Abstract: A system and method for extracting search terms for corresponding data elements from a natural language document identifies meaningful words within the context; identifies and structures the keywords; expounds on the keywords to optimize the search results; and captures the most relevant data elements from the corresponding database. Predetermined demographic characteristics and short (one- or two-word) search phrases that capture descriptors of behavioral characteristics are structured in the process. The result of the completed process yields a parameter set naming demographic and behavioral characteristics along with a structure that is optimized for search within a database comprising a large number of data elements.

Abstract: A system and method for identifying a leaked data file and assigning guilt to one or more suspected leakers proceeds through a plurality of levels. At a first level, primary watermark detection occurs. Data is inserted into a subset of data to determine correlation with data in the suspected leaked file. The guilt probability that results is then weighted based on the number of bits matched. In a second level, another search process is performed for detecting additional salt-related patterns. The guilt score is then computed for every detected recipient identifier for the suspected leaked data file, and the relative guilt of these recipients is weighted. In a third layer, the statistical distribution of data in the suspected leaked file is compared with that of corresponding data in the reference files. After this layer is complete, the average of guilt scores across each of the layers is calculated.

Abstract: A system and method for statistical fingerprinting of structured datasets begins by dividing the structured database into groups of data subsets. These subsets are created based on the structure of the data; for example, data delineated by columns and rows may be broken into subsets by designating each column as a subset. A fingerprint is derived from each subset, and then the fingerprint for each subset is combined in order to create an overall fingerprint for the dataset. By applying this process to a “wild file” of unknown provenance, and comparing the result to a data owner's files, it may be determined if data in the wild file was wrongfully acquired from the data owner.

Abstract: A system and method for statistical fingerprinting of structured datasets begins by dividing the structured database into groups of data subsets. These subsets are created based on the structure of the data; for example, data delineated by columns and rows may be broken into subsets by designating each column as a subset. A fingerprint is derived from each subset, and then the fingerprint for each subset is combined in order to create an overall fingerprint for the dataset. By applying this process to a “wild file” of unknown provenance, and comparing the result to a data owner's files, it may be determined if data in the wild file was wrongfully acquired from the data owner.

Abstract: A system and method for extracting search terms for corresponding data elements from a natural language document identifies meaningful words within the context; identifies and structures the keywords; expounds on the keywords to optimize the search results; and captures the most relevant data elements from the corresponding database. Predetermined demographic characteristics and short (one- or two-word) search phrases that capture descriptors of behavioral characteristics are structured in the process. The result of the completed process yields a parameter set naming demographic and behavioral characteristics along with a structure that is optimized for search within a database comprising a large number of data elements.

Abstract: A system and method for identifying a leaked data file and assigning guilt to one or more suspected leakers proceeds through a plurality of levels. At a first level, primary watermark detection occurs. Data is inserted into a subset of data to determine correlation with data in the suspected leaked file. The guilt probability that results is then weighted based on the number of bits matched. In a second level, another search process is performed for detecting additional salt-related patterns. The guilt score is then computed for every detected recipient identifier for the suspected leaked data file, and the relative guilt of these recipients is weighted. In a third layer, the statistical distribution of data in the suspected leaked file is compared with that of corresponding data in the reference files. After this layer is complete, the average of guilt scores across each of the layers is calculated.