1 citations · 1 across the 1 of their papers we have counts for
2 papers
cs.CR2021★ 1 cited
Learning Numeric Optimal Differentially Private Truncated Additive Mechanisms
David M. Sommer, Lukas Abfalterer, Sheila Zingg +1
Differentially private (DP) mechanisms face the challenge of providing accurate results while protecting their inputs: the privacy-utility trade-off. A simple but powerful techniqu…
cs.CR2019
Differential privacy with partial knowledge
Damien Desfontaines, Esfandiar Mohammadi, Elisabeth Krahmer +1
Differential privacy offers formal quantitative guarantees for algorithms over datasets, but it assumes attackers that know and can influence all but one record in the database. Th…