Information Leakage in Optimal Anonymized and Diversified Data. - Chengfang Fang; Ee-Chien Chang (IACR Cryptol. ePrint Arch., 2008)

A Lightweight Mechanism to Mitigate Application Layer DDoS Attacks. - Jie Yu 008; Chengfang Fang; Liming Lu; Zhoujun Li 001 (Infoscale, 2009)

ID Repetition in Kad. - Jie Yu 008; Chengfang Fang; Jia Xu 006; Ee-Chien Chang; Zhoujun Li 001 (Peer-to-Peer Computing, 2009)

Secure Sketch for Multiple Secrets. - Chengfang Fang; Qiming Li; Ee-Chien Chang (ACNS, 2010)

Securing interactive sessions using mobile device through visual channel and visual inspection. - Chengfang Fang; Ee-Chien Chang (ACSAC, 2010)

A chameleon encryption scheme resistant to known-plaintext attack. - Ee-Chien Chang; Chengfang Fang; Jia Xu 006 (Digital Rights Management Workshop, 2010)

Securing Kiosk using Mobile Devices coupled with Visual Inspection - Chengfang Fang; Ee-Chien Chang (CoRR, 2010)

Identity leakage mitigation on asymmetric secure sketch. - Chengfang Fang; Qiming Li; Ee-Chien Chang (IJCB, 2011)

ID Repetition in Structured P2P Networks. - Jie Yu 008; Zhoujun Li 001; Peng Xiao; Chengfang Fang; Jia Xu 006; Ee-Chien Chang (Comput. J., 2011)

Publishing Location Dataset Differential Privately with Isotonic Regression - Chengfang Fang; Ee-Chien Chang (CoRR, 2011)

Intrusion diagnosis and prediction with expert system. - Xuejiao Liu 002; Chengfang Fang; Debao Xiao (Secur. Commun. Networks, 2011)

Adaptive Differentially Private Histogram of Low-Dimensional Data. - Chengfang Fang; Ee-Chien Chang (Privacy Enhancing Technologies, 2012)

Differential privacy with δ-neighbourhood for spatial and dynamic datasets. - Chengfang Fang; Ee-Chien Chang (AsiaCCS, 2014)

An Optimization Model for Aesthetic Two-Dimensional Barcodes. - Chengfang Fang; Chunwang Zhang; Ee-Chien Chang (MMM (1), 2014)

Optimal strategy of coupon subset collection when each package contains half of the coupons. - Chengfang Fang; Ee-Chien Chang (Inf. Process. Lett., 2014)

BDPL: A Boundary Differentially Private Layer Against Machine Learning Model Extraction Attacks. - Huadi Zheng; Qingqing Ye 001; Haibo Hu 001; Chengfang Fang; Jie Shi (ESORICS (1), 2019)

Where Does the Robustness Come from?: A Study of the Transformation-based Ensemble Defence. - Chang Liao; Yao Cheng; Chengfang Fang; Jie Shi (AISec@CCS, 2020)

Bident Structure for Neural Network Model Protection. - Hsiao-Ying Lin; Chengfang Fang; Jie Shi (ICISSP, 2020)

Cert-RNN: Towards Certifying the Robustness of Recurrent Neural Networks. - Tianyu Du; Shouling Ji; Lujia Shen; Yao Zhang 019; Jinfeng Li; Jie Shi; Chengfang Fang; Jianwei Yin; Raheem Beyah; Ting Wang 006 (CCS, 2021)

Backdoor Pre-trained Models Can Transfer to All. - Lujia Shen; Shouling Ji; Xuhong Zhang 002; Jinfeng Li; Jing Chen 003; Jie Shi; Chengfang Fang; Jianwei Yin; Ting Wang 006 (CCS, 2021)

A-FMI: Learning Attributions from Deep Networks via Feature Map Importance. - An Zhang 003; Xiang Wang 010; Chengfang Fang; Jie Shi; Tat-Seng Chua; Zehua Chen (CoRR, 2021)

Thief, Beware of What Get You There: Towards Understanding Model Extraction Attack. - Xinyi Zhang; Chengfang Fang; Jie Shi (CoRR, 2021)

MExMI: Pool-based Active Model Extraction Crossover Membership Inference. - Yaxin Xiao; Qingqing Ye 001; Haibo Hu 001; Huadi Zheng; Chengfang Fang; Jie Shi (NeurIPS, 2022)

A Survey on Reinforcement Learning Security with Application to Autonomous Driving. - Ambra Demontis; Maura Pintor; Luca Demetrio; Kathrin Grosse; Hsiao-Ying Lin; Chengfang Fang; Battista Biggio; Fabio Roli (CoRR, 2022)

Protecting Decision Boundary of Machine Learning Model With Differentially Private Perturbation. - Huadi Zheng; Qingqing Ye 001; Haibo Hu 001; Chengfang Fang; Jie Shi (IEEE Trans. Dependable Secur. Comput., 2022)

QUDA: Query-Limited Data-Free Model Extraction. - Zijun Lin; Ke Xu; Chengfang Fang; Huadi Zheng; Aneez Ahmed Jaheezuddin; Jie Shi (AsiaCCS, 2023)

Mitigating Adversarial Attacks by Distributing Different Copies to Different Buyers. - Jiyi Zhang; Han Fang; Wesley Joon-Wie Tann; Ke Xu; Chengfang Fang; Ee-Chien Chang (AsiaCCS, 2023)

Tracing the Origin of Adversarial Attack for Forensic Investigation and Deterrence. - Han Fang; Jiyi Zhang; Yupeng Qiu; Jiayang Liu; Ke Xu; Chengfang Fang; Ee-Chien Chang (ICCV, 2023)

Differential Aggregation against General Colluding Attackers. - Rong Du; Qingqing Ye 001; Yue Fu; Haibo Hu 001; Jin Li 002; Chengfang Fang; Jie Shi (ICDE, 2023)

Detecting Attacks Against Deep Reinforcement Learning for Autonomous Driving. - Maura Pintor; Luca Demetrio; Angelo Sotgiu; Hsiao-Ying Lin; Chengfang Fang; Ambra Demontis; Battista Biggio (ICMLC, 2023)

DoubleDeceiver: Deceiving the Speaker Verification System Protected by Spoofing Countermeasures. - Mengao Zhang; Ke Xu; Hao Li; Lei Wang; Chengfang Fang; Jie Shi (INTERSPEECH, 2023)

DeNoL: A Few-Shot-Sample-Based Decoupling Noise Layer for Cross-channel Watermarking Robustness. - Han Fang; Kejiang Chen; Yupeng Qiu; Jiayang Liu; Ke Xu; Chengfang Fang; Weiming Zhang 001; Ee-Chien Chang (ACM Multimedia, 2023)

3DFed: Adaptive and Extensible Framework for Covert Backdoor Attack in Federated Learning. - Haoyang Li; Qingqing Ye 001; Haibo Hu 001; Jin Li 002; Leixia Wang; Chengfang Fang; Jie Shi (SP, 2023)

Tracing the Origin of Adversarial Attack for Forensic Investigation and Deterrence. - Han Fang; Jiyi Zhang; Yupeng Qiu; Ke Xu; Chengfang Fang; Ee-Chien Chang (CoRR, 2023)

PrivKVM*: Revisiting Key-Value Statistics Estimation With Local Differential Privacy. - Qingqing Ye 001; Haibo Hu 001; Xiaofeng Meng 001; Huadi Zheng; Kai Huang 011; Chengfang Fang; Jie Shi (IEEE Trans. Dependable Secur. Comput., 2023)

ADVSV: An Over-the-Air Adversarial Attack Dataset for Speaker Verification. - Li Wang; Jiaqi Li; Yuhao Luo; Jiahao Zheng 002; Lei Wang; Hao Li; Ke Xu; Chengfang Fang; Jie Shi; Zhizheng Wu 001 (ICASSP, 2024)

Perfect Gradient Inversion in Federated Learning: A New Paradigm from the Hidden Subset Sum Problem. - Qiongxiu Li; Lixia Luo; Agnese Gini; Changlong Ji; Zhanhao Hu; Xiao Li 028; Chengfang Fang; Jie Shi; Xiaolin Hu 001 (CoRR, 2024)

RMR: A Relative Membership Risk Measure for Machine Learning Models. - Li Bai 004; Haibo Hu 001; Qingqing Ye 001; Jianliang Xu; Jin Li 002; Chengfang Fang; Jie Shi (IEEE Trans. Dependable Secur. Comput., 2025)