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Found 4 papers, 1 papers with code
Measuring Re-identification Risk
In this work, we present a new theoretical framework to measure re-identification risk in such user representations.
Smooth Anonymity for Sparse Graphs
When working with user data providing well-defined privacy guarantees is paramount.
On the Pitfalls of Label Differential Privacy
We complement the negative results with a non-parametric estimator for the true privacy loss, and apply our techniques on large-scale benchmark data to demonstrate how to achieve a desired privacy protection.
Non-parametric Revenue Optimization for Generalized Second Price Auctions
To our knowledge, this is the first attempt to apply learning algorithms to the problem of reserve price optimization in GSP auctions.
