Abstract: A computer-implemented method is provided that includes identifying an input dataset formatted as an input matrix, the input matrix including a plurality of rows and a plurality of columns. The computer-implemented method also includes dividing the input matrix into a plurality of input matrix blocks. Further, the computer-implemented method includes distributing the input matrix blocks to a plurality of different machines across a distributed filesystem, and sampling, by at least two of the different machines in parallel, at least two of the input matrix blocks. Finally, the computer-implemented method includes generating at least one sample matrix based on the sampling of the at least two of the input matrix blocks.

Abstract: A computer-implemented method is provided that includes identifying an input dataset formatted as an input matrix, the input matrix including a plurality of rows and a plurality of columns. The computer-implemented method also includes dividing the input matrix into a plurality of input matrix blocks. Further, the computer-implemented method includes distributing the input matrix blocks to a plurality of different machines across a distributed filesystem, and sampling, by at least two of the different machines in parallel, at least two of the input matrix blocks. Finally, the computer-implemented method includes generating at least one sample matrix based on the sampling of the at least two of the input matrix blocks.

Abstract: A computer-implemented method is provided that includes identifying an input dataset formatted as an input matrix, the input matrix including a plurality of rows and a plurality of columns. The computer-implemented method also includes dividing the input matrix into a plurality of input matrix blocks. Further, the computer-implemented method includes distributing the input matrix blocks to a plurality of different machines across a distributed filesystem, and sampling, by at least two of the different machines in parallel, at least two of the input matrix blocks. Finally, the computer-implemented method includes generating at least one sample matrix based on the sampling of the at least two of the input matrix blocks.

Abstract: A computer-implemented method is provided that includes identifying an input dataset formatted as an input matrix, the input matrix including a plurality of rows and a plurality of columns. The computer-implemented method also includes dividing the input matrix into a plurality of input matrix blocks. Further, the computer-implemented method includes distributing the input matrix blocks to a plurality of different machines across a distributed filesystem, and sampling, by at least two of the different machines in parallel, at least two of the input matrix blocks. Finally, the computer-implemented method includes generating at least one sample matrix based on the sampling of the at least two of the input matrix blocks.

Abstract: System and methods are provided for managing and mitigating privacy risks in a system having a network of processing elements. According to one method, there is receive a request for at least one output product from the system. The request includes a set of privacy parameters. At least one set of workflow generating strategies are created that results in the output product having a privacy risk value below a predefined threshold. At least one of the workflow generating strategies is deployed for automatically producing the at least one output product.

Abstract: A method for protecting information in a distributed stream processing system, including: assigning a principal label to a processing component; assigning a first channel label to a first communication channel that is input to the processing component; comparing the principal label to the first channel label to determine if the processing component can read data attributes of the first channel label; and reading the data attributes of the first channel label when the principal label is equal to or has precedence over the first channel label, wherein the principal label includes a read label and a write label and at least one of a selection label, an addition label or a suppression label.

Abstract: System and methods are provided for managing and mitigating privacy risks in a system having a network of processing elements. According to one method, there is receive a request for at least one output product from the system. The request includes a set of privacy parameters. At least one set of workflow generating strategies are created that results in the output product having a privacy risk value below a predefined threshold. At least one of the workflow generating strategies is deployed for automatically producing the at least one output product.

Abstract: A method for protecting information in a distributed stream processing system, including: assigning a principal label to a processing component; assigning a first channel label to a first communication channel that is input to the processing component; comparing the principal label to the first channel label to determine if the processing component can read data attributes of the first channel label; and reading the data attributes of the first channel label when the principal label is equal to or has precedence over the first channel label, wherein the principal label includes a read label and a write label and at least one of a selection label, an addition label or a suppression label.

Abstract: A system and method is provided for identifying the source of an unauthorized database disclosure. The system and method stores a plurality of past database queries and determines the relevance of the results of the past database queries (query results) to a sensitive table containing the unauthorized disclosed data. The system and method also ranks the past database queries based on the determined relevance. A list of the most relevant past database queries can then be generated which are ranked according to the relevance, such that the highest ranked queries on the list are most similar to said disclosed data. Three techniques used in embodiments of the invention include partial tuple matching, statistical linkage and deviation probability gain.

Abstract: A system and method is provided for identifying the source of an unauthorized database disclosure. The system and method stores a plurality of past database queries and determines the relevance of the results of the past database queries (query results) to a sensitive table containing the unauthorized disclosed data. The system and method also ranks the past database queries based on the determined relevance. A list of the most relevant past database queries can then be generated which are ranked according to the relevance, such that the highest ranked queries on the list are most similar to said disclosed data. Three techniques used in embodiments of the invention include partial tuple matching, statistical linkage and deviation probability gain.

Abstract: A method for protecting information in a distributed stream processing system, including: assigning a principal label to a processing component; assigning a first channel label to a first communication channel that is input to the processing component; comparing the principal label to the first channel label to determine if the processing component can read data attributes of the first channel label; and reading the data attributes of the first channel label when the principal label is equal to or has precedence over the first channel label, wherein the principal label includes a read label and a write label and at least one of a selection label, an addition label or a suppression label.