294 citations · 433 across the 18 of their papers we have counts for
18 papers
CAFE: Catastrophic Data Leakage in Vertical Federated Learning
Xiao Jin, Pin-Yu Chen, Chia-Yi Hsu +2
Recent studies show that private training data can be leaked through the gradients sharing mechanism deployed in distributed machine learning systems, such as federated learning (F…
Perceptual Indistinguishability-Net (PI-Net): Facial Image Obfuscation with Manipulable Semantics
Jia-Wei Chen, Li-Ju Chen, Chia-Mu Yu +1
With the growing use of camera devices, the industry has many image datasets that provide more opportunities for collaboration between the machine learning community and industry.…
Formalizing Generalization and Robustness of Neural Networks to Weight Perturbations
Yu-Lin Tsai, Chia-Yi Hsu, Chia-Mu Yu +1
Studying the sensitivity of weight perturbation in neural networks and its impacts on model performance, including generalization and robustness, is an active research topic due to…
Adversarial Examples can be Effective Data Augmentation for Unsupervised Machine Learning
Chia-Yi Hsu, Pin-Yu Chen, Songtao Lu +2
Adversarial examples causing evasive predictions are widely used to evaluate and improve the robustness of machine learning models. However, current studies focus on supervised lea…
Non-Singular Adversarial Robustness of Neural Networks
Yu-Lin Tsai, Chia-Yi Hsu, Chia-Mu Yu +1
Adversarial robustness has become an emerging challenge for neural network owing to its over-sensitivity to small input perturbations. While being critical, we argue that solving t…
DPCrowd: Privacy-preserving and Communication-efficient Decentralized Statistical Estimation for Real-time Crowd-sourced Data
Xuebin Ren, Chia-Mu Yu, Wei Yu +3
In Internet of Things (IoT) driven smart-world systems, real-time crowd-sourced databases from multiple distributed servers can be aggregated to extract dynamic statistics from a l…
Privacy in Data Service Composition
Mahmoud Barhamgi, Charith Perera, Chia-Mu Yu +3
In modern information systems different information features, about the same individual, are often collected and managed by autonomous data collection services that may have differ…
Detecting Deepfake-Forged Contents with Separable Convolutional Neural Network and Image Segmentation
Chia-Mu Yu, Ching-Tang Chang, Yen-Wu Ti
Recent advances in AI technology have made the forgery of digital images and videos easier, and it has become significantly more difficult to identify such forgeries. These forgeri…
Locally Differentially Private Minimum Finding
Kazuto Fukuchi, Chia-Mu Yu, Arashi Haishima +1
We investigate a problem of finding the minimum, in which each user has a real value and we want to estimate the minimum of these values under the local differential privacy constr…
On The Utility of Conditional Generation Based Mutual Information for Characterizing Adversarial Subspaces
Chia-Yi Hsu, Pei-Hsuan Lu, Pin-Yu Chen +1
Recent studies have found that deep learning systems are vulnerable to adversarial examples; e.g., visually unrecognizable adversarial images can easily be crafted to result in mis…
On the Limitation of MagNet Defense against -based Adversarial Examples
Pei-Hsuan Lu, Pin-Yu Chen, Kang-Cheng Chen +1
In recent years, defending adversarial perturbations to natural examples in order to build robust machine learning models trained by deep neural networks (DNNs) has become an emerg…
On the Limitation of Local Intrinsic Dimensionality for Characterizing the Subspaces of Adversarial Examples
Pei-Hsuan Lu, Pin-Yu Chen, Chia-Mu Yu
Understanding and characterizing the subspaces of adversarial examples aid in studying the robustness of deep neural networks (DNNs) to adversarial perturbations. Very recently, Ma…
Data-Driven and Deep Learning Methodology for Deceptive Advertising and Phone Scams Detection
TonTon Hsien-De Huang, Chia-Mu Yu, Hung-Yu Kao
The advance of smartphones and cellular networks boosts the need of mobile advertising and targeted marketing. However, it also triggers the unseen security threats. We found that…
R2-D2: ColoR-inspired Convolutional NeuRal Network (CNN)-based AndroiD Malware Detections
TonTon Hsien-De Huang, Hung-Yu Kao
The influence of Deep Learning on image identification and natural language processing has attracted enormous attention globally. The convolution neural network that can learn with…
LoPub: High-Dimensional Crowdsourced Data Publication with Local Differential Privacy
Xuebin Ren, Chia-Mu Yu, Weiren Yu +4
High-dimensional crowdsourced data collected from a large number of users produces rich knowledge for our society. However, it also brings unprecedented privacy threats to particip…
Reducing Reconciliation Communication Cost with Compressed Sensing
H. T. Kung, Chia-Mu Yu
We consider a reconciliation problem, where two hosts wish to synchronize their respective sets. Efficient solutions for minimizing the communication cost between the two hosts hav…
Secure Multidimensional Queries in Tiered Sensor Networks
Chia-Mu Yu, Chun-Shien Lu, Sy-Yen Kuo
In this paper, aiming at securing range query, top-k query, and skyline query in tiered sensor networks, we propose the Secure Range Query (SRQ), Secure Top- Query (STQ), and Se…
Constrained Function Based En-Route Filtering for Sensor Networks
Chia-Mu Yu, Chun-Shien Lu, Sy-Yen Kuo
Sensor networks are vulnerable to \emph{false data injection attack} and \emph{path-based DoS} (PDoS) attack. While conventional authentication schemes are insufficient for solving…