BZPEERReading, trust and collaboration for research
Menu

Discover

GraphFollow connections around a paper, topic or saved view.

Workspaces

HomePick up where your research flow left off.InboxHandle requests, replies and notifications from one place.CollectionsCurate reading lists, evidence sets and shareable dossiers.ResearchSee your private provenance graph, trail and imports.CollaborationMove from discovery into focused discussion rooms.PublishDraft, preview and publish full-text research articles.

Network

ExpertsFind specialists worth following or asking for help.CommunitiesJoin research circles organized around shared work.PartnersSee external organizations active around the same topics.

Opportunities

ListingsBrowse roles, calls and collaborations with clearer fit signals.

Account

Log inReturn to your reading and collaboration workspace.Create accountStart saving work, following people and joining teams.
Log inCreate account

Researcher profile

Seng Pei Liew

Seng Pei Liew contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Cryptography and SecurityMachine Learningastro-ph.COhep-phDatabasesComputation and Languagecond-mat.mes-hallcond-mat.stat-mechquant-ph

Trust snapshot

Quick read

Trust 21 - EmergingVerification L1Unclaimed author
12works
0followers
9topics
4close collaborators

Actions

Decide how to stay connected

Follow researcher0

Identity and collaboration

How to connect with this researcher

Claiming links this public author record to a researcher profile and unlocks direct collaboration workflows.

Log in to claim

Direct collaboration

Open a focused conversation when the fit is right

Claim this author entity first to unlock direct invitations.

Research graph

See the researcher in context

Open full explorer

Inspect adjacent work, topics, institutions and collaborators without jumping out to a separate graph page.

Building this graph slice

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Published work

12 published item(s)

preprint2026arXiv

Differentially Private Sampling from Distributions via Wasserstein Projection

In this paper, we study the problem of sampling from a distribution under the constraint of differential privacy (DP). Prior works measure the utility of DP sampling with density ratio-based measures such as KL divergence. However, such formulations suffer from two key limitations: 1) they fail to capture the geometric structure of the support, and 2) they are not applicable when the supports of the distributions differ. To deal with these issues, we develop a novel framework for DP sampling with Wasserstein distance as the utility measure. In this formulation, we propose Wasserstein Projection Mechanism (WPM), a minimax optimal mechanism based on Wasserstein projection. Furthermore, we develop efficient algorithms for computing the proposed mechanisms approximately and provide convergence guarantees.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Shuffling-Aware Optimization for Private Vector Mean Estimation

We study $d$-dimensional unbiased mean estimation in the single-message shuffle model, where each user sends a single privatized message and the analyzer only observes the shuffled multiset of reports. While minimax-optimal mechanisms are well understood in the local differential privacy setting, the corresponding notion of optimality after shuffling has remained largely unexplored. To address this gap, we introduce the recently proposed shuffle index and use it to formulate the post-shuffling mechanism design problem as an explicit optimization problem. We then establish a minimax lower bound on the achievable mean squared error in terms of the shuffle index, which implies that mechanisms that are optimal under LDP can become suboptimal once shuffling is applied. Finally, we construct an asymptotically minimax optimal mechanism in the high privacy regime, which as a consequence achieves a privacy-utility trade-off nearly identical to that of the central Gaussian mechanism.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Towards Principled Design of Mixture-of-Experts Language Models under Memory and Inference Constraints

Modern Mixture-of-Experts (MoE) language models are designed based on total parameters (memory footprint) and active parameters (inference cost). However, we find these two factors alone are insufficient to describe an optimal architecture. Through a systematic study, we demonstrate that MoE performance is primarily determined by total parameters ($N_{total}$) and expert sparsity ($s:=n_{exp}/n_{topk}$). Moreover, $n_{exp}$ and $n_{topk}$ do not "cancel out" within the sparsity ratio; instead, a larger total number of experts slightly penalizes performance by forcing a reduction in core model dimensions (depth and width) to meet memory constraints. This motivates a simple principle for MoE design which maximizes $N_{total}$ while minimizing $s$ (maximizing $n_{topk}$) and $n_{exp}$ under the given constraints. Our findings provide a robust framework for resolving architectural ambiguity and guiding MoE design.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

HDPView: Differentially Private Materialized View for Exploring High Dimensional Relational Data

How can we explore the unknown properties of high-dimensional sensitive relational data while preserving privacy? We study how to construct an explorable privacy-preserving materialized view under differential privacy. No existing state-of-the-art methods simultaneously satisfy the following essential properties in data exploration: workload independence, analytical reliability (i.e., providing error bound for each search query), applicability to high-dimensional data, and space efficiency. To solve the above issues, we propose HDPView, which creates a differentially private materialized view by well-designed recursive bisected partitioning on an original data cube, i.e., count tensor. Our method searches for block partitioning to minimize the error for the counting query, in addition to randomizing the convergence, by choosing the effective cutting points in a differentially private way, resulting in a less noisy and compact view. Furthermore, we ensure formal privacy guarantee and analytical reliability by providing the error bound for arbitrary counting queries on the materialized views. HDPView has the following desirable properties: (a) Workload independence, (b) Analytical reliability, (c) Noise resistance on high-dimensional data, (d) Space efficiency. To demonstrate the above properties and the suitability for data exploration, we conduct extensive experiments with eight types of range counting queries on eight real datasets. HDPView outperforms the state-of-the-art methods in these evaluations.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Measuring Lower Bounds of Local Differential Privacy via Adversary Instantiations in Federated Learning

Local differential privacy (LDP) gives a strong privacy guarantee to be used in a distributed setting like federated learning (FL). LDP mechanisms in FL protect a client's gradient by randomizing it on the client; however, how can we interpret the privacy level given by the randomization? Moreover, what types of attacks can we mitigate in practice? To answer these questions, we introduce an empirical privacy test by measuring the lower bounds of LDP. The privacy test estimates how an adversary predicts if a reported randomized gradient was crafted from a raw gradient $g_1$ or $g_2$. We then instantiate six adversaries in FL under LDP to measure empirical LDP at various attack surfaces, including a worst-case attack that reaches the theoretical upper bound of LDP. The empirical privacy test with the adversary instantiations enables us to interpret LDP more intuitively and discuss relaxation of the privacy parameter until a particular instantiated attack surfaces. We also demonstrate numerical observations of the measured privacy in these adversarial settings, and the worst-case attack is not realistic in FL. In the end, we also discuss the possible relaxation of privacy levels in FL under LDP.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Network Shuffling: Privacy Amplification via Random Walks

Recently, it is shown that shuffling can amplify the central differential privacy guarantees of data randomized with local differential privacy. Within this setup, a centralized, trusted shuffler is responsible for shuffling by keeping the identities of data anonymous, which subsequently leads to stronger privacy guarantees for systems. However, introducing a centralized entity to the originally local privacy model loses some appeals of not having any centralized entity as in local differential privacy. Moreover, implementing a shuffler in a reliable way is not trivial due to known security issues and/or requirements of advanced hardware or secure computation technology. Motivated by these practical considerations, we rethink the shuffle model to relax the assumption of requiring a centralized, trusted shuffler. We introduce network shuffling, a decentralized mechanism where users exchange data in a random-walk fashion on a network/graph, as an alternative of achieving privacy amplification via anonymity. We analyze the threat model under such a setting, and propose distributed protocols of network shuffling that is straightforward to implement in practice. Furthermore, we show that the privacy amplification rate is similar to other privacy amplification techniques such as uniform shuffling. To our best knowledge, among the recently studied intermediate trust models that leverage privacy amplification techniques, our work is the first that is not relying on any centralized entity to achieve privacy amplification.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

PEARL: Data Synthesis via Private Embeddings and Adversarial Reconstruction Learning

We propose a new framework of synthesizing data using deep generative models in a differentially private manner. Within our framework, sensitive data are sanitized with rigorous privacy guarantees in a one-shot fashion, such that training deep generative models is possible without re-using the original data. Hence, no extra privacy costs or model constraints are incurred, in contrast to popular approaches such as Differentially Private Stochastic Gradient Descent (DP-SGD), which, among other issues, causes degradation in privacy guarantees as the training iteration increases. We demonstrate a realization of our framework by making use of the characteristic function and an adversarial re-weighting objective, which are of independent interest as well. Our proposal has theoretical guarantees of performance, and empirical evaluations on multiple datasets show that our approach outperforms other methods at reasonable levels of privacy.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Scaling Private Deep Learning with Low-Rank and Sparse Gradients

Applying Differentially Private Stochastic Gradient Descent (DPSGD) to training modern, large-scale neural networks such as transformer-based models is a challenging task, as the magnitude of noise added to the gradients at each iteration scales with model dimension, hindering the learning capability significantly. We propose a unified framework, $\textsf{LSG}$, that fully exploits the low-rank and sparse structure of neural networks to reduce the dimension of gradient updates, and hence alleviate the negative impacts of DPSGD. The gradient updates are first approximated with a pair of low-rank matrices. Then, a novel strategy is utilized to sparsify the gradients, resulting in low-dimensional, less noisy updates that are yet capable of retaining the performance of neural networks. Empirical evaluation on natural language processing and computer vision tasks shows that our method outperforms other state-of-the-art baselines.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Light Axinos from Freeze-in: production processes, phase space distributions, and Ly-$α$ forest constraints

We consider freeze-in production of 7 keV axino dark matter (DM) in the supersymmetric Dine-Fischler-Srednicki-Zhitnitsky (DFSZ) model in light of the 3.5 keV line excess. The warmness of such 7 keV DM produced from the thermal bath, in general, appears in tension with Ly-$α$ forest data, although a direct comparison is not straightforward. This is because the Ly-$α$ forest constraints are usually reported on the mass of the conventional warm dark matter (WDM), where large entropy production is implicitly assumed to occur in the thermal bath after WDM particles decouple. The phase space distribution of freeze-in axino DM varies depending on production processes and axino DM may alleviate the tension with the tight Ly-$α$ forest constraints. By solving the Boltzmann equation, we first obtain the resultant phase space distribution of axinos produced by 2-body decay, 3-body decay, and 2-to-2 scattering, respectively. The reduced collision term and resultant phase space distribution are useful for studying other freeze-in scenarios as well. We then calculate the resultant linear matter power spectra for such axino DM and directly compare them with the linear matter power spectra for the conventional WDM. In order to demonstrate realistic axino DM production, we consider benchmark points with the Higgsino next-to-lightest supersymmetric particle (NLSP) and wino NLSP. In the case of the Higgsino NLSP, the phase space distribution of axinos is colder than that in the conventional WDM case, so the most stringent Ly-$α$ forest constraint can be evaded with mild entropy production from saxion decay inherent in the supersymmetric DFSZ axion model.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Axino dark matter with R-parity violation and 130 GeV gamma-ray line

We show that decaying axino dark matter with R-parity violation can explain the observed excess of the 130GeV gamma-ray line from the Galactic center in the Fermi data. The branching fraction of the axino decay into monochromatic photons can be O(1), and constraints from continuum gamma-rays and the anti-proton flux are ameliorated. The Peccei-Quinn scale of $O(10^{13}-10^{14})$\,GeV and the R-parity violation parameter of $O(10^{-12}-10^{-11})$ are cosmologically favored.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Gamma-ray line from radiative decay of gravitino dark matter

We study radiative decay of gravitino dark matter with trilinear R-parity violations. We show that the branching ratio of the decay of gravitino into monochromatic photon can be large enough to explain the observed gamma-ray line from the Galactic centre in the Fermi-LAT data without producing too much continuum gamma-ray and anti-proton flux. This scenario is realized when the mass of sfermions and the trilinear R-parity violating coupling are $O(1-10)$ TeV and $O(10^{-7}-10^{-6})$ respectively.

0 citations0 reviewsNo code linkedOpen access