Ketch Kloud, Inc.

Microsoft Technology Licensing, LLC

Rapt, Inc.

Salesforce, Inc.

Switchbit, Inc.

Vivek Vaidya

Yacov Salomon

Seth Yates

Tom A Chavez

Anton Winter

Samuel Alexander

Maxwell Anderson

Paul Dagum

Pascal M Bensoussan

Michael Goldbach

Balazs Kralik

Adam Galper

Leonardo Dagum

Vance Maverick

Mala Anand

David P Mackler

Mihir R Mehta

Methods, systems, and devices for data processing are described. Some systems may support data processing permits and cryptographic techniques tying user consent to data handling. By tying user consent to data handling, the systems may comply with data regulations on a technical level and efficiently update to handle changing data regulations and/or regulations across different jurisdictions. For example, the system may maintain a set of data processing permits indicating user consent for the system to use a user's data for particular data processes. The system may encrypt the user's data using a cryptographic key (e.g., a cryptographic nonce) and may encrypt the nonce using permit keys for any permits applicable to that data. In this way, to access a user's data for a data process, the system may first verify that a relevant permit indicates that the user complies with the requested process prior to decrypting the user's data.

Data processing permits system with keys

Methods, systems, and devices for data processing are described. Some database systems may implement data processing permits to manage data access. A database system may use encryption schemes to tie permits to data (e.g., cryptographically ensuring that the system follows data regulations). To support queries for a database implementing such encryption schemes, the database may implement a proxy. When the system receives a query, the database proxy may intercept and transform the query based on the encryption schema of the database. The database proxy may execute the transformed query at the database, receive encrypted query results in response, and decrypt the results for use by the querying application. Additionally, the system may access relevant data processing permits to support querying operations. For example, the system may use permits when transforming the query, executing the query in the database, preparing query results for the querying application, or any combination thereof.

Managing queries with data processing permits

Methods, systems, and devices for data processing are described. Some database systems may support differential privacy for encrypted data. For example, a database may store user data as ciphertext. A system may receive a statistical query for the user data and may identify a relevant differential privacy mechanism. The system may transform the query to operate on encrypted data while including a noisification function based on the mechanism. The system may execute the transformed query at the database, involving adding noise to the query result according to the noisification function without decrypting the data. For example, the system may leverage homomorphic encryption techniques to inject the noise while the data remains encrypted. The database may return the noisified, encrypted query results, which the system may decrypt for statistical analysis. By applying differential privacy on the encrypted data, the system may avoid exposing any private user information throughout the process.

Differential privacy for encrypted data

Methods, systems, and devices for data processing are described. In some systems, data pipelines may be implemented to handle data processing jobs. To improve data pipeline flexibility, the systems may use separate pipeline and policy declarations. For example, a pipeline server may receive both a pipeline definition defining a first set of data operations to perform and a policy definition including instructions for performing a second set of data operations, where the first set of data operations is a subset of the second set. The server may execute a data pipeline based on a trigger (e.g., a scheduled trigger, a received message, etc.). To execute the pipeline, the server may layer the policy definition into the pipeline definition when creating an execution plan. The server may execute the execution plan by performing a number of jobs using a set of resources and plugins according to the policy definition.

Policy handling for data pipelines

A system for reliable message delivery includes a storage unit configured to store a message, and a message stored in the storage unit, wherein the message is persisted in the storage unit. The system also includes a status information associated with the message, the status information capable of indicating a first status. The system further includes a module coupled to the storage unit. The module is operable to execute on a computer and transmit the message to a second computer. The module is also operable to receive an acknowledgement from the second computer, and, in response to receiving the acknowledgement, the module is operable to indicate a second status in the status information. A method for providing reliable message delivery includes providing a storage unit configured to store a message and a message stored in the storage unit, wherein the message is persisted in the storage unit. The method also includes providing a record corresponding to the message, wherein the record is operable to store status information for the message. The method further includes storing a first status in the record, transmitting the message to a destination, and, in response to receiving an acknowledgement from the destination, storing a second status in the record.

System and method for reliable message delivery

A representative implementation of a method begins when a planner captures assumptions about products and components in an entity called a 'scenario.' A scenario is the parameterization of all the demand, financial, and operational information for a portfolio of products and components across a set of time buckets (planning periods). The planner then specifies a component plan to be analyzed, which identifies the quantities of each resource that will be used or procured during each planning period. A request for analysis includes one or more analysis parameters that will be used in order to evaluate the performance and the risks associated with the component plan and scenario. The request for analysis is submitted to an analysis engine for calculation of risk indicators. The analytical engine calculates all the performance indicators and returns the results. The results are then typically stored in a database or other persistent storage system.

Method and apparatus for component plan analysis under uncertainty

In one implementation, a volatility of expected service usage is calculated along with an expected revenue. User input provides estimates of service usage such as an estimated high usage, an estimated low usage, and a swing range of percentiles for the high and low usage estimates, for each time period in a plurality of future time periods. A user also provides a schedule of prices that are based on usage rates. A model volatility is calculated based on the swing range of percentiles, the estimated high and low usage, and a cumulative density function, such as a cumulative lognormal density function, for each time period in a plurality of time periods. The expected usage and the schedule of prices, along with any discount plans, are also used to calculate an estimated revenue.

Method and apparatus for utility pricing analysis

A method and apparatus for component plan analysis under uncertainty is disclosed. A representative implementation of this method begins when a planner captures assumptions about products and components in an entity called a 'scenario.' A scenario is the parameterization of all the demand, financial, and operational information for a portfolio of products and components across a set of time buckets (planning periods). The planner then specifies a component plan to be analyzed. The component plan identifies the quantities of each resource that will be used or procured during each planning period. The planner then generates a request for analysis including one or more analysis parameters that will be used in order to evaluate the performance and the risks associated with the component plan and scenario. The request for analysis is submitted to an analysis engine for calculation of risk indicators. The analytical engine calculates all the performance indicators and returns the results. The results are then typically stored in a database or other persistent storage system.

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Vivek Vaidya