8 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…
Privacy by Postprocessing the Discrete Laplace Mechanism
Quentin Hillebrand, Jacob Imola, Rasmus Pagh +1
We show that an "old dog", the classical discrete Laplace (aka.~geometric) mechanism, can "perform new tricks": 1. It can be post-processed to yield a simple, unbiased estimator of…
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…
Communication Cost Reduction for Subgraph Counting under Local Differential Privacy via Hash Functions
Quentin Hillebrand, Vorapong Suppakitpaisarn, Tetsuo Shibuya
We suggest the use of hash functions to cut down the communication costs when counting subgraphs under edge local differential privacy. While various algorithms exist for computing…
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…