294 citations · 535 across the 22 of their papers we have counts for
22 papers
Fuzziness-tuned: Improving the Transferability of Adversarial Examples
Xiangyuan Yang, Jie Lin, Hanlin Zhang +2
With the development of adversarial attacks, adversairal examples have been widely used to enhance the robustness of the training models on deep neural networks. Although considera…
Improving the Transferability of Adversarial Examples via Direction Tuning
Xiangyuan Yang, Jie Lin, Hanlin Zhang +2
In the transfer-based adversarial attacks, adversarial examples are only generated by the surrogate models and achieve effective perturbation in the victim models. Although conside…
Improving the Robustness and Generalization of Deep Neural Network with Confidence Threshold Reduction
Xiangyuan Yang, Jie Lin, Hanlin Zhang +2
Deep neural networks are easily attacked by imperceptible perturbation. Presently, adversarial training (AT) is the most effective method to enhance the robustness of the model aga…
FACM: Intermediate Layer Still Retain Effective Features against Adversarial Examples
Xiangyuan Yang, Jie Lin, Hanlin Zhang +2
In strong adversarial attacks against deep neural networks (DNN), the generated adversarial example will mislead the DNN-implemented classifier by destroying the output features of…
Gradient Aligned Attacks via a Few Queries
Xiangyuan Yang, Jie Lin, Hanlin Zhang +2
Black-box query attacks, which rely only on the output of the victim model, have proven to be effective in attacking deep learning models. However, existing black-box query attacks…
Mixed Strategies for Security Games with General Defending Requirements
Rufan Bai, Haoxing Lin, Xinyu Yang +3
The Stackelberg security game is played between a defender and an attacker, where the defender needs to allocate a limited amount of resources to multiple targets in order to minim…
ACE: Towards Application-Centric Edge-Cloud Collaborative Intelligence
Luhui Wang, Cong Zhao, Shusen Yang +2
Intelligent applications based on machine learning are impacting many parts of our lives. They are required to operate under rigorous practical constraints in terms of service late…
Defending against Contagious Attacks on a Network with Resource Reallocation
Rufan Bai, Haoxing Lin, Xinyu Yang +3
In classic network security games, the defender distributes defending resources to the nodes of the network, and the attacker attacks a node, with the objective to maximize the dam…
Latent Dirichlet Allocation Model Training with Differential Privacy
Fangyuan Zhao, Xuebin Ren, Shusen Yang +3
Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for hidden semantic discovery of text data and serves as a fundamental tool for text analysis in various app…
A Comprehensive Survey on Local Differential Privacy Toward Data Statistics and Analysis
Teng Wang, Xuefeng Zhang, Jingyu Feng +1
Collecting and analyzing massive data generated from smart devices have become increasingly pervasive in crowdsensing, which are the building blocks for data-driven decision-making…
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…
Preserving Dynamic Attention for Long-Term Spatial-Temporal Prediction
Haoxing Lin, Rufan Bai, Weijia Jia +2
Effective long-term predictions have been increasingly demanded in urban-wise data mining systems. Many practical applications, such as accident prevention and resource pre-allocat…
OL4EL: Online Learning for Edge-cloud Collaborative Learning on Heterogeneous Edges with Resource Constraints
Qing Han, Shusen Yang, Xuebin Ren +3
Distributed machine learning (ML) at network edge is a promising paradigm that can preserve both network bandwidth and privacy of data providers. However, heterogeneous and limited…
Reviewing and Improving the Gaussian Mechanism for Differential Privacy
Jun Zhao, Teng Wang, Tao Bai +7
Differential privacy provides a rigorous framework to quantify data privacy, and has received considerable interest recently. A randomized mechanism satisfying -differentia…
On Privacy Protection of Latent Dirichlet Allocation Model Training
Fangyuan Zhao, Xuebin Ren, Shusen Yang +1
Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for discovery of hidden semantic architecture of text datasets, and plays a fundamental role in many machine…
Impact of Prior Knowledge and Data Correlation on Privacy Leakage: A Unified Analysis
Yanan Li, Xuebin Ren, Shusen Yang +1
It has been widely understood that differential privacy (DP) can guarantee rigorous privacy against adversaries with arbitrary prior knowledge. However, recent studies demonstrate…
Locally Differentially Private Data Collection and Analysis
Teng Wang, Jun Zhao, Xinyu Yang +1
Local differential privacy (LDP) can provide each user with strong privacy guarantees under untrusted data curators while ensuring accurate statistics derived from privatized data.…
Privacy-preserving Crowd-guided AI Decision-making in Ethical Dilemmas
Teng Wang, Jun Zhao, Han Yu +4
With the rapid development of artificial intelligence (AI), ethical issues surrounding AI have attracted increasing attention. In particular, autonomous vehicles may face moral dil…
Energy-efficient Analytics for Geographically Distributed Big Data
Peng Zhao, Shusen Yang, Xinyu Yang +2
Big data analytics on geographically distributed datasets (across data centers or clusters) has been attracting increasing interests from both academia and industry, but also signi…
Cheating-Resilient Incentive Scheme for Mobile Crowdsensing Systems
Cong Zhao, Xinyu Yang, Wei Yu +3
Mobile Crowdsensing is a promising paradigm for ubiquitous sensing, which explores the tremendous data collected by mobile smart devices with prominent spatial-temporal coverage. A…
Rapid, User-Transparent, and Trustworthy Device Pairing for D2D-Enabled Mobile Crowdsourcing
Cong Zhao, Shusen Yang, Xinyu Yang +1
Mobile Crowdsourcing is a promising service paradigm utilizing ubiquitous mobile devices to facilitate largescale crowdsourcing tasks (e.g. urban sensing and collaborative computin…
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…