Amazon Technologies, Inc.

Abhishek Sharma

Anurag Windlass Gupta

Mehul A Shah

Gaurav Saxena

Prajakta Datta Damle

Mehul Y Shah

Stephen Michael Ash

Timothy Jones

Andrew Borthwick

Juliana Saussy

Kiran Kumar Chinta

James Claiborne Moore

Shehzad Qureshi

Todd Jeffrey Green

Pedrito Uriah Maynard-Zhang

Sergei Dobroshinsky

Yanzhu Ji

Dmytro Bogatov

Chethan Kommaranahalli Rudramuni

Adam Lawrence Joseph Heinermann

Alaykumar Navinchandra Desai

Techniques for priority-based masking policy selection in a database environment are described. Masking policies are defined and attached to columns of relational data for particular users or roles. The attachment of a masking policy to a column includes a user-specified priority value. When multiple policies could apply to a particular query, the conflict can be easily resolved and understood by use of the priority values, for example, by selecting a candidate policy having a highest priority value.

Priority-based masking policy selection in a database environment

Techniques for dynamic database redaction using protected encryption secret material are described. A masking policy is defined that includes a reference to a secret material stored by a secrets manager service. The masking policy further identifies a pseudonymous redaction function that utilizes a cryptographic function requiring such a secret material. The secrets manager service is configured to grant access to the secret material by an entity of the database service that executes queries, such as a leader node of a cluster. For a particular query, the cluster obtains the secret material from the secrets manager service in a secure manner, uses the secret material for applying the cryptographic function to values for redaction purposes, and deletes any copies of secret material thereafter.

Dynamic database redaction using protected secret material

Specified performance attributes may be used to configure machine learning transformations for ETL jobs. Performance attributes for a machine learning pipeline that applies a model to as part of a transformation for an ETL job may be used to configure a parameter in a stage of the machine learning pipeline. The configured stage may then be used when training the model. The trained machine learning pipeline may then be applied as part of a transformation operation included in an ETL job performed by the ETL system.

Using specified performance attributes to configure machine learning pipepline stages for an ETL job

Using specified performance attributes to configure machine learning pipeline stages for an ETL job

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Seattle, WA, United States of America

Abhishek Sharma