Abstract: Systems and methods for manipulation of private information in untrusted environments are disclosed. In one embodiment, in a trusted computing environment comprising at least one computer processor, for a plurality of data records, a method for manipulation of private information in untrusted environments may include: (1) separating each data record into a confidential data attribute and a non-confidential data attribute; (3) calculating an encrypted value for the confidential data attribute using an encryption key; (4) calculating an authentication value for the confidential data attribute using a hash value key; (5) associating the encrypted value and the authentication value in a protected data set; and (6) associating the non-confidential data record with the associated encrypted value and the authentication value; and (7) exporting the protected data set to an untrusted computing environment.

Abstract: Systems and methods for manipulation of private information in untrusted environments are disclosed. In one embodiment, in a trusted computing environment comprising at least one computer processor, for a plurality of data records, a method for manipulation of private information in untrusted environments may include: (1) separating each data record into a confidential data attribute and a non-confidential data attribute; (3) calculating an encrypted value for the confidential data attribute using an encryption key; (4) calculating an authentication value for the confidential data attribute using a hash value key; (5) associating the encrypted value and the authentication value in a protected data set; and (6) associating the non-confidential data record with the associated encrypted value and the authentication value; and (7) exporting the protected data set to an untrusted computing environment.

Abstract: According to an embodiment of the present invention, a federated context collaboration architecture comprises: a context store that stores contexts consumed and produced by participants, where a context represents a description of a dataset that provides a consistent flexible and technology independent mechanism for referencing data; each context comprises a context key; a context state and a context class; a context producer interface programmed to: define and reference datasets, data exchange, and data dependencies associated with each context; a context engine coupled to the context database and the context producer interface to communicate at least the state of data, data lineage and dependencies of data used to integrate and coordinate processes across different domains and infrastructures; a method for optimized selective priming of testing environments based on datasets necessary and sufficient for designated test suite; and a communication interface that communicates a defined metadata profile of each

Abstract: A risk analysis method uses a multi-dimensional risk representation that allows a standard OLAP engine to perform analysis on multi-dimensional data corresponding to a portfolio of financial positions. The analysis includes context-dependent, heterogeneous aggregation functions. The multi-dimensional data is represented as a multi-layered multi-dimensional cube (“outer” cube), which consists of dimensions and cells. Each cell includes a set of coordinates and an inner multi-dimensional cube (“inner” cube). Dimensions of the inner cube include all dimensions required for aggregations. Dimensions of the outer cube include only dimensions needed for context (or reporting). An aggregation is performed on the set of measures of the inner cube based on a context for the aggregation provided by the outer cube.

Abstract: A risk analysis method uses a multi-dimensional risk representation that allows a standard OLAP engine to perform analysis on multi-dimensional data corresponding to a portfolio of financial positions. The analysis includes context-dependent, heterogeneous aggregation functions. The multi-dimensional data is represented as a multi-layered multi-dimensional cube (“outer” cube), which consists of dimensions and cells. Each cell includes a set of coordinates and an inner multi-dimensional cube (“inner” cube). Dimensions of the inner cube include all dimensions required for aggregations. Dimensions of the outer cube include only dimensions needed for context (or reporting). An aggregation is performed on the set of measures of the inner cube based on a context for the aggregation provided by the outer cube.

Abstract: A system and method for performing Value at Risk (VaR) analysis at a large volume scale. The system employs two basic types of elements in its architecture: controllers and brokers. Controllers are engines that perform actual processing of data, while brokers manage the access to and from the data resources. Controllers of the present invention have three main components, an input queue, a manager and workers. The controllers retrieve units of work from the incoming queue, process the units, and place the result onto an outgoing queue. The outgoing queue of one controller is shared with the next element in the processing chain. Brokers are responsible for maintaining pool of common resources and providing access to those resources to a requestor (i.e., a controller). In the preferred embodiment, the resource is a data source, such as a database containing market pricing data. The broker accesses the data source though an adapter.