Data Anonymization Professional Certification

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Data Anonymization Professional Certification

Developing the Knowledge and Experience

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Presenter

Luk Arbuckle, Director of Analyticslarbuckle@privacyanalytics.ca

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Finding an Expert

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Motivations for Anonymization

Population HealthRegulation

Comparative BenchmarkingReleasing Data

Detecting Fraud

Monetizing Data Compliance

Accelerating Research

Data Complexity

Re-identification Risk

Post-marketing surveillance

Data Breach

Marketing

Reputation

Ethics

Software Testing

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Safeguard and Enable Data for Secondary Purposes

•PARAT automates the masking and de-identification of data

•Anonymize structured and unstructured data

•Peer-reviewed methodologies and value-added services that certify data as de-identified

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While the HIPAA Privacy Rule only applies to the anonymization of U.S. regulated health data, it is a useful and relevant standard for the anonymization of all data.

HIPAA Privacy Rule

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• A person with appropriate knowledge of and experience with generally accepted statistical and scientific principles and methods for rendering information not individually identifiable:I. Applying such principles and methods; determines that the risk is “very

small” that the information could be used, alone or in combination with other reasonably available information by an anticipated recipient to identify an individual who is a subject of the information; and

II. Documents the methods and results of the analysis that justify such determination

Expert Determination (Statistical) Method

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Shortage of Anonymization Professionals

• Ad-hoc methods will be used, and data will have lower analytic utility

• Analytics will not occur, impeding research and data monetization

• Non-experts may perform the anonymization, risking disclosure

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Defining Expertise Required to Anonymize Data

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Resources to Get You Started

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Enterprise Re-Identification Risk Management Framework

• Develop the skills needed to manage the risk of re-identification when data is shared for secondary purposes.

• Ensure that responsible privacy and security protocols are in place to allow the ethical use of these large data stores of sensitive personal or protected health information.

• Standards and guidelines for protecting this information need to be implemented.

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Appropriate Knowledge to Anonymize Data

• The Case for Anonymizing Data• Concepts and Definitions• Selecting Identifiers• Risk Measurement for Public Data Releases• Setting Thresholds for Public Data Releases• Risk Measurement for Non-Public Data Releases• Risk Management• Implementing Anonymization Methods

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Appropriate Experience to Anonymize Data

Anonymize two real data sets under the coaching and guidance of someone who is already a seasoned expert (someone involved in the anonymization of a minimum of ten data sets).

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Automating Anonymization

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Certification Exam

The ability to define very small risk in a defensible way; the ability to select appropriate metrics and to measure the risk of re-identification; and the ability to transform the data to ensure that the measured risk is indeed very small.

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Who is the Anonymization Professional?

• The background of the candidate can be data analysis, database management, health data management, statistician, or software programming.

• Automation means that it’s possible for individuals who are not statisticians or data analysts to anonymize data sets.

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Data Quality1 Analytic Granularity2 Depth of

Insight3

Ensuring de-identified data has analytic usefulness by minimizing the amount of distortion but still ensure that re-identification risk is very small

Allowing users to configure the extent of de-identification to match the characteristics of the analysis that is anticipated

Enabling analysis of the total patient health experience, to compile a complete picture of this experience from multiple data sources and types

Balancing Privacy with Data Utility