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Practical Considerations for Differential Privacy
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Differential privacy is the gold standard for statistical data release. Used by governments, companies, and academics, its mathematically rigorous guarantees and worst-case assumptions on the strength and knowledge of attackers make it a robust and compelling framework for reasoning about privacy. However, even with landmark successes, differential privacy has not achieved widespread adoption in everyday data use and data protection. In this work we examine some of the practical obstacles that stand in the way.
Forward citations
Cited by 2 Pith papers
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Edit-Neighboring Data Streams and Privacy under Continual Observation
Under the new 'edit-neighboring' privacy definition, private continual counting is possible with only polylogarithmic error, while every additive-noise counter provably needs polynomial error.
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Synopsis: Secure and private trend inference from encrypted semantic embeddings
A system combining local and central differential privacy with malicious-secure MPC lets journalists query semantic embeddings of donated E2EE messages without accessing the underlying texts.
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