The data clean room architecture can implement row access policy or dynamic data masking for row and column based restrictions of data provided through the clean room. The data clean room architecture can provide a limited set of data that does not require obfuscation of data for direction matching and correlation of data in the different datasets, such as matching user identifiers or emails.

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.

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.

Abstract: Embodiments of the present disclosure may provide a data clean room architecture that restricts data included in the clean room. The data clean room architecture can implement a policy to enable data restrictions for data shared between multiple parties via a distributed database. Multiple database accounts can implement validation instances to validate queries when received from other database accounts. One or more of the database accounts can provide a query template that is congruent with the validation instance for use by the other database accounts to generate queries against the data shared in the data clean room.

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.