Abstract: Change fingerprinting is applied to a text file, database table, or data feed to determine the timeframe in which an identified “wild file” was generated, even when its file creation meta-data is missing. Each row in the data contains information on a single object. At least one column in the data contains an age for each object at the time the file was created. The age data can be used to determine the date the file was created, such as by using recognition processing or by looking at data that has been added or dropped from the file based on age. By identifying the timeframe in which the wild file was created, the data owner may greatly reduce the computational burden needed to determine if the wild file contains stolen data because it greatly reduces the universe of files that must be compared to the wild file.

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 for applying fingerprinting/watermarking of consumer data, and analyzing “wild files” of consumer data to assign a guilt score for a particular party who may have leaked the data, allows the owner of data sources (“Data Owners”) to identify and assert ownership of textual data that has been distributed outside of their firewall in the clear (i.e., without encryption), either intentionally or unintentionally, and assign guilt to parties misusing the data. The system can be used by Data Owners who transmit, lease, or sell data to individuals or organizations (“Trusted Third Parties” or “TTPs”) to recognize and assert ownership of their data in the case where one or more TTPs leaks the data (the leaked file is defined as a “Leaked Subset”) into the hands of others (“Bad Actors”) who either knowingly or unknowingly use the data illegally.

Abstract: Principal components analysis is applied to data sets to fingerprint the dataset or to compare the dataset to a “wild file” that may have been constructed from data found in the dataset. Principal components analysis allows for the reduction of data used for comparison down to a parsimonious compressed signature of a dataset. Datasets with different patterns among the variables will have different patterns of principal components. The principal components of variables (or a relevant subset thereof) in a wild file may be computed and statistically compared to the principal components of identical variables in a data provider's reference file to provide a score. This constitutes a unique and compressed signature of a file that can be used for identification and comparison with similarly defined patterns from other files.

Abstract: Principal components analysis is applied to data sets to fingerprint the dataset or to compare the dataset to a “wild file” that may have been constructed from data found in the dataset. Principal components analysis allows for the reduction of data used for comparison down to a parsimonious compressed signature of a dataset. Datasets with different patterns among the variables will have different patterns of principal components. The principal components of variables (or a relevant subset thereof) in a wild file may be computed and statistically compared to the principal components of identical variables in a data provider's reference file to provide a score. This constitutes a unique and compressed signature of a file that can be used for identification and comparison with similarly defined patterns from other files.

Abstract: A system for applying fingerprinting/watermarking of consumer data, and analyzing “wild files” of consumer data to assign a guilt score for a particular party who may have leaked the data, allows the owner of data sources (“Data Owners”) to identify and assert ownership of textual data that has been distributed outside of their firewall in the clear (i.e., without encryption), either intentionally or unintentionally, and assign guilt to parties misusing the data. The system can be used by Data Owners who transmit, lease, or sell data to individuals or organizations (“Trusted Third Parties” or “TTPs”) to recognize and assert ownership of their data in the case where one or more TTPs leaks the data (the leaked file is defined as a “Leaked Subset”) into the hands of others (“Bad Actors”) who either knowingly or unknowingly use the data illegally.