Liveramp, Inc.

Arthur Coleman

Martin Rose

Tsz Ling Christina Leung

Michael Anderson

Matt LeBaron

Christina Tsz Ling Leung

Chivon Powers

Joe Hsy

Ray Kraus

Natarajan Shankar

Charles Zinsmeyer

The invention is a system based on decentralized ledgers to enable compliance with privacy regulations. A consumer identifier (consumer ID) allows consent provenance to be saved in a shared decentralized ledger. The consent ID also empowers consumers to query as to how their data has been moved through the data supply chain. The consumer data itself is not stored in the blockchain, only consents and data transfer events. A consent API allows companies to enter the original consumer consent in a consent ledger; a data transfer API allows companies to record transfers from one to another; and a subscription API allows companies to be notified of changes to a given consent.

Consent provenance and compliance tracking over a complex consumer data supply chain using blockchain distributed ledger

A system for 'horizontal' salting of database tables, text files, and data feeds utilizes a key field and character position within that field (the 'Key Character') and a Salting Field, which contains content that can legitimately be in one of at least two states without impacting the usefulness of the data. A unique identifier, which is assigned to the recipient of the data, is hidden within the data by using the variations of the states in the Salting Field, with the value of the Key Character identifying the position within the unique identifier. This type of salting is invisible to the recipient of the data file, does not alter the accuracy of the data, and can be made unique for a particular party receiving data files or unique for each data file.

Salting text in database tables, text files, and data feeds

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 (&#x201c;Trusted Third Parties&#x201d; or &#x201c;TTPs&#x201d;) 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 &#x201c;Leaked Subset&#x201d;) into the hands of others (&#x201c;Bad Actors&#x201d;) who either knowingly or unknowingly use the data illegally.

Data watermarking and fingerprinting system and method

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.

Change fingerprinting for database tables, text files, and data feeds

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.

System and method for identifying leaked data and assigning guilt to a suspected leaker

A system and method for salting and fingerprinting database tables, text files, data feeds, and the like, first resorts the data according to a field or fields in the data set. A salting recipe is selected and applied to the sorted data. A fingerprint of the data is captured after sorting and salting. The data is then restored to its original order before being sent to a trusted third party. Because the data owner retains information concerning the sorting sorting sonar technique, salting technique, and the fingerprint, the data owner can reconstruct the unsalted file from the salted file, and can determine if a wild file contains data from the data file. The salting is difficult to detect by the bad actor, even if the bad actor is aware that the data has been salted.

Salting text and fingerprinting in database tables, text files, and data feeds

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.

Statistical fingerprinting of large structure datasets

A system for 'horizontal' salting of database tables, text files, and data feeds utilizes a key field and character position within that field (the 'Key Character') and a Salting Field, which contains content that can legitimately be in one of at least two states without impacting the usefulness of the data. A unique identifier, which is assigned to the recipient of the data, is hidden within the data by using the variations of the states in the Salting Field to represent a binary 0 or 1, with the value of the Key Character identifying the bit position of the binary 0 or 1 within the unique identifier. This type of salting is invisible to the recipient of the data file, does not alter the accuracy of the data, and can be made unique for a particular party receiving data files or unique for each data file.

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Carmel Valley, CA, United States of America

Arthur Coleman