Publications

[Google Scholar Page]

2024

• Hongbin Liu, Moyang Guo, Zhengyuan Jiang, Lun Wang, and Neil Zhenqiang Gong. "AudioMarkBench: Benchmarking Robustness of Audio Watermarking". In NeurIPS Datasets and Benchmarks, 2024.
• Bo Hui, Haolin Yuan, Neil Gong, Philippe Burlina, and Yinzhi Cao. "PLeak: Prompt Leaking Attacks against Large Language Model Applications". In ACM Conference on Computer and Communications Security (CCS), 2024.
• Jiawen Shi, Zenghui Yuan, Yinuo Liu, Yue Huang, Pan Zhou, Lichao Sun, and Neil Zhenqiang Gong. "Optimization-based Prompt Injection Attack to LLM-as-a-Judge". In ACM Conference on Computer and Communications Security (CCS), 2024.
• Zonghao Huang, Neil Zhenqiang Gong, and Michael K Reiter. "A General Framework for Data-Use Auditing of ML Models". In ACM Conference on Computer and Communications Security (CCS), 2024.
• Minghong Fang, Zifan Zhang, Hairi, Prashant Khanduri, Jia Liu, Songtao Lu, Yuchen Liu, and Neil Gong. "Byzantine-Robust Decentralized Federated Learning". In ACM Conference on Computer and Communications Security (CCS), 2024.
• Roy Xie, Junlin Wang, Ruomin Huang, Minxing Zhang, Rong Ge, Jian Pei, Neil Zhenqiang Gong, and Bhuwan Dhingra. "ReCaLL: Membership Inference via Relative Conditional Log-Likelihoods". In Conference on Empirical Methods in Natural Language Processing (EMNLP), 2024.
• Zhengyuan Jiang, Moyang Guo, Yuepeng Hu, Jinyuan Jia, and Neil Zhenqiang Gong. "Certifiably Robust Image Watermark". In European Conference on Computer Vision (ECCV), 2024.
• Wen Huang, Hongbin Liu, Minxin Guo, and Neil Zhenqiang Gong. "Visual Hallucinations of Multi-modal Large Language Models". In Findings of the Association for Computational Linguistics (ACL Findings), 2024.
• Yueqi Xie, Minghong Fang, Renjie Pi, and Neil Zhenqiang Gong. "GradSafe: Detecting Jailbreak Prompts for LLMs via Safety-Critical Gradient Analysis". In Annual Meeting of the Association for Computational Linguistics (ACL), 2024.
• Yupei Liu, Yuqi Jia, Runpeng Geng, Jinyuan Jia, and Neil Zhenqiang Gong. "Formalizing and Benchmarking Prompt Injection Attacks and Defenses". In USENIX Security Symposium, 2024.
• Hongbin Liu, Michael K Reiter, and Neil Zhenqiang Gong. "Mudjacking: Patching Backdoor Vulnerabilities in Foundation Models". In USENIX Security Symposium, 2024.
• Minxue Tang, Anna Dai, Louis DiValentin, Aolin Ding, Amin Hass, Neil Zhenqiang Gong, and Yiran Chen. "ModelGuard: Information-Theoretic Defense Against Model Extraction Attacks". In USENIX Security Symposium, 2024.
• Yueqi Xie, Minghong Fang, and Neil Zhenqiang Gong. "FedREDefense: Defending against Model Poisoning Attacks for Federated Learning using Model Update Reconstruction Error". In International Conference on Machine Learning (ICML), 2024.
• Sun et al. "Position: TrustLLM: Trustworthiness in Large Language Models". In International Conference on Machine Learning (ICML), 2024.
• Yixin Wu, Xinlei He, Pascal Berrang, Mathias Humbert, Michael Backes, Neil Zhenqiang Gong, and Yang Zhang. "Link Stealing Attacks Against Inductive Graph Neural Networks". In Privacy Enhancing Technologies Symposium (PETS), 2024.
• Wei Sun, Tingjun Chen, and Neil Gong. "SoK: Secure Human-centered Wireless Sensing". In Privacy Enhancing Technologies Symposium (PETS), 2024.
• Jinghuai Zhang, Hongbin Liu, Jinyuan Jia, and Neil Zhenqiang Gong. "Data Poisoning based Backdoor Attacks to Contrastive Learning". In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024.
• Ming Yin, Yichang Xu, Minghong Fang, and Neil Zhenqiang Gong. "Poisoning Federated Recommender Systems with Fake Users". In The Web Conference (WWW), 2024.
• Yichang Xu, Ming Yin, Minghong Fang, and Neil Zhenqiang Gong. "Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks". In The Web Conference (WWW), short paper, 2024.
• Yue Huang, Jiawen Shi, Yuan Li, Chenrui Fan, Siyuan Wu, Qihui Zhang, Yixin Liu, Pan Zhou, Yao Wan, Neil Zhenqiang Gong, and Lichao Sun. "MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use". In International Conference on Learning Representations (ICLR), 2024.
• Kaijie Zhu, Jiaao Chen, Jindong Wang, Neil Zhenqiang Gong, Diyi Yang, and Xing Xie. "Dyval: Dynamic evaluation of large language models for reasoning tasks". In International Conference on Learning Representations (ICLR), 2024.
• Yuchen Yang, Bo Hui, Haolin Yuan, Neil Gong, and Yinzhi Cao. "SneakyPrompt: Jailbreaking Text-to-image Generative Models". In IEEE Symposium on Security and Privacy (IEEE S&P), 2024.

2023

• Zhengyuan Jiang, Jinghuai Zhang, and Neil Zhenqiang Gong. "Evading Watermark based Detection of AI-Generated Content". In ACM Conference on Computer and Communications Security (CCS), 2023.
• Jinyuan Jia, Yupei Liu, Yuepeng Hu, and Neil Zhenqiang Gong. "PORE: Provably Robust Recommender Systems against Data Poisoning Attacks". In USENIX Security Symposium, 2023.
• Yuchen Yang, Bo Hui, Haolin Yuan, Neil Gong, and Yinzhi Cao. "PrivateFL: Accurate, Differentially Private Federated Learning via Personalized Data Transformation". In USENIX Security Symposium, 2023.
• Xiaoguang Li, Ninghui Li, Wenhai Sun, Neil Zhenqiang Gong, and Hui Li. "Fine-grained Poisoning Attack to Local Differential Privacy Protocols for Mean and Variance Estimation". In USENIX Security Symposium, 2023.
• Jinghuai Zhang, Jinyuan Jia, Hongbin Liu, and Neil Zhenqiang Gong. "PointCert: Point Cloud Classification with Deterministic Certified Robustness Guarantees". In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
• Xiaoyu Cao, Jinyuan Jia, Zaixi Zhang, and Neil Zhenqiang Gong. "FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Information". In IEEE Symposium on Security and Privacy, 2023.
  Note: FedRecover was implemented in MXNet. When implementing using Pytorch or TensorFlow, a learning rate reported in the paper should be scaled up by the batch size because MXNet uses sum of mini-batch gradient while others use mean.
• Wenjie Qu, Jinyuan Jia, and Neil Zhenqiang Gong. "REaaS: Enabling Adversarially Robust Downstream Classifiers via Robust Encoder as a Service". In ISOC Network and Distributed System Security Symposium (NDSS), 2023.

2022

• Xiaoyu Cao, Zaixi Zhang, Jinyuan Jia, and Neil Zhenqiang Gong. "FLCert: Provably Secure Federated Learning against Poisoning Attacks". IEEE Transactions on Information Forensics and Security (TIFS), 2022.
• Jinyuan Jia, Wenjie Qu, and Neil Zhenqiang Gong. "MultiGuard: Provably Robust Multi-label Classification against Adversarial Examples". In Conference on Neural Information Processing Systems (NeurIPS), 2022.
• Yupei Liu, Jinyuan Jia, Hongbin Liu, and Neil Zhenqiang Gong. "StolenEncoder: Stealing Pre-trained Encoders in Self-supervised Learning". In ACM Conference on Computer and Communications Security (CCS), 2022.
• Haolin Yuan, Bo Hui, Yuchen Yang, Philippe Burlina, Neil Zhenqiang Gong, and Yinzhi Cao. "Addressing Heterogeneity in Federated Learning via Distributional Transformation". In European Conference on Computer Vision (ECCV), 2022.
• Xinlei He, Hongbin Liu, Neil Zhenqiang Gong, and Yang Zhang. "Semi-Leak: Membership Inference Attacks Against Semi-supervised Learning". In European Conference on Computer Vision (ECCV), 2022.
• Zaixi Zhang, Xiaoyu Cao, Jinayuan Jia, and Neil Zhenqiang Gong. "FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients". In ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022.
• Hongbin Liu, Jinyuan Jia, and Neil Zhenqiang Gong. "PoisonedEncoder: Poisoning the Unlabeled Pre-training Data in Contrastive Learning". In USENIX Security Symposium, 2022.
• Yongji Wu, Xiaoyu Cao, Jinyuan Jia, and Neil Zhenqiang Gong. "Poisoning Attacks to Local Differential Privacy Protocols for Key-Value Data". In USENIX Security Symposium, 2022.
• Xiaoyu Cao and Neil Zhenqiang Gong. "MPAF: Model Poisoning Attacks to Federated Learning based on Fake Clients". In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2022.
• Huanrui Yang, Xiaoxuan Yang, Neil Zhenqiang Gong, and Yiran Chen. "HERO: Hessian-Enhanced Robust Optimization for Unifying and Improving Generalization and Quantization Performance". In Design Automation Conference (DAC), 2022.
• Da Zhong, Haipei Sun, Song Li, Jun Xu, Neil Zhenqiang Gong, and Hui (Wendy) Wang. "Understanding Disparate Effects of Membership Inference Attacks and Their Countermeasures". In ACM ASIA Conference on Computer and Communications Security (ASIACCS), 2022.
• Binghui Wang, Tianchen Zhou, Song Li, Yinzhi Cao, and Neil Zhenqiang Gong. "GraphTrack: A Graph-based Cross-Device Tracking Framework". In ACM ASIA Conference on Computer and Communications Security (ASIACCS), 2022.
• Jinyuan Jia, Yupei Liu, and Neil Zhenqiang Gong. "BadEncoder: Backdoor Attacks to Pre-trained Encoders in Self-Supervised Learning". In IEEE Symposium on Security and Privacy, 2022.
• Jinyuan Jia, Binghui Wang, Xiaoyu Cao, Hongbin Liu, and Neil Zhenqiang Gong. "Almost Tight L0-norm Certified Robustness of Top-k Predictions against Adversarial Perturbations". In International Conference on Learning Representations (ICLR), 2022.
• Jinyuan Jia, Yupei Liu, Xiaoyu Cao, and Neil Zhenqiang Gong. "Certified Robustness of Nearest Neighbors against Data Poisoning and Backdoor Attacks". In AAAI Conference on Artificial Intelligence (AAAI), 2022.

2021

• Hongbin Liu*, Jinyuan Jia*, Wenjie Qu, and Neil Zhenqiang Gong. "EncoderMI: Membership Inference against Pre-trained Encoders in Contrastive Learning". In ACM Conference on Computer and Communications Security (CCS), 2021. *Equal contribution
• Binghui Wang, Jinyuan Jia, Xiaoyu Cao, and Neil Zhenqiang Gong. "Certified Robustness of Graph Neural Networks against Adversarial Structural Perturbation". In ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021. [Code]
• Xiao Liang, Zheng Yang, Binghui Wang, Shaofeng Hu, Zijie Yang, Dong Yuan, Neil Zhenqiang Gong, Qi Li, and Fang He. "Unveiling Fake Accounts at the Time of Registration: An Unsupervised Approach". In ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021.
• Hongbin Liu, Jinyuan Jia, and Neil Zhenqiang Gong. "On the Intrinsic Differential Privacy of Bagging". In International Joint Conference on Artificial Intelligence (IJCAI), 2021.
• Xiaoyu Cao, Jinyuan Jia, and Neil Zhenqiang Gong. "Data Poisoning Attacks to Local Differential Privacy Protocols". In USENIX Security Symposium, 2021.
• Xinlei He, Jinyuan Jia, Michael Backes, Neil Zhenqiang Gong, and Yang Zhang. "Stealing Links from Graph Neural Networks". In USENIX Security Symposium, 2021.
• Hongbin Liu*, Jinyuan Jia*, and Neil Zhenqiang Gong. "PointGuard: Provably Robust 3D Point Cloud Classification". In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021. *Equal contribution
• Zaixi Zhang*, Jinyuan Jia*, Binghui Wang, and Neil Zhenqiang Gong. "Backdoor Attacks to Graph Neural Networks". In ACM Symposium on Access Control Models and Technologies (SACMAT), 2021. *Equal contribution
• Minghong Fang, Minghao Sun, Qi Li, Neil Zhenqiang Gong, Jin Tian, and Jia Liu. "Data Poisoning Attacks and Defenses to Crowdsourcing Systems". In The Web Conference (WWW), 2021.
• Yongji Wu, Defu Lian, Neil Zhenqiang Gong, Lu Yin, Mingyang Yin, Jingren Zhou, and Hongxia Yang. "Linear-Time Self Attention with Codeword Histogram for Efficient Recommendation". In The Web Conference (WWW), 2021.
• Xiaoyu Cao, Jinyuan Jia, and Neil Zhenqiang Gong. "IPGuard: Protecting Intellectual Property of Deep Neural Networks via Fingerprinting the Classification Boundary". In ACM ASIA Conference on Computer and Communications Security (ASIACCS), 2021. [Code]
• Jinyuan Jia, Binghui Wang, and Neil Zhenqiang Gong. "Robust and Verifiable Information Embedding Attacks to Deep Neural Networks via Error-Correcting Codes". In ACM ASIA Conference on Computer and Communications Security (ASIACCS), 2021.
• Xiaoyu Cao, Minghong Fang, Jia Liu, and Neil Zhenqiang Gong. "FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping". In ISOC Network and Distributed System Security Symposium (NDSS), 2021. [code] [slides] [talk on YouTube]
  Note: FLTrust was implemented in MXNet. When implementing using Pytorch or TensorFlow, a learning rate reported in the paper should be scaled up by the batch size because MXNet uses sum of mini-batch gradient while others use mean.
• Bo Hui, Yuchen Yang, Haolin Yuan, Philippe Burlina, Neil Zhenqiang Gong, and Yinzhi Cao. "Practical Blind Membership Inference Attack via Differential Comparisons". In ISOC Network and Distributed System Security Symposium (NDSS), 2021.
• Hai Huang, Jiaming Mu, Neil Zhenqiang Gong, Qi Li, Bin Liu, and Mingwei Xu. "Data Poisoning Attacks to Deep Learning Based Recommender Systems". In ISOC Network and Distributed System Security Symposium (NDSS), 2021.
• Xiaoyu Cao, Jinyuan Jia, and Neil Zhenqiang Gong. "Provably Secure Federated Learning against Malicious Clients". In AAAI Conference on Artificial Intelligence (AAAI), 2021. [slides] [talk on YouTube]
• Jinyuan Jia, Xiaoyu Cao, and Neil Zhenqiang Gong. "Intrinsic Certified Robustness of Bagging against Data Poisoning Attacks". In AAAI Conference on Artificial Intelligence (AAAI), 2021.
  Code and data are available [here]
• Binghui Wang, Jinyuan Jia, and Neil Zhenqiang Gong. "Semi-Supervised Node Classification on Graphs: Markov Random Fields vs. Graph Neural Networks". In AAAI Conference on Artificial Intelligence (AAAI), 2021.

2020

2019

• Jinyuan Jia, Ahmed Salem, Michael Backes, Yang Zhang, and Neil Zhenqiang Gong. "MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples". In ACM Conference on Computer and Communications Security (CCS), 2019.
  Code and data are available [here]
• Binghui Wang and Neil Zhenqiang Gong. "Attacking Graph-based Classification via Manipulating the Graph Structure". In ACM Conference on Computer and Communications Security (CCS), 2019.
• Dong Yuan, Yuanli Miao, Neil Zhenqiang Gong, Zheng Yang, Qi Li, Dawn Song, Qian Wang, and Xiao Liang. "Detecting Fake Accounts in Online Social Networks at the Time of Registrations". In ACM Conference on Computer and Communications Security (CCS), 2019.

• Zenghua Xia, Chang Liu, Neil Zhenqiang Gong, Qi Li, Yong Cui, and Dawn Song. "Characterizing and Detecting Malicious Accounts in Privacy-Centric Mobile Social Networks: A Case Study". In ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Applied Data Science Track, 2019.

• Jinyuan Jia and Neil Zhenqiang Gong. "Calibrate: Frequency Estimation and Heavy Hitter Identification with Local Differential Privacy via Incorporating Prior Knowledge". In IEEE International Conference on Computer Communications (INFOCOM), 2019.

• Binghui Wang, Jinyuan Jia, and Neil Zhenqiang Gong. "Graph-based Security and Privacy Analytics via Collective Classification with Joint Weight Learning and Propagation". In ISOC Network and Distributed System Security Symposium (NDSS), 2019.
  Distinguished Paper Award Honorable Mention
• Binghui Wang, Jinyuan Jia, Le Zhang, and Neil Zhenqiang Gong. "Structure-based Sybil Detection in Social Networks via Local Rule-based Propagation". IEEE Transactions on Network Science and Engineering (TNSE), 6(3), 2019.

2018

• Minghong Fang, Guolei Yang, Neil Zhenqiang Gong, and Jia Liu. "Poisoning Attacks to Graph-Based Recommender Systems". In Annual Computer Security Applications Conference (ACSAC), 2018.

• Chris Chao-Chun, Chen Shi, Neil Zhenqiang Gong, and Yong Guan. "EviHunter: Identifying Digital Evidence in the Permanent Storage of Android Devices via Static Analysis". In ACM Conference on Computer and Communications Security (CCS), 2018.

• Jinyuan Jia and Neil Zhenqiang Gong. "AttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine Learning". In USENIX Security Symposium, 2018.
  Code and data are available [here]
  Featured by WIRED, Boing Boing

• Binghui Wang and Neil Zhenqiang Gong. "Stealing Hyperparameters in Machine Learning". In IEEE Symposium on Security and Privacy (IEEE S&P), 2018.
  This paper demonstrates that an adversary can steal the hyperparameters used to train a machine learning model by strategically querying the model.

• Binghui Wang, Le Zhang, and Neil Zhenqiang Gong. "SybilBlind: Detecting Fake Users in Online Social Networks without Manual Labels". In International Symposium on Research in Attacks, Intrusions and Defenses (RAID), 2018.

• Peng Gao, Binghui Wang, Neil Zhenqiang Gong, Sanjeev R. Kulkarni, Kurt Thomas, and Prateek Mittal. "SybilFuse: Combining Local Attributes with Global Structure to Perform Robust Sybil Detection." In IEEE Conference on Communications and Network Security (CNS), 2018.

• Zhen Xu, Chen Shi, Chris Chao-Chun Cheng, Neil Zhengqiang Gong, and Yong Guan. "A Dynamic Taint Analysis Tool for Android App Forensics. In International Workshop on Systematic Approaches to Digital Forensics Engineering (SADFE), 2018.
  Best Paper Award

• Peng Gao, Binghui Wang, Neil Zhenqiang Gong, Sanjeev R. Kulkarni, and Prateek Mittal. "SybilFuse: Combining Local Attributes with Global Structure to Perform Robust Sybil Detection (Extended Abstract)." In Workshop on Misinformation and Misbehavior Mining on the Web (MIS2), co-located with WSDM, 2018.

• Neil Zhenqiang Gong and Bin Liu. "Attribute Inference Attacks in Online Social Networks". ACM Transactions on Privacy and Security (TOPS), 21(1), 2018.

2017

• Xiaoyu Cao and Neil Zhenqiang Gong. "Mitigating Evasion Attacks to Deep Neural Networks via Region-based Classification". In Annual Computer Security Applications Conference (ACSAC), 2017.

• Binghui Wang, Neil Zhenqiang Gong, and Hao Fu. "GANG: Detecting Fraudulent Users in Online Social Networks via Guilt-by-Association on Directed Graphs". In IEEE International Conference on Data Mining (ICDM), regular paper, 2017.

• Hao Fu, Xing Xie, Yong Rui, Neil Zhenqiang Gong, Guangzhong Sun, and Enhong Chen. "Robust Spammer Detection in Microblogs: Leveraging User Carefulness". ACM Transactions on Intelligent Systems and Technology (TIST), 8(6), 2017.

• Jinyuan Jia, Binghui Wang, and Neil Zhenqiang Gong. "Random Walk based Fake Account Detection in Online Social Networks". In IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 2017.

• Neil Zhenqiang Gong, Altay Ozen, Yu Wu, Xiaoyu Cao, Richard Shin, Dawn Song, Hongxia Jin, and Xuan Bao. "PIANO: Proximity-based User Authentication on Voice-Powered Internet-of-Things Devices." In IEEE International Conference on Distributed Computing Systems (ICDCS), short paper, 2017.

• Binghui Wang, Le Zhang, and Neil Zhenqiang Gong. "SybilSCAR: Sybil Detection in Online Social Networks via Local Rule based Propagation". In IEEE International Conference on Computer Communications (INFOCOM), 2017.
  Fast tracked to IEEE TNSE
  Only 10 papers were selected for fast tracking
  Code and data are available [here]

• Jinyuan Jia, Binghui Wang, Le Zhang, and Neil Zhenqiang Gong. "AttriInfer: Inferring User Attributes in Online Social Networks Using Markov Random Fields. In International World Wide Web Conference (WWW), 2017.

• Guolei Yang, Neil Zhenqiang Gong, and Ying Cai. "Fake Co-visitation Injection Attacks to Recommender Systems". In ISOC Network and Distributed System Security Symposium (NDSS), 2017.

2016

• Bin Liu, Yao Wu, Neil Zhenqiang Gong, Junjie Wu, Hui Xiong, and Martin Ester. "Structural Analysis of User Choices for Mobile App Recommendation". ACM Transactions on Knowledge Discovery from Data (TKDD), 11(2), 2016.

• Neil Zhenqiang Gong and Bin Liu. "You are Who You Know and How You Behave: Attribute Inference Attacks via Users' Social Friends and Behaviors". In USENIX Security Symposium, 2016.

• Shouling Ji, Weiqing Li, Neil Zhenqiang Gong, Prateek Mittal, and Raheem Beyah. "Seed-based De-anonymizability Quantification of Social Networks". IEEE Transactions on Information Forensics and Security (TIFS), 11(7), 2016.

• Neil Zhenqiang Gong, Mathias Payer, Reza Moazzezi, and Mario Frank. "Forgery-Resistant Touch-based Authentication on Mobile Devices". In ACM Asia Conference on Computer and Communications Security (ASIACCS), 2016.

2015

• Bing Hu, Bin Liu, Neil Zhenqiang Gong, Deguang Kong, and Hongxia Jin. "Protecting Your Children from Inappropriate Content in Mobile Apps: An Automatic Maturity Rating Framework". In ACM International Conference on Information and Knowledge Management (CIKM), 2015.

• Bin Liu, Deguang Kong, Lei Cen, Neil Zhenqiang Gong, Hongxia Jin, and Hui Xiong. "Personalized Mobile App Recommendation: Reconciling App Functionality and User Privacy Preference. In ACM International Conference on Web Search and Data Mining (WSDM), 2015.

• Shouling Ji, Weiqing Li, Neil Zhenqiang Gong, Prateek Mittal, and Raheem Beyah. "On Your Social Network De-anonymizablity: Quantification and Large Scale Evaluation with Seed Knowledge". In ISOC Network and Distributed System Security Symposium (NDSS), 2015.

• Sakshi Jain, Neil Zhenqiang Gong, Sreya Basuroy, Juan Lang, Dawn Song, and Prateek Mittal. "New Directions in Social Authentication". In Workshop on Usable Security (USEC), co-located with NDSS, 2015

• Mathias Payer, Ling Huang, Neil Zhenqiang Gong, Kevin Borgolte, and Mario Frank. "What You Submit is Who You Are: A Multi-Modal Approach for Deanonymizing Scientific Publications". IEEE Transactions on Information Forensics and Security (TIFS), 10(1), 2015.

2014

• Xuan Bao, Yilin Shen, Neil Zhenqiang Gong, Hongxia Jin, and Bing Hu. "Connect the Dots by Understanding User Status and Transitions". In UbiComp/ISWC Programming Competition, co-located with UbiComp, 2014.

• Neil Zhenqiang Gong and Di Wang. "On the Security of Trustee-based Social Authentications". IEEE Transactions on Information Forensics and Security (TIFS), 9(8), 2014.
  Featured in VentureBeat.

• Neil Zhenqiang Gong, Mario Frank, and Prateek Mittal. "SybilBelief: A Semi-supervised Learning Approach for Structure-based Sybil Detection". IEEE Transactions on Information Forensics and Security (TIFS), 9(6), 2014.
  Code and data are available [here].

• Neil Zhenqiang Gong and Wenchang Xu. "Reciprocal versus Parasocial Relationships in Online Social Networks". Springer Social Network Analysis and Mining (SNAM), 4(1), 2014.

• Neil Zhenqiang Gong, Ameet Talwalkar, Lester Mackey, Ling Huang, Richard Shin, Emil Stefanov, Elaine Shi, and Dawn Song. "Joint Link Prediction and Attribute Inference using a Social-Attribute Network". ACM Transactions on Intelligent Systems and Technology (TIST), 5(2), 2014.

2012

• Neil Zhenqiang Gong, Wenchang Xu, Ling Huang, Prateek Mittal, Emil Stefanov, Vyas Sekar, and Dawn Song. "Evolution of Social-Attribute Networks: Measurements, Modeling, and Implications using Google+". In ACM/USENIX Internet Measurement Conference (IMC), 2012.
  Our Google+ dataset is available [here].
• Neil Zhenqiang Gong, Ameet Talwalkar, Lester Mackey, Ling Huang, Richard Shin, Emil Stefanov, Elaine Shi, and Dawn Song. "Jointly Predicting Links and Inferring Attributes using a Social-Attribute Network (SAN)". In ACM Workshop on Social Network Mining and Analysis (SNA-KDD), co-located with KDD, 2012.
• Arvind Narayanan, Hristo Paskov, Neil Zhenqiang Gong, John Bethencourt, Richard Shin, Emil Stefanov, and Dawn Song. "On the Feasibility of Internet-Scale Author Identification". In IEEE Symposium on Security and Privacy (IEEE S&P), 2012.
  Featured in WNYC Studios, Boing Boing, Slashdot, etc.
• Zhenqiang Gong, Nick Matzke, Bard Ermentrout, Dawn Song, Jann E. Vendetti, Montgomery Slatkin, and George Oster. "The evolution of patterns on Conus shells". Proceedings of the National Academy of Sciences (PNAS), 109(5), 2012.
  Featured as a breakthrough by WIRED, ScienceDaily, PhysOrg, etc.
  Selected as ''The Best Scientific Figures in 2012'' by WIRED. Only 21 papers out of all scientific publications in 2012 were selected.

Before 2012 (work done during my undergraduate study)

• Dongqu Chen, Guang-Zhong Sun, and Neil Zhenqiang Gong. "Efficient Approximate Top-k Query Algorithm Using Cube Index". In Asia-Pacific Web Conference (APWeb), 2011.

• Zhenqiang Gong. "Parallel Algorithms for Top-k Query Processing". ACM SIGMOD International Conference on Management of Data (SIGMOD), poster, 2010.

• Zhenqiang Gong, Guangzhong Sun, and Xing Xie. "Protecting Privacy in Location-based Services Using K-anonymity without Cloaked Region". In IEEE International Conference on Mobile Data Management (MDM) Workshops, 2010.

• Zhenqiang Gong, Guangzhong Sun, Jing Yuan, and Yanjing Zhong. "Efficient Top-k Query Algorithms Using K-Skyband Partition". In International ICST Conference on Scalable Information Systems (INFOSCALE), 2009.