Faculty
Adam Dziedzic is a tenure-track faculty member at CISPA, where he co-leads the SprintML group with a research focus on Secure, Private, Robust, Interpretable, and Trustworthy Machine Learning. Before joining CISPA, he was a Postdoctoral Fellow at the Vector Institute and the University of Toronto, a member of the CleverHans Lab, advised by Prof. Nicolas Papernot. He earned his PhD in computer science at the University of Chicago, where he was advised by Prof. Sanjay Krishnan and worked on input and model compression for adaptive and robust neural networks.
International Conference on Learning Representations (ICLR)
Annual Meeting of the Association for Computational Linguistics (ACL) On the Privacy Risk of In-context Learning
Privacy Enhancing Technologies Symposium (PETS) Individualized PATE: Differentially Private Machine Learning with Individual Privacy Guarantees.
IEEE European Symposium on Security and Privacy (EuroS&P) Reconstructing Individual Data Points in Federated Learning Hardened with Differential Privacy and Secure Aggregation
IEEE European Symposium on Security and Privacy (EuroS&P) When the Curious Abandon Honesty: Federated Learning Is Not Private
International Conference on Learning Representations (ICLR) Sentence Embedding Encoders are Easy to Steal but Hard to Defend
Conference on Neural Information Processing Systems (NeurIPS) Have it your way: Individualized Privacy Assignment for DP-SGD
International Conference on Machine Learning (ICML) On the Difficulty of Defending Self-Supervised Learning against Model Extraction.
International Conference on Learning Representations (ICLR) Stealing and Defending Transformer-based Encoders
International Conference on Learning Representations (ICLR) A Perturbation Analysis of Input Transformations for Adversarial Attacks