Alina Oprea

Professor

Alina Oprea
  • Security analytics
  • Cloud security
  • Network security
  • Applied cryptography
  • PhD in Computer Science, Carnegie Mellon University
  • MS in Computer Science, Carnegie Mellon University
  • BS in Mathematics and Computer Science, University of Bucharest — Romania

Alina Oprea is a professor in the Khoury College of Computer Sciences at Northeastern University, based in Boston.

Oprea is interested in extracting meaningful intelligence from different data sources for security applications. By designing rigorous machine learning techniques to predict the behavior of sophisticated attackers, she hopes to protect cloud infrastructures against emerging threats. Oprea co-directs the Network and Distributed Systems Security Lab, which focuses on building distributed systems and network protocols that achieve security, availability, and performance.

Before joining Khoury College, Oprea was a research scientist at RSA Laboratories, where she studied cloud security, applied cryptography, foundations of cybersecurity, and security analytics. 

As the co-author of numerous journal and peer-review conference papers, Oprea has participated in many technical program committees — including IEEE S&P, NDSS, ACM CCS, ACSAC, and DSN — and is a co-inventor on 20 patents. She is an associate editor for the ACM Transactions on Privacy and Security journal. At the 2005 Network and Distributed System Security Conference, Oprea earned the Best Paper Award, and in 2011, she received the Technology Review TR35 award for her research in cloud security.

  • User Inference Attacks on Large Language Models

    Citation: Nikhil Kandpal, Krishna Pillutla, Alina Oprea, Peter Kairouz, Christopher A. Choquette-Choo, Zheng Xu . (2024). User Inference Attacks on Large Language Models EMNLP, 18238-18265. https://aclanthology.org/2024.emnlp-main.1014

  • SNAP: Efficient Extraction of Private Properties with Poisoning

    Citation: Harsh Chaudhari, John Abascal, Alina Oprea, Matthew Jagielski, Florian Tramèr, Jonathan R. Ullman. (2023). SNAP: Efficient Extraction of Private Properties with Poisoning SP, 400-417. https://doi.org/10.1109/SP46215.2023.10179334

  • One-shot Empirical Privacy Estimation for Federated Learning

    Citation: Galen Andrew, Peter Kairouz, Sewoong Oh, Alina Oprea, H. Brendan McMahan, Vinith Suriyakumar. (2023). One-shot Empirical Privacy Estimation for Federated Learning CoRR, abs/2302.03098. https://doi.org/10.48550/arXiv.2302.03098

  • Backdoor Attacks in Peer-to-Peer Federated Learning

    Citation: Gökberk Yar, Cristina Nita-Rotaru, Alina Oprea. (2023). Backdoor Attacks in Peer-to-Peer Federated Learning CoRR, abs/2301.09732. https://doi.org/10.48550/arXiv.2301.09732

  • Machine Learning Security and Privacy

    Citation: Nathalie Baracaldo, Alina Oprea. (2022). Machine Learning Security and Privacy IEEE Secur. Priv., 20, 11-13. https://doi.org/10.1109/MSEC.2022.3188190

  • Subpopulation Data Poisoning Attacks

    Citation: Matthew Jagielski, Giorgio Severi, Niklas Pousette Harger, Alina Oprea. (2021). Subpopulation Data Poisoning Attacks CCS, 3104-3122. https://doi.org/10.1145/3460120.3485368

  • Differentially Private Fair Learning

    Citation: Jagielski, Matthew, Kearns, Michael, Mao, Jieming, Oprea, Alina, Roth, Aaron, Sharifi, Saeed, & Ullman, Jonathan. (2019). Differentially Private Fair Learning. Proceedings of the 36 Th International Conference on Machine Learning.

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Previous PhD Students

  • Talha Ongun