3 papers
math.ST2025
Interpreting Network Differential Privacy
Jonathan Hehir, Xiaoyue Niu, Aleksandra Slavkovic
How do we interpret the differential privacy (DP) guarantee for network data? We take a deep dive into a popular form of network DP (--edge DP) to find that many of it…
cs.CR2024
Gaussian Differentially Private Human Faces Under a Face Radial Curve Representation
Carlos Soto, Matthew Reimherr, Aleksandra Slavkovic +1
In this paper we consider the problem of releasing a Gaussian Differentially Private (GDP) 3D human face. The human face is a complex structure with many features and inherently ti…
cs.CR2022
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…