Health 2.0 Presentation How BigPrivacy Enables Lawful Analytics

Presentation Transcript
Read the press article about Anonos' presentation at the Health 2.0 event: News Article
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My name is Gary LaFever and I will demonstrate how BigPrivacy technology enables lawful Big Data analytics.
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Static approaches to privacy fail to protect Big Data. However, complying with new dynamic data protection requirements actually enables even greater Big Data use.
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When awarding Anonos BigPrivacy cool vendor status for Privacy Management, Gartner noted that common static privacy techniques fail to protect Big Data.
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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.
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Anonos BigPrivacy dynamically produces non-identifying Variant Twins of protected data to reconcile this conflict.
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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.
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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.
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We start with protected health information which is static and exposes the individual data subject to potential privacy harms.
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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.
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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.
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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.
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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.
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Most importantly, only if a user has the authority and a lawful reason to do so, can they re-link data back to identities.
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We invite you to watch a Ted Talk six minutes in length at 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
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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.

Are you facing any of these 4 problems with data?

You need a solution that removes the impediments to achieving speed to insight, lawfully & ethically

to Insight
Are you unable to get desired business outcomes from your data within critical time frames? 53% of CDOs cannot achieve their desired uses of data. Are you one of them?
Lack of
Do you have trouble getting access to the third-party data that you need to maximise the value of your data assets? Are third-parties and partners you work with worried about liability, or disruption of their operations?
Inability to
Are you unable to process data due to limitations imposed by internal or external parties? Do they have concerns about your ability to control data use, sharing or combining?
Are you unable to defend the lawfulness of your current data processing activities, or data processing you have done in the past?
Traditional privacy technologies focus on protecting data by putting it in “cages,” “containers,” or limiting use to centralised processing only. This limitation is done without considering the context of what the desired data use will be, including decentralised data sharing and combining. These approaches are based on decades-old, limited-use perspectives on data protection that severely minimise the kinds of data uses that remain available after controls have been applied. On the other hand, many other new data-use technologies focus on delivering desired business outcomes without considering that roadblocks may exist, such as those noted in the four problems above.
Anonos technology allows data to be accessed and processed in line with desired business outcomes (including sharing and combining data) with full awareness of, and the ability to remove, potential roadblocks.