PEPR'19 2019 Aug. 12, 2019 to Aug. 13, 2019, Santa Clara, United States

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Title Speakers Summary Topic Types
The Privacy Engineering Mindset: From Design to Launch Sha Sundaram The privacy engineering mindset is a set of methodologies, tools and patterns you can employ ...
Our Friend from Privacy: Building and Harnessing Meaningful Working Relationships Amber Yust Privacy engineering doesn't work in a vacuum - we support a broader product, engineering, and ...
Addressing Privacy Risks of Targeted Advertising Stephen A. Weis A real world case study of how a large advertising network responded to a vulnerability ...
User-centric Privacy: Designing Effective Privacy Protections That Meet Users' Needs Florian Schaub Privacy engineering aims to respect and protect users' privacy. User studies provide insights on users' ...
Mandating the Forbidden: Balancing Privacy and Security in Production Abuse Systems Andy Schou Production-scale abuse systems at major platforms are increasingly the subject of both regulatory mandates and ...
Privacy as a Service: Building an End-to-End Consent Platform Roche Janken Confirming user consent in a consistent and transparent way is an important aspect of giving ...
Secure Messaging? More Like Secure Mess Gennie Portnoy There is no such thing as a perfect or one-size-fits-all messaging app. But people who ...
Now You See It, Now You Don't: Uber's Data Deletion Service Yash Shah Deletion at scale is a complex and iterative process because as more and more systems ...
If you are trying to register for LISA19, please complete your registration before or after this time period. Frank Kargl , Nicolas Papernot This talk will illustrate how learning with rigorous differential privacy guarantees is possible using TensorFlow ...
Differentially Private Data Release under Partial Information David Zeber Differential privacy (DP) is now a standard technique for releasing reports based on sensitive data. ...
Machine Learning at Scale with Differential Privacy in TensorFlow Nicolas Papernot This talk will illustrate how learning with rigorous differential privacy guarantees is possible using TensorFlow ...
Making Ethical Decisions for the Immersive Web Diane Hosfelt This talk focuses on building a platform that encourages ethical development and usage in an ...
Privacy in Unusual Contexts: A Case Study of A Theater Company Maggie Oates From social media APIs to VR performance art, artists are collecting, generating, and transforming digital ...
Panel on Privacy Engineering Career Paths Lea Kissner , Sha Sundaram , Giles Douglas , Alison Huml Privacy engineering is rapidly gaining significance across all sectors in industry. As privacy is a ...
Explore NIST's Privacy Engineering Collaboration Space Kaitlin Boeckl Explore NIST’s recently-launched Privacy Engineering Collaboration Space and learn how you can engage at this ...
How to Run an Engineering-Focused Privacy Program Giles Douglas Getting engineering involved with the review and design of projects leads to better interactions. I ...
Privacy, Triage, and Risk Yonatan Zunger Triage is often overlooked in threat management. But without understanding the significance of risks, making ...
Building and Scaling a Data Stewardship Program for Products Used by Hundreds of Millions of People Rebecca Weiss Data stewardship programs provide a unique solution to balancing the costs of adhering to privacy ...