Data Masking/Protection
To configure both JDBC and ODBC to support the Transport Layer Security (TLS) protocol, there are additional configuration steps that are needed when compared to this previous article. Read More
Big Data
Editor’s Note:
As of 2024 and DarkShield V5, the search and masking methods for these NoSQL DBs has been upgraded; please refer to the discussion and demonstration in this updated article instead! 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
Adding ‘random noise’ to data through blurring or perturbation is a data common anonymization requirement for researchers and marketers of protected health information (PHI) seeking to comply with the HIPAA Expert Determination Method security rule. 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
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
Data Masking/Protection
It is a common mistake to refer to data masking and data encryption interchangeably to mean the same things. While field-level encryption is considered one of many possible “data masking” functions, we define data masking and encryption as technically distinct processes below. Read More
