8 citations · 10 across the 4 of their papers we have counts for
4 papers
"Having Confidence in My Confidence Intervals": How Data Users Engage with Privacy-Protected Wikipedia Data
Harold Triedman, Jayshree Sarathy, Priyanka Nanayakkara +4
In response to calls for open data and growing privacy threats, organizations are increasingly adopting privacy-preserving techniques such as differential privacy (DP) that inject…
Practitioners' Perspectives on a Differential Privacy Deployment Registry
Priyanka Nanayakkara, Elena Ghazi, Salil Vadhan
Differential privacy (DP) -- a principled approach to producing statistical data products with strong, mathematically provable privacy guarantees for the individuals in the underly…
Models Matter: Setting Accurate Privacy Expectations for Local and Central Differential Privacy
Mary Anne Smart, Priyanka Nanayakkara, Rachel Cummings +2
Differential privacy is a popular privacy-enhancing technology that has been deployed both in industry and government agencies. Unfortunately, existing explanations of differential…
What Are the Chances? Explaining the Epsilon Parameter in Differential Privacy
Priyanka Nanayakkara, Mary Anne Smart, Rachel Cummings +2
Differential privacy (DP) is a mathematical privacy notion increasingly deployed across government and industry. With DP, privacy protections are probabilistic: they are bounded by…