Neil Gong | Scholars@Duke

Neil Gong
Assistant Professor of Electrical and Computer Engineering

Neil Gong is interested in cybersecurity and data privacy with a recent focus on the intersections between security, privacy, and machine learning. On one hand, he leverages machine learning techniques to study security and privacy. On the other hand, he builds secure and privacy-preserving machine learning. He has received an NSF CAREER Award, an Army Research Office (ARO) Young Investigator Award, an IBM Faculty Award, and an NDSS Distinguished Paper Award Honorable Mention. He received a B.E. from the University of Science and Technology of China in 2010 (with the highest honor) and a Ph.D. in Computer Science from the University of California at Berkeley in 2015. During 2015-2019, he was an Assistant Professor in the Department of Electrical and Computer Engineering at Iowa State University.

Current Research Interests

Security, privacy, and machine learning.

Current Appointments & Affiliations

Assistant Professor of Electrical and Computer Engineering, Electrical and Computer Engineering, Pratt School of Engineering 2019
Assistant Professor of Computer Science, Computer Science, Trinity College of Arts & Sciences 2019

Contact Information

Box 90291, Durhamn, NC 27708
neil.gong@duke.edu (919) 660-5252
Gong Research Group

Background
Education, Training, & Certifications
- D.Phil., University of California - Berkeley 2015
- B.S.E., University of Science and Technology of China (China) 2010
Recognition
In the News
- OCT 15, 2019 Duke Stories
  
  New Faculty, Bold Thinking
Awards & Honors
Expertise
Subject Headings
Research
Selected Grants
- Collaborative Research: SaTC: CORE: Medium: Towards Secure Federated Learning awarded by National Science Foundation 2022 - 2026
- Collaborative Research: SaTC: CORE:Small: Securing Recommender Systems against Data Poisoning Attacks awarded by National Science Foundation 2022 - 2024
- Provably Secure Machine Learning awarded by Army Research Office 2021 - 2024
- SaTC: Core: Medium: Collaborative: Towards Building Robust Machine Learning Systems awarded by National Science Foundation 2019 - 2023
- CAREER: Graph-Based Security Analytics: New Algorithms, Robustness under Adversarial Settings, and Robustness Enhancements awarded by National Science Foundation 2019 - 2023
External Relationships
- CISPA Helmholtz Center for Information Security
- Johns Hopkins University
- National Taiwan University
- The Ohio State University
- Tsinghua University
- Zhejiang University
Publications & Artistic Works
Selected Publications
- Academic Articles
  - Gong, Neil Zhenqiang, and Bin Liu. “Attribute Inference Attacks in Online Social Networks.” Acm Transactions on Privacy and Security 21, no. 1 (January 6, 2018): 1–30. https://doi.org/10.1145/3154793.
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  - Gong, Neil Zhenqiang, Ameet Talwalkar, Lester Mackey, Ling Huang, Eui Chul Richard Shin, Emil Stefanov, Elaine Runting Shi, and Dawn Song. “Joint Link Prediction and Attribute Inference Using a Social-Attribute Network.” Acm Transactions on Intelligent Systems and Technology 5, no. 2 (April 2014): 1–20. https://doi.org/10.1145/2594455.
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  - Gong, Zhenqiang, Nichilos J. Matzke, Bard Ermentrout, Dawn Song, Jann E. Vendetti, Montgomery Slatkin, and George Oster. “Evolution of patterns on Conus shells.” Proceedings of the National Academy of Sciences 109, no. 5 (January 31, 2012). https://doi.org/10.1073/pnas.1119859109.
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- Conference Papers
  - Jia, Jinyuan, 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.
  - Wang, Binghui, 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.
  - Jia, Jinyuan, and Neil Zhenqiang Gong. “AttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine Learning.” In Usenix Security Symposium, 2018.
  - Jia, Jinyuan, Binghui Wang, Le Zhang, and Neil Zhenqiang Gong. “AttriInfer: Inferring User Attributes in Online Social Networks Using Markov Random Fields.” In Proceedings of the 26th International Conference on World Wide Web, 2017.
  - Yang, Guolei, Neil Zhenqiang Gong, and Ying Cai. “Fake Co-visitation Injection Attacks to Recommender Systems.” In Isoc Network and Distributed System Security Symposium (Ndss), 2017.
  - Gong, Neil Zhenqiang, 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.
  - Ji, Shouling, 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.
  - Gong, Neil Zhenqiang, 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.
  - “Personalized Mobile App Recommendation: Reconciling App Functionality and User Privacy Preference,” n.d. https://doi.org/10.1145/2684822.2685322.
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  - “Mitigating Evasion Attacks to Deep Neural Networks via Region-based Classification,” n.d. https://doi.org/10.1145/3134600.3134606.
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  - “EviHunter: Identifying Digital Evidence in the Permanent Storage of Android Devices via Static Analysis,” n.d. https://doi.org/10.1145/3243734.3243808.
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  - “Poisoning Attacks to Graph-Based Recommender Systems,” n.d. https://doi.org/10.1145/3274694.3274706.
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  - “On the Feasibility of Internet-Scale Author Identification,” n.d. https://doi.org/10.1109/SP.2012.46.
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  - “SybilBelief: A Semi-Supervised Learning Approach for Structure-Based Sybil Detection,” n.d. https://doi.org/10.1109/TIFS.2014.2316975.
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  - “SybilSCAR: Sybil detection in online social networks via local rule based propagation,” n.d. https://doi.org/10.1109/INFOCOM.2017.8057066.
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  - “PIANO: Proximity-Based User Authentication on Voice-Powered Internet-of-Things Devices,” n.d. https://doi.org/10.1109/ICDCS.2017.68.
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  - “Random Walk Based Fake Account Detection in Online Social Networks,” n.d. https://doi.org/10.1109/DSN.2017.55.
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  - “GANG: Detecting Fraudulent Users in Online Social Networks via Guilt-by-Association on Directed Graphs,” n.d. https://doi.org/10.1109/ICDM.2017.56.
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  - “Stealing Hyperparameters in Machine Learning,” n.d. https://doi.org/10.1109/SP.2018.00038.
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Teaching & Mentoring
Recent Courses

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Education, Training, & Certifications

In the News

Awards & Honors

Subject Headings

Selected Grants

External Relationships

Selected Publications

Academic Articles

Conference Papers

Recent Courses