Health 2.0 Presentation How BigPrivacy Enables Lawful Analytics

 

My name is Gary LaFever and I will demonstrate how BigPrivacy technology enables lawful Big Data analytics.

Static approaches to privacy fail to protect Big Data. However, complying with new dynamic data protection requirements actually enables even greater Big Data use.

When awarding Anonos BigPrivacy cool vendor status for Privacy Management, Gartner noted that common static privacy techniques fail to protect Big Data.

Neither HIPAA de-identification techniques protect privacy for dynamic data use. FTC rules that apply to data collected from fitness devices similarly fail. On the other extreme, Common Rule and related IRB restrictions can be so rigid that BigData breakthroughs are impossible.

Anonos BigPrivacy dynamically produces non-identifying Variant Twins of protected data to reconcile this conflict.

Earlier laws protected static data use. If data was used for a single purpose, it was protected. But combining or sharing data broke down the protection. New laws - including here in California - now require protection for dynamic data use.

BigPrivacy creates Variant Twins that maximize the lawful value of data in 3 steps: 1) De-Linking, 2) De-Risking and 3) Controlling the Linkability of data. Variant Twins reveal information that can be used for lawful Big Data analytics.

We start with protected health information which is static and exposes the individual data subject to potential privacy harms.

In the first step, data is automatically de-identified by de-linking it from identifying information. Each occurrence of a person's name is replaced with a different dynamically generated pseudonym that can be re-linked back to an individual's name but only under tightly controlled conditions.

The second step is de-risking. Here common data values are grouped into a common cohort class which is non-identifying and represented by a dynamically generated pseudonym.

In the third step, BigPrivacy dynamically classifies values within cohort classes for later controlled re-linking. In this example, age, gender, and race are represented by cohorts that do not reveal underlying values to protect against discrimination.

This enables the production of Variant Twins that support lawful Big Data analytics. Here, you see how many people are in each of the different groups without revealing identities.

Most importantly, only if a user has the authority and a lawful reason to do so, can they re-link data back to identities.

We invite you to watch a Ted Talk six minutes in length at anonos.com/tedtalk describing how BigPrivacy can enable maximum Big Data value. On the right-hand side is an article highlighting a heart-wrenching situation where the limitations of static privacy technologies make it impossible to identify children with similar symptoms as this young girl to help in her treat ent. But, with BigPrivacy technology, her doctors would not have to settle for this result

Prior static approaches to data protection are no longer lawful. But complying with new legal requirements enables even greater Big Data use. This is Big. This is BigPrivacy.