Data Masking/Protection
As you may already know, IRI DarkShield produces search annotations and masking results with every operation. These log files can contain a large volume of data, including:
metadata about the file/database source(s) what data was found as a match which data class search matcher matched the data how the data was masked (if it was) where matches occurred within each file or database. Read More
Data Masking/Protection
Abstract: Previously the DarkShield API for Files could perform black-box redaction of primary account numbers (PANs, also known as CCNs) on credit cards only within defined areas in the image . Read More
Data Masking/Protection
Abstract: With hundreds of thousands of medical imaging devices in use, DICOM is one of the most widely deployed healthcare messaging standards in the world; billions of DICOM images are currently in use for clinical care. Read More
Data Masking/Protection
Quasi-identifiers, or indirect identifiers, are personal attributes that are true about, but not necessarily unique, to an individual. Examples are one’s age or date of birth, race, salary, educational attainment, occupation, marital status and zip code. Read More
Data Masking/Protection
One of the biggest concerns with releasing a dataset is the risk that a potential attacker can identify the owners of particular records. Even though masking or removing unique identifiers, like names and Social Security Numbers, can reduce that risk substantially, it may still not be enough. Read More
Big Data
According to Simson L. Garfinkel at the NIST Information Access Division’s Information Technology Laboratory,
De-identification is not a single technique, but a collection of approaches, algorithms, and tools that can be applied to different kinds of data with differing levels of effectiveness. Read More
Data Masking/Protection
Editor’s Note: Note that this article refers to older but still supported methods for obfuscating PII elements like dates of birth and ages in IRI FieldShield. Read More