3 citations · 3 across the 18 of their papers we have counts for
22 papers
Decisive Margins in Differentially Private Voting
Quentin Hillebrand, Pasin Manurangsi, Vorapong Suppakitpaisarn +1
Differential privacy protects individual voting records by injecting randomness into the published outcome, but this noise can lead to erroneous results when an election is close.…
Publishing Below-Threshold Triangle Counts under Local Weight Differential Privacy
Kevin Pfisterer, Quentin Hillebrand, Vorapong Suppakitpaisarn
We propose an algorithm for counting below-threshold triangles in weighted graphs under local weight differential privacy. While prior work has largely focused on unweighted graphs…
Approximation Algorithms for the -Matching and List-Restricted Variants of MaxQAP
Jiratchaphat Nanta, Vorapong Suppakitpaisarn, Piyashat Sripratak
We study approximation algorithms for two natural generalizations of the Maximum Quadratic Assignment Problem (MaxQAP). In the Maximum List-Restricted Quadratic Assignment Problem,…
Improved Differentially Private Algorithms for Rank Aggregation
Quentin Hillebrand, Pasin Manurangsi, Vorapong Suppakitpaisarn +1
Rank aggregation is a task of combining the rankings of items from multiple users into a single ranking that best represents the users' rankings. Alabi et al. (AAAI'22) presents di…
Optimal Representation for Right-to-Left Parallel Scalar Point Multiplication
Kittiphon Phalakarn, Kittiphop Phalakarn, Vorapong Suppakitpaisarn
This paper introduces an optimal representation for a right-to-left parallel elliptic curve scalar point multiplication. The right-to-left approach is easier to parallelize than th…
Communication-Efficient Publication of Sparse Vectors under Differential Privacy
Quentin Hillebrand, Vorapong Suppakitpaisarn, Tetsuo Shibuya
In this work, we propose a differentially private algorithm for publishing matrices aggregated from sparse vectors. These matrices include social network adjacency matrices, user-i…