data masking Archives

Anonymizing Indirect Identifiers to Lower Re-ID Risk

by Claudia Irvine

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.

Data Masking/Protection

PII (Age/Date) Blurring in IRI FieldShield

by Craig Schien

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.

Data Masking/Protection

Native MongoDB Masking (and More)

by Chaitali Mitra

Note: This article covers the third and most recently available IRI customer method for statically masking or encrypting data in MongoDB through the IRI FieldShield product or IRI Voracity platform (both powered by IRI CoSort v10 and its support of the native MongoDB driver).

Data Masking/Protection

PII Masking & More in CSV Files

by Chaitali Mitra

This article demonstrates the manipulation of a CSV file using an IRI Workbench wizard. In fact, this example shows how PII can be masked from almost any IRI job wizard.

Data Masking/Protection

Scoring FieldShield-Masked Data for Re-Identification Risk

by Dmitry Kulakov

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.

Data Masking/Protection

Defining Startpoint Data Security

by David Friedland

IRI has discussed startpoint security in further detail with the Outlook Series in a segment about data masking.

This article defines what we’d like to call “startpoint security” mostly by virtue of a comparison to endpoint security.

Big Data

Which Data Masking Function Should I Use?

by David Friedland

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.

Data Masking/Protection

Data Security Governance

by Donna Davis

The Latest Data Risk Landscape

It’s the end of 2016, and enterprise data continues to change rapidly in form, size, use, and residence. Rarely does it remain in siloed constructs anymore, limited to certain business units or untouched by the outside world.

Big Data

A Fresh Look at Data Preparation

by David Friedland

To analyze data successfully, it must first be prepared successfully. Poor quality data creates poor results. Worse yet is data that takes too long to collect and clean because it is too big or too foreign.