Scalable Strategies For Protecting Data Privacy In Your Shared Data Sets

Summary

There are many dimensions to the work of protecting the privacy of users in our data. When you need to share a data set with other teams, departments, or businesses then it is of utmost importance that you eliminate or obfuscate personal information. In this episode Will Thompson explores the many ways that sensitive data can be leaked, re-identified, or otherwise be at risk, as well as the different strategies that can be employed to mitigate those attack vectors. He also explains how he and his team at Privacy Dynamics are working to make those strategies more accessible to organizations so that you can focus on all of the other tasks required of you.

Announcements

• Hello and welcome to the Data Engineering Podcast, the show about modern data management
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• Your host is Tobias Macey and today I’m interviewing Will Thompson about managing data privacy concerns for data sets used in analytics and machine learning

Interview

• Introduction
• How did you get involved in the area of data management?
• Data privacy is a multi-faceted problem domain. Can you start by enumerating the different categories of privacy concern that are involved in analytical use cases?
• Can you describe what Privacy Dynamics is and the story behind it?
  • Which categor(y|ies) are you focused on addressing?
• What are some of the best practices in the definition, protection, and enforcement of data privacy policies?
  • Is there a data security/privacy equivalent to the OWASP top 10?
• What are some of the techniques that are available for anonymizing data while maintaining statistical utility/significance?
  • What are some of the engineering/systems capabilities that are required for data (platform) engineers to incorporate these practices in their platforms?
• What are the tradeoffs of encryption vs. obfuscation when anonymizing data?
• What are some of the types of PII that are non-obvious?
• What are the risks associated with data re-identification, and what are some of the vectors that might be exploited to achieve that?
  • How can privacy risks mitigation be maintained as new data sources are introduced that might contribute to these re-identification vectors?
• Can you describe how Privacy Dynamics is implemented?
  • What are the most challenging engineering problems that you are dealing with?
• How do you approach validation of a data set’s privacy?
• What have you found to be useful heuristics for identifying private data?
  • What are the risks of false positives vs. false negatives?
• Can you describe what is involved in integrating the Privacy Dynamics system into an existing data platform/warehouse?
  • What would be required to integrate with systems such as Presto, Clickhouse, Druid, etc.?
• What are the most interesting, innovative, or unexpected ways that you have seen Privacy Dynamics used?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on Privacy Dynamics?
• When is Privacy Dynamics the wrong choice?
• What do you have planned for the future of Privacy Dynamics?

Contact Info

• LinkedIn
• @willseth on Twitter

Parting Question

• From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

• Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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Links

• Privacy Dynamics
• Pandas
  • Podcast Episode – Pandas For Data Engineering
• Homomorphic Encryption
• Differential Privacy
• Immuta
  • Podcast Episode

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA