arXiv subjects

Thang Hoang

Publications and source records attributed to Thang Hoang.

16 recordsLinked to original sources

arXiv2025

Leveraging Plasmonic Nanocavity Arrays Forming Metasurfaces to Boost Second Harmonic Generation due to Surface Effects

Plasmonic metasurfaces have emerged as a promising platform for enhancing a range of nonlinear optical processes, offering compact geometry and flexibility in light manipulation. Second order nonlinear processes, like second harmonic generation (SHG), typically require non-centrosymmetric crystals to be realized. Here, we experimentally demonstrate enhanced SHG response by using a gold nanocavity array forming a plasmonic metasurface absorber where titanium dioxide (TiO2), a centrosymmetric dielectric material, with subwavelength thickness is deposited in the realized nanogaps. While such dielectric material has an extremely low second order nonlinear susceptibility, we observe 105-fold boosting in the nonlinear SHG process mainly due to the surface nonlinear susceptibility of the gold metal aided by the significant electric field enhancement that occurs in the nanogaps due to the formed nanocavity resonance. The experimental results obtained are theoretically explained with extensive and rigorous nonlinear simulations that consider all the bulk and surface linear and nonlinear material properties. The presented robust harmonic generation from an ultrathin plasmonic metasurface can be used in nonlinear and quantum integrated photonic applications.

physics.optics↗

arXiv2025

Local Differential Privacy for Federated Learning with Fixed Memory Usage and Per-Client Privacy

Federated learning (FL) enables organizations to collaboratively train models without sharing their datasets. Despite this advantage, recent studies show that both client updates and the global model can leak private information, limiting adoption in sensitive domains such as healthcare. Local differential privacy (LDP) offers strong protection by letting each participant privatize updates before transmission. However, existing LDP methods were designed for centralized training and introduce challenges in FL, including high resource demands that can cause client dropouts and the lack of reliable privacy guarantees under asynchronous participation. These issues undermine model generalizability, fairness, and compliance with regulations such as HIPAA and GDPR. To address them, we propose L-RDP, a DP method designed for LDP that ensures constant, lower memory usage to reduce dropouts and provides rigorous per-client privacy guarantees by accounting for intermittent participation.

cs.CR↗

arXiv2025

Secret-Key Agreement Through Hidden Markov Modeling of Wavelet Scattering Embeddings

Secret-key generation and agreement based on wireless channel reciprocity offers a promising avenue for securing IoT networks. However, existing approaches predominantly rely on the similarity of instantaneous channel measurement samples between communicating devices. This narrow view of reciprocity is often impractical, as it is highly susceptible to noise, asynchronous sampling, channel fading, and other system-level imperfections -- all of which significantly impair key generation performance. Furthermore, the quantization step common in traditional schemes introduces irreversible errors, further limiting efficiency. In this work, we propose a novel approach for secret-key generation by using wavelet scattering networks to extract robust and reciprocal CSI features. Dimensionality reduction is applied to uncover hidden cluster structures, which are then used to build hidden Markov models for efficient key agreement. Our approach eliminates the need for quantization and effectively captures channel randomness. It achieves a 5x improvement in key generation rate compared to traditional benchmarks, providing a secure and efficient solution for key generation in resource-constrained IoT environments.

cs.CR↗

arXiv2025

Plasmon-Assisted Random Lasing of Perovskite Materials

Random lasing occurs as the result of coherent optical feedback from random scattering centers. Plasmonic nanostructures, such as silver or gold nanoparticles, efficiently scatter light due to the formation of hot spots and optical confinement at the nanoscale. In this work, using silver nanocubes as highly efficient light scattering centers, a broad band plasmon assisted lasing action of halide perovskite materials is demonstrated. By embedding silver nanocubes in CH3NH3PbBr3 and CH3NH3PbI3 solutions, narrow bandwidth lasing modes with a full width at half-maximum of approximately 1 nm are supported. It is observed that the lasing thresholds of perovskites are different for glass and gold substrates, and for different nanocube concentrations. Results of time-resolved measurements indicate a significant shortening in the decay time of the emission at above the lasing threshold, implying a stimulated emission process. The results of this work thus provide a pathway to generate coherent light sources from widely studied perovskite materials.

physics.optics↗

arXiv2024

Privacy-Preserving Verifiable Neural Network Inference Service

Machine learning has revolutionized data analysis and pattern recognition, but its resource-intensive training has limited accessibility. Machine Learning as a Service (MLaaS) simplifies this by enabling users to delegate their data samples to an MLaaS provider and obtain the inference result using a pre-trained model. Despite its convenience, leveraging MLaaS poses significant privacy and reliability concerns to the client. Specifically, sensitive information from the client inquiry data can be leaked to an adversarial MLaaS provider. Meanwhile, the lack of a verifiability guarantee can potentially result in biased inference results or even unfair payment issues. While existing trustworthy machine learning techniques, such as those relying on verifiable computation or secure computation, offer solutions to privacy and reliability concerns, they fall short of simultaneously protecting the privacy of client data and providing provable inference verifiability. In this paper, we propose vPIN, a privacy-preserving and verifiable CNN inference scheme that preserves privacy for client data samples while ensuring verifiability for the inference. vPIN makes use of partial homomorphic encryption and commit-and-prove succinct non-interactive argument of knowledge techniques to achieve desirable security properties. In vPIN, we develop various optimization techniques to minimize the proving circuit for homomorphic inference evaluation thereby, improving the efficiency and performance of our technique. We fully implemented and evaluated our vPIN scheme on standard datasets (e.g., MNIST, CIFAR-10). Our experimental results show that vPIN achieves high efficiency in terms of proving time, verification time, and proof size, while providing client data privacy guarantees and provable verifiability.

cs.CR↗

arXiv2023

Efficient Secure Aggregation for Privacy-Preserving Federated Machine Learning

Secure aggregation protocols ensure the privacy of users' data in federated learning by preventing the disclosure of local gradients. Many existing protocols impose significant communication and computational burdens on participants and may not efficiently handle the large update vectors typical of machine learning models. Correspondingly, we present e-SeaFL, an efficient verifiable secure aggregation protocol taking only one communication round during the aggregation phase. e-SeaFL allows the aggregation server to generate proof of honest aggregation to participants via authenticated homomorphic vector commitments. Our core idea is the use of assisting nodes to help the aggregation server, under similar trust assumptions existing works place upon the participating users. Our experiments show that the user enjoys an order of magnitude efficiency improvement over the state-of-the-art (IEEE S\&P 2023) for large gradient vectors with thousands of parameters. Our open-source implementation is available at https://github.com/vt-asaplab/e-SeaFL.

cs.CR↗

arXiv2023

ezDPS: An Efficient and Zero-Knowledge Machine Learning Inference Pipeline

Machine Learning as a service (MLaaS) permits resource-limited clients to access powerful data analytics services ubiquitously. Despite its merits, MLaaS poses significant concerns regarding the integrity of delegated computation and the privacy of the server's model parameters. To address this issue, Zhang et al. (CCS'20) initiated the study of zero-knowledge Machine Learning (zkML). Few zkML schemes have been proposed afterward; however, they focus on sole ML classification algorithms that may not offer satisfactory accuracy or require large-scale training data and model parameters, which may not be desirable for some applications. We propose ezDPS, a new efficient and zero-knowledge ML inference scheme. Unlike prior works, ezDPS is a zkML pipeline in which the data is processed in multiple stages for high accuracy. Each stage of ezDPS is harnessed with an established ML algorithm that is shown to be effective in various applications, including Discrete Wavelet Transformation, Principal Components Analysis, and Support Vector Machine. We design new gadgets to prove ML operations effectively. We fully implemented ezDPS and assessed its performance on real datasets. Experimental results showed that ezDPS achieves one-to-three orders of magnitude more efficient than the generic circuit-based approach in all metrics while maintaining more desirable accuracy than single ML classification approaches.

cs.CR↗

arXiv2022

Harpocrates: Privacy-Preserving and Immutable Audit Log for Sensitive Data Operations

The audit log is a crucial component to monitor fine-grained operations over sensitive data (e.g., personal, health) for security inspection and assurance. Since such data operations can be highly sensitive, it is vital to ensure that the audit log achieves not only validity and immutability, but also confidentiality against active threats to standard data regulations (e.g., HIPAA) compliance. Despite its critical needs, state-of-the-art privacy-preserving audit log schemes (e.g., Ghostor (NSDI '20), Calypso (VLDB '19)) do not fully obtain a high level of privacy, integrity, and immutability simultaneously, in which certain information (e.g., user identities) is still leaked in the log. In this paper, we propose Harpocrates, a new privacy-preserving and immutable audit log scheme. Harpocrates permits data store, share, and access operations to be recorded in the audit log without leaking sensitive information (e.g., data identifier, user identity), while permitting the validity of data operations to be publicly verifiable. Harpocrates makes use of blockchain techniques to achieve immutability and avoid a single point of failure, while cryptographic zero-knowledge proofs are harnessed for confidentiality and public verifiability. We analyze the security of our proposed technique and prove that it achieves non-malleability and indistinguishability. We fully implemented Harpocrates and evaluated its performance on a real blockchain system (i.e., Hyperledger Fabric) deployed on a commodity platform (i.e., Amazon EC2). Experimental results demonstrated that Harpocrates is highly scalable and achieves practical performance.

cs.CR↗

arXiv2022

Efficient Privacy-Preserving Machine Learning with Lightweight Trusted Hardware

In this paper, we propose a new secure machine learning inference platform assisted by a small dedicated security processor, which will be easier to protect and deploy compared to today's TEEs integrated into high-performance processors. Our platform provides three main advantages over the state-of-the-art: (i) We achieve significant performance improvements compared to state-of-the-art distributed Privacy-Preserving Machine Learning (PPML) protocols, with only a small security processor that is comparable to a discrete security chip such as the Trusted Platform Module (TPM) or on-chip security subsystems in SoCs similar to the Apple enclave processor. In the semi-honest setting with WAN/GPU, our scheme is 4X-63X faster than Falcon (PoPETs'21) and AriaNN (PoPETs'22) and 3.8X-12X more communication efficient. We achieve even higher performance improvements in the malicious setting. (ii) Our platform guarantees security with abort against malicious adversaries under honest majority assumption. (iii) Our technique is not limited by the size of secure memory in a TEE and can support high-capacity modern neural networks like ResNet18 and Transformer. While previous work investigated the use of high-performance TEEs in PPML, this work represents the first to show that even tiny secure hardware with really limited performance can be leveraged to significantly speed-up distributed PPML protocols if the protocol can be carefully designed for lightweight trusted hardware.

cs.CR↗

arXiv2022

Second-Harmonic Generation Enhancement by Film-Coupled Plasmonic Nanopatch Antennas With Integrated Quantum Emitters

Plasmonic nanocavities have proven to be a powerful optical platform for confining light at a length scale much smaller than the diffraction limit. Enhanced electromagnetic fields within the cavity mode volume enable multiple significant effects that lead to applications in both the linear and nonlinear optical regimes. In this work, we demonstrate enhanced second harmonic generation from individual plasmonic nanopatch antennas which are formed by separating silver nanocubes from a smooth gold film using a sub-10 nm zinc oxide spacer layer. When the nanopatch antennas are excited at their fundamental plasmon frequency, a 10^4-fold increase in the intensity of the second harmonic generation wave is observed. Moreover, by integrating quantum emitters that have an absorption energy at the fundamental frequency, a second order nonlinear exciton-polariton strong coupling response is observed with a Rabi splitting energy of 19 meV. The nonlinear frequency conversion using nanopatch antennas thus provides an excellent platform for nonlinear control of the light-matter interactions in both weak and strong coupling regimes which will have a great potential for applications in optical engineering and information processing.

physics.optics↗

arXiv2021

Near-field imaging of plasmonic nanopatch antennas with integrated semiconductor quantum dots

Plasmonic nanopatch antennas that incorporate dielectric gaps hundreds of picometers to several nanometers thick have drawn increasing attention over the past decade because they confine electromagnetic fields to grossly sub-diffraction limited volumes. Substantial control over the optical properties of excitons and color centers confined within these plasmonic cavities has already been demonstrated with far-field optical spectroscopies, but near-field optical spectroscopies are essential to an improved understanding of the plasmon-emitter interaction at the nanoscale. Here, we characterize the intensity and phase-resolved plasmonic response of isolated nanopatch antennas with cathodoluminescence microscopy. Further, we explore the distinction between optical and electron-beam spectroscopies of coupled plasmon-exciton heterostructures to identify constraints and opportunities for future nanoscale characterization and control of hybrid nanophotonic structures. While we observe substantial Purcell enhancement in time-resolved photoluminescence spectroscopies, negligible Purcell enhancement is observed in cathodoluminescence spectroscopies of hybrid nanophotonic structures. The substantial differences in measured Purcell enhancement for electron-beam and laser excitation can be understood as a result of the different selection rules for these complementary experiments. These results provide a fundamentally new understanding of near-field plasmon-exciton interactions in nanopatch antennas that is essential to myriad emerging quantum photonic devices.

physics.optics↗

arXiv2021

Enhanced lasing action and spontaneous emission in periodic plasmonic nanohole arrays

Periodic arrays of air nanoholes in thin metal films that support surface plasmon resonances can provide an alternative approach for boosting the light-matter interactions at the nanoscale. Indeed, nanohole arrays have garnered great interest in recent years for their use in biosensing, light emission enhancement and spectroscopy. However, the large-scale use of nanohole arrays in emerging technology requires new low-cost fabrication techniques. Here, we demonstrate a simple technique to fabricate nanohole arrays and examine their photonic applications. In contrast to the complicated and most commonly used electron beam lithography technique, hexagonal arrays of nanoholes are fabricated by using a simple combination of shadowing nanosphere lithography technique and electron beam deposition. These arrays are shown to offer enhancements in the lasing emission of an organic dye liquid gain medium with a quality factor above 150. Additionally, a 7-fold increase in Purcell factor is observed for CdSe quantum dot-integrated nanohole arrays.

physics.optics↗

arXiv2020

Temperature- and Polarization- Dependent Optical Properties of Single Si2Te3 Nanoplates

We report a combined experimental and computational study of the optical properties of individual silicon telluride (Si2Te3) nanoplates. The p-type semiconductor Si2Te3 has a unique layered crystal structure with hexagonal closed-packed Te sublattices and Si-Si dimers occupying octahedral intercalation sites. The orientation of the silicon dimers leads to unique optical and electronic properties. Two-dimensional Si2Te3 nanoplates with thicknesses of hundreds of nanometers and lateral sizes of tens of micrometers are synthesized by a chemical vapor deposition technique. At temperatures below 150 K, the Si2Te3 nanoplates exhibit a direct band structure with a band gap energy of 2.394 eV at 7 K and an estimated free exciton binding energy of 150 meV. Polarized reflection measurements at different temperatures show anisotropy in the absorption coefficient due to an anisotropic orientation of the silicon dimers, which is in excellent agreement with theoretical calculations of the dielectric functions. Polarized Raman measurements of single Si2Te3 nanoplates at different temperatures reveal various vibrational modes, which agree with density functional perturbation theory calculations. The unique structural and optical properties of nanostructured Si2Te3 hold great potential applications in optoelectronics and chemical sensing.

cond-mat.mtrl-sci↗

arXiv2020

Plasmonic random laser on an optical fiber tip

Random lasing occurs as the result of a coherent optical feedback from multiple scattering centers. Here, we demonstrate that plasmonic gold nanostars are efficient light scattering centers, exhibiting strong field enhancement at their nanotips, which assists a very narrow bandwidth and highly amplified coherent random lasing with a low lasing threshold. First, by embedding plasmonic gold nanostars in a rhodamine 6G dye gain medium, we observe a series of very narrow random lasing peaks with full-width at half-maximum ~ 0.8 nm. In contrast, free rhodamine 6G dye molecules exhibit only a single amplified spontaneous emission peak with a broader linewidth of 6 nm. The lasing threshold for the dye with gold nanostars is two times lower than that for a free dye. Furthermore, by coating the tip of a single-mode optical fiber with gold nanostars, we demonstrate a collection of random lasing signal through the fiber that can be easily guided and analyzed. Time-resolved measurements show a significant increase in the emission rate above the lasing threshold, indicating a stimulated emission process. Our study provides a method for generating random lasing in the nanoscale with low threshold values that can be easily collected and guided, which promise a range of potential applications in remote sensing, information processing, and on-chip coherent light sources.

physics.optics↗

arXiv2017

Single wavelength 480 Gb/s direct detection over 80km SSMF enabled by Stokes Vector Kramers Kronig transceiver

We propose 4D modulation with directed detection employing a novel Stokes-Vector Kramers-Kronig transceiver. It shows that employing Stokes vector receiver, transmitted digital carrier and Kramers-Kronig detection offers an effective way to de-rotate polarization multiplexed complex double side band signal without using a local oscillator at receiver. The impact of system parameters and configurations including carrier-to-signal-power ratio, guard band of the digital carrier, oversampling ratio and real MIMO is experimentally investigated. Finally, a record 480 Gb/s data rate over 80 km SSMF is achieved in a 60 Gbaud PDM-16QAM single carrier experiment with a BER below the threshold of 2.0x10-2

eess.SP↗

arXiv2016

On the Instability of Sensor Orientation in Gait Verification on Mobile Phone

Authentication schemes using tokens or biometric modalities have been proposed to ameliorate the security strength on mobile devices. However, the existing approaches are obtrusive since the user is required to perform explicit gestures in order to be authenticated. While the gait signal captured by inertial sensors is understood to be a reliable profile for effective implicit authentication, recent studies have been conducted in ideal conditions and might therefore be inapplicable in the real mobile context. Particularly, the acquiring sensor is always fixed to a specific position and orientation. This paper mainly focuses on addressing the instability of sensor's orientation which mostly happens in the reality. A flexible solution taking advantages of available sensors on mobile devices which can help to handle this problem is presented. Moreover, a novel gait recognition method utilizes statistical analysis and supervised learning to adapt itself to the instability of the biometric gait under various circumstances is also proposed. By adopting PCA+SVM to construct the gait model, the proposed method outperformed other state-of-the-art studies, with an equal error rate of 2.45\% and accuracy rate of 99.14\% in terms of the verification and identification aspects being achieved, respectively.

cs.CR↗