Neil Gong

Research Interests

Cybersecurity and trustworthy AI

Bio

Neil Gong is interested in cybersecurity and trustworthy AI. He received a B.E. from the University of Science and Technology of China (USTC) in 2010 (with the highest honor) and a Ph.D in Computer Science from the University of California, Berkeley in 2015. He has received NSF CAREER Award (2018), Army Research Office Young Investigator Program (YIP) Award (2021), Rising Star Award from the Association of Chinese Scholars in Computing (2020), IBM Faculty Award (2020, 2023), Facebook Research Award (2021), and multiple best paper or best paper honorable mention awards.

Education

• B.S.E. University of Science and Technology of China (China), 2010
• D.Phil. University of California, Berkeley, 2015

Positions

• Associate Professor of Electrical and Computer Engineering
• Assistant Professor of Computer Science

Courses Taught

• ECE 899: Special Readings in Electrical Engineering
• ECE 891: Internship
• ECE 663: Machine Learning in Adversarial Settings
• ECE 590: Advanced Topics in Electrical and Computer Engineering
• ECE 560: Computer and Information Security
• ECE 356: Computer Network Architecture
• COMPSCI 356: Computer Network Architecture

Publications

• Jia J, Gong NZ. 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 B, Jia J, Gong NZ. 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.
• Gong NZ, Liu B. Attribute Inference Attacks in Online Social Networks. ACM Transactions on Privacy and Security. 2018 Feb 28;21(1):1–30.
• Jia J, Gong NZ. AttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine Learning. In: USENIX Security Symposium. 2018.
• Jia J, Wang B, Zhang L, Gong NZ. 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 G, Gong NZ, Cai Y. Fake Co-visitation Injection Attacks to Recommender Systems. In: ISOC Network and Distributed System Security Symposium (NDSS). 2017.
• Gong NZ, Liu B. You are Who You Know and How You Behave: Attribute Inference Attacks via Users' Social Friends and Behaviors. In: USENIX Security Symposium. 2016.
• Ji S, Li W, Gong NZ, Mittal P, Beyah R. 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 NZ, Talwalkar A, Mackey L, Huang L, Shin ECR, Stefanov E, et al. Joint Link Prediction and Attribute Inference Using a Social-Attribute Network. ACM Transactions on Intelligent Systems and Technology. 2014 Apr;5(2):1–20.
• Gong Z, Matzke NJ, Ermentrout B, Song D, Vendetti JE, Slatkin M, et al. Evolution of patterns on Conus shells. Proceedings of the National Academy of Sciences. 2012 Jan 31;109(5).
• Gong NZ, Xu W, Huang L, Mittal P, Stefanov E, Sekar V, et al. 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. In.
• GANG: Detecting Fraudulent Users in Online Social Networks via Guilt-by-Association on Directed Graphs. In.
• On the Feasibility of Internet-Scale Author Identification. In.
• PIANO: Proximity-Based User Authentication on Voice-Powered Internet-of-Things Devices. In.
• SybilSCAR: Sybil detection in online social networks via local rule based propagation. In.
• SybilBelief: A Semi-Supervised Learning Approach for Structure-Based Sybil Detection. In.
• Random Walk Based Fake Account Detection in Online Social Networks. In.
• EviHunter: Identifying Digital Evidence in the Permanent Storage of Android Devices via Static Analysis. In.
• Poisoning Attacks to Graph-Based Recommender Systems. In.
• Stealing Hyperparameters in Machine Learning. In.
• Mitigating Evasion Attacks to Deep Neural Networks via Region-based Classification. In.

In The News

• AI is the Biggest Cybersecurity Question Mark. Is It Also the Answer? (Nov 12, 2024 | Pratt School of Engineering)
• New Faculty, Bold Thinking (Oct 15, 2019 | Duke Stories)