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Privacy-preserving parametric inference: a case for robust statistics
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Abstract: Differential privacy is a cryptographically-motivated approach to privacy
that has become a very active field of research over the last decade in
theoretical computer science and machine learning. In this paradigm one assumes
there is a trusted curator who holds the data of individuals in a database and
the goal of privacy is to simultaneously protect individual data while allowing
the release of global characteristics of the database. In this setting we
introduce a general framework for parametric infe ...
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Yuval Filmus
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Answer: When a question is asked about a paper, are the authors contacted?
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Andy
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It depends.
**Authors that are registered users:**
If an author of the paper is
- a registered user,
- known to be the author (se…
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There are no hard rules criteria for what makes a "good" post. The most important thing is that it either contains **information that's us…
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Use a distibution that's symmetric around the origin and normalize the results so they lie on the sphere. E.g. you can use a Gaussian. He…
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It is used to prove the bound in proposition 4.6 which is then used crucially in proposition 4.7 to prove the uniform boundedness of the op…
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