Data transformation and migration in polystores. - Adam Dziedzic; Aaron J. Elmore; Michael Stonebraker (HPEC, 2016)

Integrating real-time and batch processing in a polystore. - John Meehan; Stan Zdonik; Shaobo Tian; Yulong Tian; Nesime Tatbul; Adam Dziedzic; Aaron J. Elmore (HPEC, 2016)

DBMS Data Loading: An Analysis on Modern Hardware. - Adam Dziedzic; Manos Karpathiotakis; Ioannis Alagiannis; Raja Appuswamy; Anastasia Ailamaki (ADMS/IMDM@VLDB, 2016)

Demonstrating the BigDAWG Polystore System for Ocean Metagenomics Analysis. - Tim Mattson; Vijay Gadepally; Zuohao She; Adam Dziedzic; Jeff Parkhurst (CIDR, 2017)

BigDAWG version 0.1. - Vijay Gadepally; Kyle O'Brien; Adam Dziedzic; Aaron J. Elmore; Jeremy Kepner; Samuel Madden 001; Tim Mattson; Jennie Rogers; Zuohao She; Michael Stonebraker (HPEC, 2017)

Version 0.1 of the BigDAWG Polystore System. - Vijay Gadepally; Kyle O'Brien; Adam Dziedzic; Aaron J. Elmore; Jeremy Kepner; Samuel Madden 001; Tim Mattson; Jennie Rogers; Zuohao She; Michael Stonebraker (CoRR, 2017)

BigDAWG Polystore Release and Demonstration. - Kyle O'Brien; Vijay Gadepally; Jennie Duggan; Adam Dziedzic; Aaron J. Elmore; Jeremy Kepner; Samuel Madden 001; Tim Mattson; Zuohao She; Michael Stonebraker (CoRR, 2017)

Columnstore and B+ tree - Are Hybrid Physical Designs Important? - Adam Dziedzic; Jingjing Wang 008; Sudipto Das; Bolin Ding; Vivek R. Narasayya; Manoj Syamala (SIGMOD Conference, 2018)

DeepLens: Towards a Visual Data Management System. - Sanjay Krishnan; Adam Dziedzic; Aaron J. Elmore (CIDR, 2019)

Band-limited Training and Inference for Convolutional Neural Networks. - Adam Dziedzic; John Paparrizos; Sanjay Krishnan; Aaron J. Elmore; Michael J. Franklin (ICML, 2019)

Artificial Intelligence in Resource-Constrained and Shared Environments. - Sanjay Krishnan; Aaron J. Elmore; Michael J. Franklin; John Paparrizos; Zechao Shang; Adam Dziedzic; Rui Liu 002 (ACM SIGOPS Oper. Syst. Rev., 2019)

Pretrained Transformers Improve Out-of-Distribution Robustness. - Dan Hendrycks; Xiaoyuan Liu; Eric Wallace; Adam Dziedzic; Rishabh Krishnan; Dawn Song (ACL, 2020)

Machine Learning based detection of multiple Wi-Fi BSSs for LTE-U CSAT. - Vanlin Sathya; Adam Dziedzic; Monisha Ghosh; Sanjay Krishnan (ICNC, 2020)

An Empirical Evaluation of Perturbation-based Defenses. - Adam Dziedzic; Sanjay Krishnan (CoRR, 2020)

Machine Learning enabled Spectrum Sharing in Dense LTE-U/Wi-Fi Coexistence Scenarios. - Adam Dziedzic; Vanlin Sathya; Muhammad Iqbal Rochman; Monisha Ghosh; Sanjay Krishnan (CoRR, 2020)

CaPC Learning: Confidential and Private Collaborative Learning. - Christopher A. Choquette-Choo; Natalie Dullerud; Adam Dziedzic; Yunxiang Zhang 003; Somesh Jha; Nicolas Papernot; Xiao Wang 012 (ICLR, 2021)

On the Exploitability of Audio Machine Learning Pipelines to Surreptitious Adversarial Examples. - Adelin Travers; Lorna Licollari; Guanghan Wang; Varun Chandrasekaran; Adam Dziedzic; David Lie; Nicolas Papernot (CoRR, 2021)

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

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

Dataset Inference for Self-Supervised Models. - Adam Dziedzic; Haonan Duan 002; Muhammad Ahmad Kaleem; Nikita Dhawan; Jonas Guan; Yannis Cattan; Franziska Boenisch; Nicolas Papernot (NeurIPS, 2022)

Selective Classification Via Neural Network Training Dynamics. - Stephan Rabanser; Anvith Thudi; Kimia Hamidieh; Adam Dziedzic; Nicolas Papernot (CoRR, 2022)

p-DkNN: Out-of-Distribution Detection Through Statistical Testing of Deep Representations. - Adam Dziedzic; Stephan Rabanser; Mohammad Yaghini; Armin Ale; Murat A. Erdogdu; Nicolas Papernot (CoRR, 2022)

When the Curious Abandon Honesty: Federated Learning Is Not Private. - Franziska Boenisch; Adam Dziedzic; Roei Schuster; Ali Shahin Shamsabadi; Ilia Shumailov; Nicolas Papernot (EuroS&P, 2023)

Reconstructing Individual Data Points in Federated Learning Hardened with Differential Privacy and Secure Aggregation. - Franziska Boenisch; Adam Dziedzic; Roei Schuster; Ali Shahin Shamsabadi; Ilia Shumailov; Nicolas Papernot (EuroS&P, 2023)

Have it your way: Individualized Privacy Assignment for DP-SGD. - Franziska Boenisch; Christopher Mühl; Adam Dziedzic; Roy Rinberg; Nicolas Papernot (NeurIPS, 2023)

Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models. - Haonan Duan 002; Adam Dziedzic; Nicolas Papernot; Franziska Boenisch (NeurIPS, 2023)

Bucks for Buckets (B4B): Active Defenses Against Stealing Encoders. - Jan Dubinski; Stanislaw Pawlak; Franziska Boenisch; Tomasz Trzcinski; Adam Dziedzic (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 012 (NeurIPS, 2023)

Is Federated Learning a Practical PET Yet? - Franziska Boenisch; Adam Dziedzic; Roei Schuster; Ali Shahin Shamsabadi; Ilia Shumailov; Nicolas Papernot (CoRR, 2023)

Robust and Actively Secure Serverless Collaborative Learning. - Olive Franzese; Adam Dziedzic; Christopher A. Choquette-Choo; Mark R. Thomas; Muhammad Ahmad Kaleem; Stephan Rabanser; Congyu Fang; Somesh Jha; Nicolas Papernot; Xiao Wang 012 (CoRR, 2023)

Individualized PATE: Differentially Private Machine Learning with Individual Privacy Guarantees. - Franziska Boenisch; Christopher Mühl; Roy Rinberg; Jannis Ihrig; Adam Dziedzic (Proc. Priv. Enhancing Technol., 2023)

Private Multi-Winner Voting for Machine Learning. - Adam Dziedzic; Christopher A. Choquette-Choo; Natalie Dullerud; Vinith M. Suriyakumar; Ali Shahin Shamsabadi; Muhammad Ahmad Kaleem; Somesh Jha; Nicolas Papernot; Xiao Wang 012 (Proc. Priv. Enhancing Technol., 2023)

Efficient Model-Stealing Attacks Against Inductive Graph Neural Networks. - Marcin Podhajski; Jan Dubinski; Franziska Boenisch; Adam Dziedzic; Agnieszka Pregowska; Tomasz P. Michalak (ECAI, 2024)

Memorization in Self-Supervised Learning Improves Downstream Generalization. - Wenhao Wang; Muhammad Ahmad Kaleem; Adam Dziedzic; Michael Backes 001; Nicolas Papernot; Franziska Boenisch (ICLR, 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 001; Adam Dziedzic (NeurIPS, 2024)

Finding NeMo: Localizing Neurons Responsible For Memorization in Diffusion Models. - Dominik Hintersdorf; Lukas Struppek; Kristian Kersting; Adam Dziedzic; Franziska Boenisch (NeurIPS, 2024)

LLM Dataset Inference: Did you train on my dataset? - Pratyush Maini; Hengrui Jia 001; Nicolas Papernot; Adam Dziedzic (NeurIPS, 2024)

Localizing Memorization in SSL Vision Encoders. - Wenhao Wang; Adam Dziedzic; Michael Backes 001; Franziska Boenisch (NeurIPS, 2024)

Decentralised, Collaborative, and Privacy-preserving Machine Learning for Multi-Hospital Data. - Congyu Fang; Adam Dziedzic; Lin Zhang; Laura Oliva; Amol Verma; Fahad Razak; Nicolas Papernot; Bo Wang (CoRR, 2024)

Alignment Calibration: Machine Unlearning for Contrastive Learning under Auditing. - Yihan Wang; Yiwei Lu 001; Guojun Zhang; Franziska Boenisch; Adam Dziedzic; Yaoliang Yu; Xiao-Shan Gao (CoRR, 2024)

Beyond the Mean: Differentially Private Prototypes for Private Transfer Learning. - Dariush Wahdany; Matthew Jagielski; Adam Dziedzic; Franziska Boenisch (CoRR, 2024)

Benchmarking Robust Self-Supervised Learning Across Diverse Downstream Tasks. - Antoni Kowalczuk; Jan Dubinski; Atiyeh Ashari Ghomi; Yi Sui 001; George Stein; Jiapeng Wu; Jesse C. Cresswell; Franziska Boenisch; Adam Dziedzic (CoRR, 2024)

On the Privacy Risk of In-context Learning. - Haonan Duan 002; Adam Dziedzic; Mohammad Yaghini; Nicolas Papernot; Franziska Boenisch (CoRR, 2024)

Differentially Private Prototypes for Imbalanced Transfer Learning. - Dariush Wahdany; Matthew Jagielski; Adam Dziedzic; Franziska Boenisch (AAAI, 2025)

CDI: Copyrighted Data Identification in Diffusion Models. - Jan Dubinski; Antoni Kowalczuk; Franziska Boenisch; Adam Dziedzic (CVPR, 2025)

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

Precise Parameter Localization for Textual Generation in Diffusion Models. - Lukasz Staniszewski; Bartosz Cywinski; Franziska Boenisch; Kamil Deja; Adam Dziedzic (ICLR, 2025)

Captured by Captions: On Memorization and its Mitigation in CLIP Models. - Wenhao Wang; Adam Dziedzic; Grace C. Kim; Michael Backes 001; Franziska Boenisch (ICLR, 2025)

Secure Noise Sampling for Differentially Private Collaborative Learning. - Olive Franzese; Congyu Fang; Radhika Garg 002; Somesh Jha; Nicolas Papernot; Xiao Wang 012; Adam Dziedzic (IACR Cryptol. ePrint Arch., 2025)

Selective Prediction via Training Dynamics. - Stephan Rabanser; Anvith Thudi; Kimia Hamidieh; Adam Dziedzic; Israfil Bahceci; Akram Bin Sediq; Hamza Umit Sokun; Nicolas Papernot (Trans. Mach. Learn. Res., 2025)

MUC: Machine Unlearning for Contrastive Learning with Black-box Evaluation. - Yihan Wang; Yiwei Lu 001; Guojun Zhang; Franziska Boenisch; Adam Dziedzic; Yaoliang Yu; Xiao-Shan Gao (Trans. Mach. Learn. Res., 2025)

Privacy Attacks on Image AutoRegressive Models. - Antoni Kowalczuk; Jan Dubinski; Franziska Boenisch; Adam Dziedzic (CoRR, 2025)

Beautiful Images, Toxic Words: Understanding and Addressing Offensive Text in Generated Images. - Aditya Kumar; Tom Blanchard; Adam Dziedzic; Franziska Boenisch (CoRR, 2025)

Strong Membership Inference Attacks on Massive Datasets and (Moderately) Large Language Models. - Jamie Hayes; Ilia Shumailov; Christopher A. Choquette-Choo; Matthew Jagielski; Georgios Kaissis; Katherine Lee; Milad Nasr; Sahra Ghalebikesabi; Niloofar Mireshghallah; Meenatchi Sundaram Mutu Selva Annamalai; Igor Shilov; Matthieu Meeus; et al. (CoRR, 2025)

Unlocking Post-hoc Dataset Inference with Synthetic Data. - Bihe Zhao; Pratyush Maini; Franziska Boenisch; Adam Dziedzic (CoRR, 2025)

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs. - Xun Wang; Jing Xu; Franziska Boenisch; Michael Backes 001; Christopher A. Choquette-Choo; Adam Dziedzic (CoRR, 2025)

BitMark for Infinity: Watermarking Bitwise Autoregressive Image Generative Models. - Louis Kerner; Michel Meintz; Bihe Zhao; Franziska Boenisch; Adam Dziedzic (CoRR, 2025)

Radioactive Watermarks in Diffusion and Autoregressive Image Generative Models. - Michel Meintz; Jan Dubinski; Franziska Boenisch; Adam Dziedzic (CoRR, 2025)

Implementing Adaptations for Vision AutoRegressive Model. - Kaif Shaikh; Franziska Boenisch; Adam Dziedzic (CoRR, 2025)

Finding Dori: Memorization in Text-to-Image Diffusion Models Is Less Local Than Assumed. - Antoni Kowalczuk; Dominik Hintersdorf; Lukas Struppek; Kristian Kersting; Adam Dziedzic; Franziska Boenisch (CoRR, 2025)

Adversarial Attacks and Defenses on Graph-aware Large Language Models (LLMs). - Iyiola E. Olatunji; Franziska Boenisch; Jing Xu; Adam Dziedzic (CoRR, 2025)

Demystifying Foreground-Background Memorization in Diffusion Models. - Jimmy Z. Di; Yiwei Lu 001; Yaoliang Yu; Gautam Kamath 001; Adam Dziedzic; Franziska Boenisch (CoRR, 2025)