Guidelines for Evaluating Differential Privacy Guarantees: NIST SP 800-226 | Differential Privacy and Its Potential in Protecting Sensitive Data | A Conversation with Damien Desfontaines | Redefining CyberSecurity Podcast with Sean Martin

Redefining CyberSecurity

Guest: Damien Desfontaines, Staff Scientist at Tumult Labs

On Linkedin | https://www.linkedin.com/in/desfontaines/

On Twitter | https://twitter.com/TedOnPrivacy

On Mastodon | https://hachyderm.io/@tedted

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Host: Sean Martin, Co-Founder at ITSPmagazine [@ITSPmagazine] and Host of Redefining CyberSecurity Podcast [@RedefiningCyber]

On ITSPmagazine | https://www.itspmagazine.com/itspmagazine-podcast-radio-hosts/sean-martin

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This Episode’s Sponsors

Imperva | https://itspm.ag/imperva277117988

Devo | https://itspm.ag/itspdvweb

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Episode Notes

This episode of Redefining CyberSecurity features a deep discussion between host, Sean Martin and guest, Damien Desfontaines on the topic of Differential Privacy (DP) and its implications in the field of cybersecurity. Damien, who currently works in a startup, Tumult Labs, primarily focuses on DP concepts and has rich prior experience from working in the anonymization team at Google. He shares key insights on how differential privacy — a tool to anonymize sensitive data can be effectively used by organizations to share or publish data safely, thus opening doors for new business opportunities.

They discuss how differential privacy is gradually becoming a standard practice for companies wanting to share more data without incurring additional privacy risk. Damien also sheds light on the forthcoming guidelines from NIST regarding DP, which will equip organizations with a concrete framework to evaluate DP claims. Despite the positive dimension, Damien also discusses the potential pitfalls in the differential privacy implementation and the need for solid data protection strategies.

The episode concludes with an interesting conversation about how technology and risk mitigation controls can pave way for more business opportunities in a secure manner.

Key insights:

• Differential Privacy (DP) offers a mathematically proven methodology to anonymize sensitive data. It enables organizations to safely share or publish data, opening new business opportunities while adhering to privacy norms and standards.
• The forthcoming guidelines from NIST will equip organizations with a concrete framework to evaluate DP claims, fine-tune their privacy governance, and promote data governance within their operations.
• Implementing DP is complex and necessitates solid data protection strategies. Even with a strong mathematical foundation, the practical implementation of DP requires careful monitoring of potential vulnerabilities, illustrating the need for a holistic approach to data privacy.

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Resources

Inspiring post: https://www.linkedin.com/feed/update/urn:li:activity:7140071119859957762/

Guidelines for Evaluating Differential Privacy Guarantees: https://csrc.nist.gov/pubs/sp/800/226/ipd

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