activity
20242026
most citedNonlocal and quantum advantages in network coding for multiple access channels

1 citations · 1 across the 3 of their papers we have counts for

collaborators

6 papers

quant-ph2026

Quantum Advantage in Locally Differentially Private Hypothesis Testing

Seung-Hyun Nam, Hyun-Young Park, Si-Hyeon Lee +1

We consider a private hypothesis testing scenario, including both symmetric and asymmetric testing, based on classical data samples. The utility is measured by the error exponents,…

cs.CR2026

Optimal Privacy-Utility Trade-Offs in LDP: Functional and Geometric Perspectives

Seung-Hyun Nam, Hyun-Young Park, Si-Hyeon Lee

Local differential privacy (LDP) has emerged as a gold-standard framework for privacy-preserving data analysis. However, characterizing the optimal privacy-utility trade-off (PUT)…

quant-ph20261 cited

Nonlocal and quantum advantages in network coding for multiple access channels

Jiyoung Yun, Seung-Hyun Nam, Hyun-Young Park +3

In this work, we consider two-sender, one-receiver communication over a discrete memoryless multiple-access channel without feedback, where two senders may cooperate on channel cod…

quant-ph2026

Enhancing Sum Capacity via Quantum and No-Signaling Cooperation Between Transmitters

Seung-Hyun Nam, Hyun-Young Park, Jiyoung Yun +3

We consider communication over discrete memoryless interference channels or multiple access channels without feedback, where transmitters exploit classical, quantum, or no-signalin…

cs.CR2025

Fundamental Limit of Discrete Distribution Estimation under Utility-Optimized Local Differential Privacy

Sun-Moon Yoon, Hyun-Young Park, Seung-Hyun Nam +1

We study the problem of discrete distribution estimation under utility-optimized local differential privacy (ULDP), which enforces local differential privacy (LDP) on sensitive dat…

cs.LG2024

Exactly Minimax-Optimal Locally Differentially Private Sampling

Hyun-Young Park, Shahab Asoodeh, Si-Hyeon Lee

The sampling problem under local differential privacy has recently been studied with potential applications to generative models, but a fundamental analysis of its privacy-utility…