Best Data De-Identification Tools for Hadoop

Unlike most data management systems, PHEMI Health DataLab is built with Privacy-by-Design principles, not as an add-on. This means privacy and data governance are built-in from the ground up, providing you with distinct advantages: Lets analysts work with data without breaching privacy guidelines Includes a comprehensive, extensible library of de-identification algorithms to hide, mask, truncate, group, and anonymize data. Creates dataset-specific or system-wide pseudonyms enabling linking and sharing of data without risking data leakage. Collects audit logs concerning not only what changes were made to the PHEMI system, but also data access patterns. Automatically generates human and machine-readable de- identification reports to meet your enterprise governance risk and compliance guidelines. Rather than a policy per data access point, PHEMI gives you the advantage of one central policy for all access patterns, whether Spark, ODBC, REST, export, and more

While protecting privacy, context, form, as well as integrity, must be retained. Data protection can be enhanced by de-sensitizing sensitive data and de-identifying it. Pseudonymize data for privacy compliance, analytics, and analytics. Obscured data preserves context and referential integrity, so that the masked data is usable in testing, analytics, and support environments. Informatica Persistent data masking is a high-performance, scalable data masking solution that protects confidential data such as credit card numbers and addresses. It creates realistic, de-identified data that can then be shared internally or externally. It can also be used to reduce the risk of data breaches occurring in nonproduction environments, create higher-quality test data, streamline development projects, and comply with data privacy regulations.

IBM InfoSphere®, Optim™, Data Privacy offers extensive capabilities to mask sensitive data in non-production environments such as development, testing, QA, or training. This single offering offers a variety transformation techniques that can replace sensitive information with fully functional, functionally masked data. Substrings, arithmetic, sequential or random number generation, date ageing, and concatenation are all examples of masking techniques. The context-aware masking capabilities allow data to be retained in a similar format as the original. Apply a range of masking techniques on-demand to transform personally-identifying information and confidential corporate data in applications, databases and reports. Data masking tools can be used to prevent misuse of information. They allow you to mask, obfuscate, and privatize personal information that is distributed across non-production environments.

Your IT organization can use flexible masking rules to limit sensitive data with flexible masking rules based upon the authentication level of a user. It ensures compliance by blocking, auditing and alerting users, IT personnel and outsourced teams that access sensitive information. Data masking solutions can be easily customized to meet different regulatory or business needs. Protect sensitive and personal information while supporting cloud-based, offshoring and outsourcing initiatives. Secure big data using dynamic masking of sensitive data in Hadoop.

Protect, Monitor, and Discover enterprise sensitive data across multiple platforms and environments. Automate your subject rights response and demonstrate regulatory compliance - all in one solution