4 papers
Certified but Private: Scalable Zero-Knowledge Proofs for Neural Network Guarantees
Youwei Zhong, Ben Merbaum, Timos Antonopoulos +4
With the growing deployment of machine learning models, formal guarantees of the robustness and fairness of these models have become increasingly important in safety-critical and l…
On a Conjecture for Parameterized st-Orientations
Charalampos Papamanthou
MaxSTN and MinSTN -- proposed by Papamanthou and Tollis (TCS 2008, JGAA 2010) -- are two algorithms for producing -orientations of biconnected graphs with long and short longes…
How Query Distribution Knowledge Breaks Multidimensional Encrypted Range Queries, With Guarantees
Daniel Blackley, Nathaniel Moyer, Charalampos Papamanthou +1
In this work, we show how knowledge of the query distribution, combined with access-pattern leakage, is sufficient to break multi-dimensional encrypted range queries, with provable…
Practical and Accurate Local Edge Differentially Private Graph Algorithms
Pranay Mundra, Charalampos Papamanthou, Julian Shun +1
The rise of massive networks across diverse domains necessitates sophisticated graph analytics, often involving sensitive data and raising privacy concerns. This paper addresses th…