Abstract: A computer implemented data product release method or system. The data product release is derived from a sensitive dataset using a privacy protection system such as a differentially private system. The privacy protection parameters, such as noise addition magnitude, are configurable as part of the data product release method or system to alter the balance between maintaining privacy of the sensitive dataset and making the data product release useful.

Abstract: A system allows the identification and protection of sensitive data in a multiple ways, which can be combined for different workflows, data situations or use cases. The system scans datasets to identify sensitive data or identifying datasets, and to enable the anonymisation of sensitive or identifying datasets by processing that data to produce a safe copy. Furthermore, the system prevents access to a raw dataset. The system enables privacy preserving aggregate queries and computations. The system uses differentially private algorithms to reduce or prevent the risk of identification or disclosure of sensitive information. The system scales to big data and is implemented in a way that supports parallel execution on a distributed compute cluster.

Abstract: A system allows the identification and protection of sensitive data in a multiple ways, which can be combined for different workflows, data situations or use cases. The system scans datasets to identify sensitive data or identifying datasets, and to enable the anonymisation of sensitive or identifying datasets by processing that data to produce a safe copy. Furthermore, the system prevents access to a raw dataset. The system enables privacy preserving aggregate queries and computations. The system uses differentially private algorithms to reduce or prevent the risk of identification or disclosure of sensitive information. The system scales to big data and is implemented in a way that supports parallel execution on a distributed compute cluster.

Abstract: A computer implemented method is presented for querying a dataset that contains sensitive attributes. The method comprises the steps of receiving a query specification, generating a set of aggregate statistics derived from the sensitive dataset based on the query specification and encoding the set of aggregate statistics using a set of linear equations. The relationships of each sensitive attribute represented in the set of aggregate statistics are also encoded into the set of linear equations.

Abstract: A computer implemented data product release method or system. The data product release is derived from a sensitive dataset using a privacy protection system such as a differentially private system. The privacy protection parameters, such as noise addition magnitude, are configurable as part of the data product release method or system to alter the balance between maintaining privacy of the sensitive dataset and making the data product release useful.

Abstract: A system allows the identification and protection of sensitive data in a multiple ways, which can be combined for different workflows, data situations or use cases. The system scans datasets to identify sensitive data or identifying datasets, and to enable the anonymisation of sensitive or identifying datasets by processing that data to produce a safe copy. Furthermore, the system prevents access to a raw dataset. The system enables privacy preserving aggregate queries and computations. The system uses differentially private algorithms to reduce or prevent the risk of identification or disclosure of sensitive information. The system scales to big data and is implemented in a way that supports parallel execution on a distributed compute cluster.