arXiv subjects

Seung-Hyun Nam

Publications and source records attributed to Seung-Hyun Nam.

10 recordsLinked to original sources

arXiv2026

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

We consider communication over discrete memoryless interference channels or multiple access channels without feedback, where transmitters exploit classical, quantum, or no-signaling cooperation. Previous works have shown that, for channels associated with pseudo-telepathy games, quantum or no-signaling cooperation can increase the sum capacity. However, a full characterization of channels admitting such an improvement remains open. Motivated by common features of previously studied examples, we propose a broader class of game-induced channels. In these channels, each input is a question-answer pair from a nonlocal game. When the inputs satisfy the game's winning condition, the channel has lower conditional output uncertainty, and the channel decomposes into parallel weakly symmetric subchannels with a unique capacity-achieving input distribution. We show that, for this class, quantum or no-signaling cooperation strictly increases the sum capacity whenever the associated game is a quantum or no-signaling pseudo-telepathy game, respectively. The proposed class recovers several previously studied channels and includes new examples.

quant-ph↗

arXiv2026

Quantum Advantage in Locally Differentially Private Hypothesis Testing

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, namely the Chernoff information and the relative entropy, while privacy is measured in terms of classical or quantum local differential privacy. In this scenario, we show a quantum advantage with respect to the optimal privacy-utility trade-off (PUT) in certain cases. Specifically, we focus on distributions referred to as smoothed point mass distributions, along with the uniform distribution, as hypotheses. We then derive upper bounds on the optimal PUTs achievable by classical privacy mechanisms, which are tight in specific instances. To show the quantum advantage, we propose a particular quantum privacy mechanism that achieves better PUTs than these upper bounds in both symmetric and asymmetric testing, specifically under stringent privacy constraints and small discrete data alphabet sizes ranging from 3 to 9. The proposed mechanism consists of a classical-quantum channel that prepares symmetric informationally complete (SIC) states, followed by a depolarizing channel.

quant-ph↗

arXiv2026

Practical and Efficient Verification of Entanglement with Incomplete Measurement Settings

In this work, we present a practical and efficient framework for verifying entangled states when only a tomographically incomplete measurement setting is available-specifically, when access to observables is severely limited. We show how the experimental estimation of a small number of observables can be directly exploited to construct a large family of entanglement witnesses, enabling the efficient identification of entangled states. Moreover, we introduce an optimization approach, formulated as a semidefinite program, that systematically searches for those witnesses best suited to reveal entanglement under the given measurement constraints. We demonstrate the practicality of the approach in a proof-of-principle experiment with photon-polarization qubits, where entanglement is certified using only a fraction of the full measurement data. These results reveal the maximal usefulness of incomplete measurement settings for entanglement verification in realistic scenarios.

quant-ph↗

arXiv2026

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

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) and the corresponding optimal LDP channels remains largely fragmented, relying on problem-specific, case-by-case analyses. In this work, we develop a unified theoretical framework that systematically characterizes the optimal PUT and optimal LDP channels for general privacy-preserving statistical decision-making problems. We first identify key functional properties of Bayesian and minimax risks as functions of the LDP channel, including the data processing inequality (DPI), direct-sum quasi-convexity (or additivity), concavity, and symmetry invariance. Leveraging these properties, we reduce the optimization domain required to compute the optimal PUT. Additionally, building on convex geometric insights, we establish a one-to-one correspondence between maximal LDP channels under the Blackwell order and a finite-dimensional polytope, yielding an exact geometric characterization. This result renders the optimal PUT computationally tractable via vertex enumeration or linear programming. Furthermore, when the underlying problem exhibits symmetries characterized by a transitive group action, we derive an exact analytic expression for the optimal PUT, leading to closed-form solutions without numerical optimization. Our framework applies broadly beyond risk minimization, encompassing the maximization of information-theoretic measures such as mutual information, $f$-divergences, and Fisher information over LDP channels. We demonstrate the efficacy of our theoretical framework by recovering or strengthening several known results, and deriving exact analytic expressions for the optimal PUTs in specific tasks that were previously unaddressed.

cs.CR↗

arXiv2026

Nonlocal and quantum advantages in network coding for multiple access channels

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 coding by using preshared resources, such as shared randomness, quantum states and measurements, or nonlocal correlations. We present the capacity region when senders employ cooperative encoding with quantum and nonlocal resources, extending beyond shared randomness, and derive a sum rate that serves as a lower bound to the sum capacity; the lower bound is computable by exploiting specific strategies. We also compute the sum capacities for two instances. One is when senders apply local resources for cooperative encoding. The other is when senders exploit nonclassical resources for encoding against channels constructed by referring to nonlocal games; in this way, correlated noise other than independent errors occurs on code words. Comparing the exact sum capacities and lower bounds, we show that nonlocal and quantum resources for cooperative encoding enable higher sum capacities over local ones. The Clauser-Horne-Shimony-Holt and magic square games are considered for constructing multiple-access channels, and we demonstrate the usefulness of nonlocal and quantum resources to achieve higher-sum capacities.

quant-ph↗

arXiv2025

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

We study the problem of discrete distribution estimation under utility-optimized local differential privacy (ULDP), which enforces local differential privacy (LDP) on sensitive data while allowing more accurate inference on non-sensitive data. In this setting, we completely characterize the fundamental privacy-utility trade-off. The converse proof builds on several key ideas, including a generalized uniform asymptotic Cramér-Rao lower bound, a reduction showing that it suffices to consider a newly defined class of extremal ULDP mechanisms, and a novel distribution decomposition technique tailored to ULDP constraints. For the achievability, we propose a class of utility-optimized block design (uBD) schemes, obtained as nontrivial modifications of the block design mechanism known to be optimal under standard LDP constraints, while incorporating the distribution decomposition idea used in the converse proof and a score-based linear estimator. These results provide a tight characterization of the estimation accuracy achievable under ULDP and reveal new insights into the structure of optimal mechanisms for privacy-preserving statistical inference.

cs.CR↗

arXiv2023

Optimal Private Discrete Distribution Estimation with One-bit Communication

We consider a private discrete distribution estimation problem with one-bit communication constraint. The privacy constraints are imposed with respect to the local differential privacy and the maximal leakage. The estimation error is quantified by the worst-case mean squared error. We completely characterize the first-order asymptotics of this privacy-utility trade-off under the one-bit communication constraint for both types of privacy constraints by using ideas from local asymptotic normality and the resolution of a block design mechanism. These results demonstrate the optimal dependence of the privacy-utility trade-off under the one-bit communication constraint in terms of the parameters of the privacy constraint and the size of the alphabet of the discrete distribution.

cs.IT↗

arXiv2023

Exactly Optimal and Communication-Efficient Private Estimation via Block Designs

In this paper, we propose a new class of local differential privacy (LDP) schemes based on combinatorial block designs for discrete distribution estimation. This class not only recovers many known LDP schemes in a unified framework of combinatorial block design, but also suggests a novel way of finding new schemes achieving the exactly optimal (or near-optimal) privacy-utility trade-off with lower communication costs. Indeed, we find many new LDP schemes that achieve the exactly optimal privacy-utility trade-off, with the minimum communication cost among all the unbiased or consistent schemes, for a certain set of input data size and LDP constraint. Furthermore, to partially solve the sparse existence issue of block design schemes, we consider a broader class of LDP schemes based on regular and pairwise-balanced designs, called RPBD schemes, which relax one of the symmetry requirements on block designs. By considering this broader class of RPBD schemes, we can find LDP schemes achieving near-optimal privacy-utility trade-off with reasonably low communication costs for a much larger set of input data size and LDP constraint.

cs.CR↗

arXiv2023

Achieving the Exactly Optimal Privacy-Utility Trade-Off with Low Communication Cost via Shared Randomness

We consider a discrete distribution estimation problem under a local differential privacy (LDP) constraint in the presence of shared randomness. By exploiting the shared randomness, we suggest a new method for constructing LDP schemes which achieve the exactly optimal privacy-utility trade-off (PUT) with the communication cost of less than or equal to the input data size for any privacy regime. The main idea is to decompose a block design scheme by Park et al. (2023), based on the combinatorial concept called resolution. The LDP scheme decomposed from a block design scheme is called a resolution of the block design scheme, and it achieves the same PUT as the original block design scheme while requiring a less communication cost. We provide two resolutions of an exactly PUT-optimal block design scheme, called the Baranyai's resolution and the cyclic shift resolution, both requiring the communication cost of less than or equal to the input data size. In particular, we show that the Baranyai's resolution achieves the minimum communication cost among all the PUT-optimal resolutions of block design schemes. One drawback of the Baranyai's resolution is that it can be obtained through a recursive algorithm in general. In contrast, the cyclic shift resolution has an explicit structure, but its communication cost can be larger than that of Baranyai's resolution. To complement this, we also suggest resolutions of other block design schemes achieving the optimal PUT for some privacy budgets, which require the minimum communication cost as the Baranyai's resolution and have explicit structures as the cyclic shift resolution.

cs.IT↗

arXiv2020

Secrecy Capacity of a Gaussian Wiretap Channel With ADCs is Always Positive

We consider a complex Gaussian wiretap channel with finite-resolution analog-to-digital converters (ADCs) at both the legitimate receiver and the eavesdropper. For this channel, we show that a positive secrecy rate is always achievable as long as the channel gains at the legitimate receiver and at the eavesdropper are different, regardless of the quantization levels of the ADCs. For the achievability, we first consider the case of one-bit ADCs at the legitimate receiver and apply a binary input distribution where the two input points have the same phase when the channel gain at the legitimate receiver is less than that at the eavesdropper, and otherwise the opposite phase. Then the result is generalized for the case of arbitrary finite-resolution ADCs at the legitimate receiver by translating the input distribution appropriately. For the special case of the real Gaussian wiretap channel with one-bit ADCs at both the legitimate receiver and the eavesdropper, we show that our choice of input distribution satisfies a necessary condition of optimal distributions for Wyner codes.

cs.IT↗