Dziedzic, Adam

49 publications

ICLR 2026 Benchmarking Empirical Privacy Protection for Adaptations of Large Language Models Bartłomiej Marek, Lorenzo Rossi, Vincent Hanke, Xun Wang, Michael Backes, Franziska Boenisch, Adam Dziedzic

ICLR 2026 Curation Leaks: Membership Inference Attacks Against Data Curation for Machine Learning Dariush Wahdany, Matthew Jagielski, Adam Dziedzic, Franziska Boenisch

ICLR 2026 Data Provenance for Image Auto-Regressive Generation Bihe Zhao, Louis Kerner, Michel Meintz, Tameem Bakr, Franziska Boenisch, Adam Dziedzic

ICLR 2026 Natural Identifiers for Privacy and Data Audits in Large Language Models Lorenzo Rossi, Bartłomiej Marek, Franziska Boenisch, Adam Dziedzic

ICLR 2026 SERUM: Simple, Efficient, Robust, and Unifying Marking for Diffusion-Based Image Generation Jan Kociszewski, Hubert Jastrzębski, Tymoteusz Stępkowski, Filip Manijak, Krzysztof Rojek, Franziska Boenisch, Adam Dziedzic

ICLRW 2025 Are Watermarks for Diffusion Models Radioactive? Jan Dubiński, Michel Meintz, Franziska Boenisch, Adam Dziedzic

ICLRW 2025 Beautiful Images, Toxic Words: Understanding and Addressing Offensive Text in Generated Images Aditya Kumar, Tom Blanchard, Adam Dziedzic, Franziska Boenisch

NeurIPS 2025 BitMark: Watermarking Bitwise Autoregressive Image Generative Models Louis Kerner, Michel Meintz, Bihe Zhao, Franziska Boenisch, Adam Dziedzic

CVPR 2025 CDI: Copyrighted Data Identification in Diffusion Models Jan Dubiński, Antoni Kowalczuk, Franziska Boenisch, Adam Dziedzic

ICLR 2025 Captured by Captions: On Memorization and Its Mitigation in CLIP Models Wenhao Wang, Adam Dziedzic, Grace C. Kim, Michael Backes, Franziska Boenisch

ICLRW 2025 Captured by Captions: On Memorization and Its Mitigation in CLIP Models Wenhao Wang, Adam Dziedzic, Grace C. Kim, Michael Backes, Franziska Boenisch

ICLRW 2025 DIET-PATE: Knowledge Transfer in PATE Without Public Data Michel Meintz, Adam Dziedzic, Franziska Boenisch

ICLR 2025 Differentially Private Federated Learning with Time-Adaptive Privacy Spending Shahrzad Kiani, Nupur Kulkarni, Adam Dziedzic, Stark Draper, Franziska Boenisch

AAAI 2025 Differentially Private Prototypes for Imbalanced Transfer Learning Dariush Wahdany, Matthew Jagielski, Adam Dziedzic, Franziska Boenisch

ICLRW 2025 Dp-Gpl: Differentially Private Graph Prompt Learning Jing Xu, Franziska Boenisch, Iyiola Emmanuel Olatunji, Adam Dziedzic

ICML 2025 Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Xun Wang, Jing Xu, Franziska Boenisch, Michael Backes, Christopher A. Choquette-Choo, Adam Dziedzic

NeurIPS 2025 Exploring the Limits of Strong Membership Inference Attacks on Large Language Models Jamie Hayes, Ilia Shumailov, Christopher A. Choquette-Choo, Matthew Jagielski, Georgios Kaissis, Milad Nasr, Meenatchi Sundaram Muthu Selva Annamalai, Niloofar Mireshghallah, Igor Shilov, Matthieu Meeus, Yves-Alexandre de Montjoye, Katherine Lee, Franziska Boenisch, Adam Dziedzic, A. Feder Cooper

TMLR 2025 MUC: Machine Unlearning for Contrastive Learning with Black-Box Evaluation Yihan Wang, Yiwei Lu, Guojun Zhang, Franziska Boenisch, Adam Dziedzic, Yaoliang Yu, Xiao-Shan Gao

NeurIPS 2025 Memorization in Graph Neural Networks Adarsh Jamadandi, Jing Xu, Adam Dziedzic, Franziska Boenisch

ICLR 2025 Precise Parameter Localization for Textual Generation in Diffusion Models Łukasz Staniszewski, Bartosz Cywiński, Franziska Boenisch, Kamil Deja, Adam Dziedzic

ICML 2025 Privacy Attacks on Image AutoRegressive Models Antoni Kowalczuk, Jan Dubiński, Franziska Boenisch, Adam Dziedzic

ICLRW 2025 Privacy Attacks on Image AutoRegressive Models Antoni Kowalczuk, Jan Dubiński, Franziska Boenisch, Adam Dziedzic

ICLRW 2025 Privacy Auditing for Large Language Models with Natural Identifiers Lorenzo Rossi, Bartłomiej Marek, Franziska Boenisch, Adam Dziedzic

TMLR 2025 Selective Prediction via Training Dynamics Stephan Rabanser, Anvith Thudi, Kimia Hamidieh, Adam Dziedzic, Israfil Bahceci, Akram Bin Sediq, Hamza Sokun, Nicolas Papernot

ICML 2025 Unlocking Post-Hoc Dataset Inference with Synthetic Data Bihe Zhao, Pratyush Maini, Franziska Boenisch, Adam Dziedzic

ICLRW 2025 Unlocking Post-Hoc Dataset Inference with Synthetic Data Bihe Zhao, Pratyush Maini, Franziska Boenisch, Adam Dziedzic

ICMLW 2024 Alignment Calibration: Machine Unlearning for Contrastive Learning Under Auditing Yihan Wang, Yiwei Lu, Guojun Zhang, Franziska Boenisch, Adam Dziedzic, Yaoliang Yu, Xiao-Shan Gao

NeurIPSW 2024 Auditing Empirical Privacy Protection of Private LLM Adaptations Lorenzo Rossi, Bartłomiej Marek, Vincent Hanke, Xun Wang, Michael Backes, Adam Dziedzic, Franziska Boenisch

ICMLW 2024 Benchmarking Robust Self-Supervised Learning Across Diverse Downstream Tasks Antoni Kowalczuk, Jan Dubiński, Atiyeh Ashari Ghomi, Yi Sui, George Stein, Jiapeng Wu, Jesse C. Cresswell, Franziska Boenisch, Adam Dziedzic

NeurIPS 2024 Finding NeMo: Localizing Neurons Responsible for Memorization in Diffusion Models Dominik Hintersdorf, Lukas Struppek, Kristian Kersting, Adam Dziedzic, Franziska Boenisch

ICMLW 2024 Finding NeMo: Localizing Neurons Responsible for Memorization in Diffusion Models Lukas Struppek, Dominik Hintersdorf, Kristian Kersting, Adam Dziedzic, Franziska Boenisch

NeurIPS 2024 LLM Dataset Inference: Did You Train on My Dataset? Pratyush Maini, Hengrui Jia, Nicolas Papernot, Adam Dziedzic

NeurIPS 2024 Localizing Memorization in SSL Vision Encoders Wenhao Wang, Adam Dziedzic, Michael Backes, Franziska Boenisch

ICLR 2024 Memorization in Self-Supervised Learning Improves Downstream Generalization Wenhao Wang, Muhammad Ahmad Kaleem, Adam Dziedzic, Michael Backes, Nicolas Papernot, Franziska Boenisch

NeurIPS 2024 Open LLMs Are Necessary for Current Private Adaptations and Outperform Their Closed Alternatives Vincent Hanke, Tom Blanchard, Franziska Boenisch, Iyiola E. Olatunji, Michael Backes, Adam Dziedzic

ICMLW 2024 Open LLMs Are Necessary for Private Adaptations and Outperform Their Closed Alternatives Vincent Hanke, Tom Blanchard, Franziska Boenisch, Iyiola Emmanuel Olatunji, Michael Backes, Adam Dziedzic

ICMLW 2024 Open LLMs Are Necessary for Private Adaptations and Outperform Their Closed Alternatives Vincent Hanke, Tom Blanchard, Franziska Boenisch, Iyiola Emmanuel Olatunji, Michael Backes, Adam Dziedzic

ICMLW 2024 POST: A Framework for Privacy of Soft-Prompt Transfer Xun Wang, Jing Xu, Franziska Boenisch, Michael Backes, Adam Dziedzic

ICMLW 2024 POST: A Framework for Privacy of Soft-Prompt Transfer Xun Wang, Jing Xu, Franziska Boenisch, Michael Backes, Adam Dziedzic

NeurIPS 2023 Bucks for Buckets (B4B): Active Defenses Against Stealing Encoders Jan Dubiński, Stanisław Pawlak, Franziska Boenisch, Tomasz Trzcinski, Adam Dziedzic

NeurIPS 2023 Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models Haonan Duan, Adam Dziedzic, Nicolas Papernot, Franziska Boenisch

NeurIPS 2023 Have It Your Way: Individualized Privacy Assignment for DP-SGD Franziska Boenisch, Christopher Mühl, Adam Dziedzic, Roy Rinberg, Nicolas Papernot

NeurIPS 2023 Robust and Actively Secure Serverless Collaborative Learning Nicholas Franzese, Adam Dziedzic, Christopher A. Choquette-Choo, Mark R Thomas, Muhammad Ahmad Kaleem, Stephan Rabanser, Congyu Fang, Somesh Jha, Nicolas Papernot, Xiao Wang

ICLRW 2023 Sentence Embedding Encoders Are Easy to Steal but Hard to Defend Adam Dziedzic, Franziska Boenisch, Mingjian Jiang, Haonan Duan, Nicolas Papernot

NeurIPS 2022 Dataset Inference for Self-Supervised Models Adam Dziedzic, Haonan Duan, Muhammad Ahmad Kaleem, Nikita Dhawan, Jonas Guan, Yannis Cattan, Franziska Boenisch, Nicolas Papernot

ICLR 2022 Increasing the Cost of Model Extraction with Calibrated Proof of Work Adam Dziedzic, Muhammad Ahmad Kaleem, Yu Shen Lu, Nicolas Papernot

ICML 2022 On the Difficulty of Defending Self-Supervised Learning Against Model Extraction Adam Dziedzic, Nikita Dhawan, Muhammad Ahmad Kaleem, Jonas Guan, Nicolas Papernot

ICLR 2021 CaPC Learning: Confidential and Private Collaborative Learning Christopher A. Choquette-Choo, Natalie Dullerud, Adam Dziedzic, Yunxiang Zhang, Somesh Jha, Nicolas Papernot, Xiao Wang

ICML 2019 Band-Limited Training and Inference for Convolutional Neural Networks Adam Dziedzic, John Paparrizos, Sanjay Krishnan, Aaron Elmore, Michael Franklin