3 citations · 5 across the 20 of their papers we have counts for
7 papers · 1 filter
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…
Facility Location Problem under Local Differential Privacy without Super-set Assumption
Kevin Pfisterer, Quentin Hillebrand, Vorapong Suppakitpaisarn
In this paper, we introduce an adaptation of the facility location problem and analyze it within the framework of local differential privacy (LDP). Under this model, we ensure the…
Cycle Counting under Local Differential Privacy for Degeneracy-bounded Graphs
Quentin Hillebrand, Vorapong Suppakitpaisarn, Tetsuo Shibuya
We propose an algorithm for counting the number of cycles under local differential privacy for degeneracy-bounded input graphs. Numerous studies have focused on counting the number…
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…
Local Differential Privacy-Preserving Spectral Clustering for General Graphs
Sayan Mukherjee, Vorapong Suppakitpaisarn
Spectral clustering is a widely used algorithm to find clusters in networks. Several researchers have studied the stability of spectral clustering under local differential privacy…
Efficient Additions and Montgomery Reductions of Large Integers for SIMD
Pengchang Ren, Reiji Suda, Vorapong Suppakitpaisarn
This paper presents efficient algorithms, designed to leverage SIMD for performing Montgomery reductions and additions on integers larger than 512 bits. The existing algorithms enc…
Performances of Symmetric Loss for Private Data from Exponential Mechanism
Jing Bi, Vorapong Suppakitpaisarn
This study explores the robustness of learning by symmetric loss on private data. Specifically, we leverage exponential mechanism (EM) on private labels. First, we theoretically re…