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.

Abstract: In one embodiment, first weight information indicating a first set of delta values is obtained, where the first set of delta values includes a first delta value for each weight in a set of weights, the set of weights including a weight for each of a set of one or more parameters of a model. In addition, second weight information indicating a second set of delta values is obtained, where the second set of delta values includes a second delta value for each weight in the set of weights. Combined weight information including a combined set of delta values or a combined set of weights is generated based, at least in part, upon the first weight information and the second weight information.

Abstract: In one embodiment, an indication that a decision has been requested, selected, or applied with respect to one or more users may be obtained. After the indication that a decision that has been requested, selected, or applied is obtained, a value function may be updated, where the value function approximates an expected reward associated with the one or more users over time since the decision has been requested, selected, or applied with respect to the one or more users. The value function may be updated by performing or providing one or more updates to the value function, where a time at which each of the one or more updates is performed or provided is independent of activity of the one or more users.

Abstract: In one embodiment, real-time adaptive binning may be performed through the modification of a set of partitions. More particularly, a set of partitions separating one or more bins from one another may be identified, each of the one or more bins having boundaries including a lower boundary and an upper boundary, wherein the boundaries of the one or more bins together define a contiguous range of data values capable of being stored in the one or more bins. A data value may be obtained and added to one of the one or more bins according to the boundaries of the one or more bins. It may be determined whether to modify the set of partitions. The set of partitions may be modified according to a result of the determining step.

Abstract: In one embodiment, an indication that a decision has been requested, selected, or applied with respect to one or more users may be obtained. After the indication that a decision that has been requested, selected, or applied is obtained, a value function may be updated, where the value function approximates an expected reward associated with the one or more users over time since the decision has been requested, selected, or applied with respect to the one or more users. The value function may be updated by performing or providing one or more updates to the value function, where a time at which each of the one or more updates is performed or provided is independent of activity of the one or more users.

Abstract: In one embodiment, an indication of a Decision Request or an Update Request may be received, where the Update Request is activated independent of user activity. A user state pertaining to at least one user may be received, obtained, accessed or constructed. For the Decision Request, one or more actions may be scored according to one or more value functions associated with a computing device, a policy associated with the computing device may be applied to identify one of the scored actions as a decision, and an indication of the decision may be provided or applied. For the Update Request, the one or more value functions and/or the policy may be updated. An indication of updates to the one or more value functions and/or an indication of updates to the policy may be provided.

Abstract: In one embodiment, an indication that a decision has been requested, selected, or applied with respect to one or more users may be obtained. After the indication that a decision that has been requested, selected, or applied is obtained, a value function may be updated, where the value function approximates an expected reward associated with the one or more users over time since the decision has been requested, selected, or applied with respect to the one or more users. The value function may be updated by performing or providing one or more updates to the value function, where a time at which each of the one or more updates is performed or provided is independent of activity of the one or more users.

Abstract: In one embodiment, an indication of a Decision Request or an Update Request may be received, where the Update Request is activated independent of user activity. A user state pertaining to at least one user may be received, obtained, accessed or constructed. For the Decision Request, one or more actions may be scored according to one or more value functions associated with a computing device, a policy associated with the computing device may be applied to identify one of the scored actions as a decision, and an indication of the decision may be provided or applied. For the Update Request, the one or more value functions and/or the policy may be updated. An indication of updates to the one or more value functions and/or an indication of updates to the policy may be provided.

Abstract: In one embodiment, a set of boundaries may be obtained, where the set of boundaries includes boundaries for each of one or more bins. The boundaries for each of the one or more bins may include a lower boundary and an upper boundary, wherein the set of boundaries of the one or more bins together defines a contiguous range of data values capable of being stored in the one or more bins. A data value may be obtained. The data value may be added to one of the one or more bins according to the boundaries of the one or more bins. It may be determined whether to modify the set of boundaries. The set of boundaries may be adjusted according to a result of the determining step.

Abstract: Methods of storing data records produced from monitoring interactions between external agents and a system are described. The method defines specific interactions that occur between the external agents and the system as events of interest. A chain of interactions occurring during respective interaction sessions between a respective external agent and the system are monitored and events of interest occurring in the chain are determined. Data records from the monitored chain are produced, the respective data record including data identifying determined events of interest and data associated therewith. A profile identity, representative of the external agent, is assigned to each data record produced during an interaction session. Data records of individual events of interest are stored in a way ordered according to the type of event of interest and data records of events of interest occurring during an interaction session are stored in a way ordered according to assigned profile identity.

Abstract: In one embodiment, first weight information indicating a first set of delta values is obtained, where the first set of delta values includes a first delta value for each weight in a set of weights, the set of weights including a weight for each of a set of one or more parameters of a model. In addition, second weight information indicating a second set of delta values is obtained, where the second set of delta values includes a second delta value for each weight in the set of weights. Combined weight information including a combined set of delta values or a combined set of weights is generated based, at least in part, upon the first weight information and the second weight information.

Abstract: In one embodiment, first weight information indicating a first set of delta values is obtained, where the first set of delta values includes a first delta value for each weight in a set of weights, the set of weights including a weight for each of a set of one or more parameters of a model. In addition, second weight information indicating a second set of delta values is obtained, where the second set of delta values includes a second delta value for each weight in the set of weights. Combined weight information including a combined set of delta values or a combined set of weights is generated based, at least in part, upon the first weight information and the second weight information.

Abstract: In one embodiment, first weight information indicating a first set of delta values is obtained, where the first set of delta values includes a first delta value for each weight in a set of weights, the set of weights including a weight for each of a set of one or more parameters of a model. In addition, second weight information indicating a second set of delta values is obtained, where the second set of delta values includes a second delta value for each weight in the set of weights. Combined weight information including a combined set of delta values or a combined set of weights is generated based, at least in part, upon the first weight information and the second weight information.

Abstract: In one embodiment, a set of boundaries may be obtained, where the set of boundaries includes boundaries for each of one or more bins. The boundaries for each of the one or more bins may include a lower boundary and an upper boundary, wherein the set of boundaries of the one or more bins together defines a contiguous range of data values capable of being stored in the one or more bins. A data value may be obtained. The data value may be added to one of the one or more bins according to the boundaries of the one or more bins. It may be determined whether to modify the set of boundaries. The set of boundaries may be adjusted according to a result of the determining step.

Abstract: In one embodiment, real-time adaptive binning may be performed through the modification of a set of partitions. More particularly, a set of partitions separating one or more bins from one another may be identified, each of the one or more bins having boundaries including a lower boundary and an upper boundary, wherein the boundaries of the one or more bins together define a contiguous range of data values capable of being stored in the one or more bins. A data value may be obtained and added to one of the one or more bins according to the boundaries of the one or more bins. It may be determined whether to modify the set of partitions. The set of partitions may be modified according to a result of the determining step.

Abstract: The present invention relates to a method of storing data records produced from monitoring interactions between external agents and a system. The method comprises defining specific interactions that can occur between the external agents and the system as potential events of interest. A chain of interactions occurring during respective interaction sessions between a respective external agent and the system are monitored and potential events of interest occurring in the said chain are determined. Data records from the monitored chain of interactions are produced, the respective data record including data identifying determined potential events of interest and data associated therewith. A profile identity, representative of the external agent, is assigned to each data record produced during a said interaction session.