Donlapark Ponnoprat

Publications

Preprint

1. Investigating Privacy Leakage in Dimensionality Reduction Methods via Reconstruction Attack [abstract] [arxiv]
   C. Lumbut, D. Ponnoprat
   Manuscript, 2024
   This study investigates privacy leakage in dimensionality reduction methods through a novel machine learning-based reconstruction attack. Employing an \emph{informed adversary} threat model, we develop a neural network capable of reconstructing high-dimensional data from low-dimensional embeddings.

   We evaluate six popular dimensionality reduction techniques: principal component analysis (PCA), sparse random projection (SRP), multidimensional scaling (MDS), Isomap, t-distributed stochastic neighbor embedding ($t$-SNE), and uniform manifold approximation and projection (UMAP). Using both MNIST and NIH Chest X-ray datasets, we perform a qualitative analysis to identify key factors affecting reconstruction quality. Furthermore, we assess the effectiveness of an additive noise mechanism in mitigating these reconstruction attacks. Our experimental results on both datasets reveal that the attack is effective against deterministic methods (PCA and Isomap). but ineffective against methods that employ random initialization (SRP, MDS, $t$-SNE and UMAP). The experimental results also show that, for PCA and Isomap, our reconstruction network produces higher quality outputs compared to a previously proposed network.

   We also study the effect of additive noise mechanism to prevent the reconstruction attack. Our experiment shows that, when adding the images with large noises before performing PCA or Isomap, the attack produced severely distorted reconstructions. In contrast, for the other four methods, the reconstructions still show some recognizable features, though they bear little resemblance to the original images.

Conference papers

1. Counting Graphlets of Size k under Local Differential Privacy [abstract] [paper]
   V. Suppakitpaisarn, D. Ponnoprat, N. Hirankarn, Q. Hillebrand
   AISTATS, 2025
   The problem of counting subgraphs or graphlets under local differential privacy is an important challenge that has attracted significant attention from researchers. However, much of the existing work focuses on small graphlets like triangles or $k$-stars. In this paper, we propose a non-interactive, locally differentially private algorithm capable of counting graphlets of any size $k$. When $n$ is the number of nodes in the input graph, we show that the expected $\ell^2$ error of our algorithm is $O(n^{k−1})$. Additionally, we prove that there exists a class of input graphs and graphlets of size $k$ for which any non-interactive counting algorithm incurs an expected $\ell^2$ error of $\Omega(n^{k−1})$, demonstrating the optimality of our result. Furthermore, we establish that for certain input graphs and graphlets, any locally differentially private algorithm must have an expected $\ell^2$ error of $\Omega(n^{k-1.5})$. Our experimental results show that our algorithm is more accurate than the classical randomized response method.


2. Detecting Anomalous LAN Activities under Differential Privacy [abstract] [arxiv]
   N. Rattanavipanon, D. Ponnoprat, H. Ochiai, K. Tantayakul, T. Angchuan4, S. Kamolphiwong
   NDSS, 2022
   Anomaly detection has emerged as a popular technique for detecting malicious activities in local area networks (LANs). Various aspects of LAN anomaly detection have been widely studied. Nonetheless, the privacy concern about individual users or their relationship in LAN has not been thoroughly explored in the prior work. In some realistic cases, the anomaly detection analysis needs to be carried out by an external party, located outside the LAN. Thus, it is important for the LAN admin to release LAN data to this party in a private way in order to protect privacy of LAN users; at the same time, the released data must also preserve the utility of being able to detect anomalies. This paper investigates the possibility of privately releasing ARP data that can later be used to identify anomalies in LAN. We present four approaches, namely naïve, histogram-based, naïve-$\delta$ and histogram-based-$\delta$, and show that they satisfy different levels of differential privacy—a rigorous and provable notion for quantifying privacy loss in a system. Our real-world experimental results confirm practical feasibility of our approaches. With a proper privacy budget, all of our approaches preserve more than 75% utility of detecting anomalies in the released data.

Journal papers

1. Uniform Confidence Bands for Optimal Transport Map on One-Dimensional Data [abstract] [paper]
   D. Ponnoprat, R. Okano, M. Imaizumi
   Electronic Journal of Statistics, 2024
   We develop a statistical inference method for an optimal transport map between distributions on real numbers with uniform confidence bands. The concept of optimal transport (OT) is used to measure distances between distributions, and OT maps are used to construct the distance. OT has been applied in many fields in recent years, and its statistical properties have attracted much interest. In particular, since the OT map is a function, a uniform norm-based statistical inference is significant for visualization and interpretation. In this study, we derive a limit distribution of a uniform norm of an estimation error for the OT map, and then develop a uniform confidence band based on it. In addition to our limit theorem, we develop a bootstrap method with kernel smoothing, then also derive its validation and guarantee on an asymptotic coverage probability of the confidence band. Our proof is based on the functional delta method and the representation of OT maps on the reals.


2. Universal Consistency of Wasserstein k-NN Classifier: a Negative and Some Positive Results [abstract] [paper]
   D. Ponnoprat
   Information and Inference, 2023
   We study the $k$-nearest neighbour classifier ($k$⁠-NN) of probability measures under the Wasserstein distance. We show that the $k$-NN classifier is not universally consistent on the space of measures supported in $(0,1)$⁠. As any Euclidean ball contains a copy of $(0,1)$⁠, one should not expect to obtain universal consistency without some restriction on the base metric space, or the Wasserstein space itself. To this end, via the notion of $\sigma$-finite metric dimension, we show that the $k$-NN classifier is universally consistent on spaces of discrete measures (and more generally, $\sigma$-finite uniformly discrete measures) with rational mass. In addition, by studying the geodesic structures of the Wasserstein spaces for $p=1$ and $p=2$⁠, we show that the $k$-NN classifier is universally consistent on spaces of measures supported on a finite set, the space of Gaussian measures and spaces of measures with finite wavelet series densities.

Dirichlet Mechanism for Differentially Private KL Divergence Minimization (code)
Transactions on Machine Learning Research, 2023.

Detecting Anomalous LAN Activities under Differential Privacy
Security and Communication Networks, 2022.

Short-term daily precipitation forecasting with seasonally-integrated autoencoder (code)
Applied Soft Computing, 2021.

Classification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma based on multi-phase CT scans
Medical Biol. Eng. Comput., 2020.

Small data well-posedness for derivative nonlinear Schrödinger equations
Journal of Differential Equations, 2018.