Data Masking/Protection
Large Language Models have brought huge changes in how companies handle and study data, but they also bring data security risks. When businesses add LLMs to their processes, they need to safeguard private information. Read More
Data Masking/Protection
The identity fabrication rule is a deterministic but non-reversible data masking rule that can help you create realistic test PII for a whole record from just one original value. Read More
Data Masking/Protection
Data Classification in the IRI Workbench GUI is used to name and find specific kinds of data – usually Personally Identifiable Information (PII) – and to consistently apply chosen functions (e.g., Read More
Data Masking/Protection
A data masking tool can be an effective, if not essential, way to protect sensitive information stored in files and databases. As the numbers of data breaches, lawsuits, and data privacy laws continue to grow, companies must find ways to anonymize production and test data without sacrificing its utility for analytics, research, marketing, or development. Read More
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
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
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
Editors Note: This articles covers data anonymization as a form of data masking for privacy protection. In particular, it covers the concepts of quasi-identifiers and re-identification risk and the use of HIPAA data de-identification standards for protecting sensitive data in research through the use of anonymizing techniques like age blurring and demographic attribute blurring in conjunction with re-ID risk scoring. Read More
Data Masking/Protection
Random noise data masking, or blurring, is a common requirement for PII anonymization in healthcare data, particularly 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