2 citations · 4 across the 4 of their papers we have counts for
4 papers
Privately Answering Queries on Skewed Data via Per Record Differential Privacy
Jeremy Seeman, William Sexton, David Pujol +1
We consider the problem of the private release of statistics (like aggregate payrolls) where it is critical to preserve the contribution made by a small number of outlying large en…
Statistical Data Privacy: A Song of Privacy and Utility
Aleksandra Slavkovic, Jeremy Seeman
To quantify trade-offs between increasing demand for open data sharing and concerns about sensitive information disclosure, statistical data privacy (SDP) methodology analyzes data…
Exact Privacy Guarantees for Markov Chain Implementations of the Exponential Mechanism with Artificial Atoms
Jeremy Seeman, Matthew Reimherr, Aleksandra Slavkovic
Implementations of the exponential mechanism in differential privacy often require sampling from intractable distributions. When approximate procedures like Markov chain Monte Carl…
Formal Privacy for Partially Private Data
Jeremy Seeman, Matthew Reimherr, Aleksandra Slavkovic
Differential privacy (DP) quantifies privacy loss by analyzing noise injected into output statistics. For non-trivial statistics, this noise is necessary to ensure finite privacy l…