Shredder: Learning Noise to Protect Privacy with Partial DNN Inference on the Edge. - Fatemehsadat Mireshghallah; Mohammadkazem Taram; Prakash Ramrakhyani; Dean M. Tullsen; Hadi Esmaeilzadeh (CoRR, 2019)

Energy-Efficient Permanent Fault Tolerance in Hard Real-Time Systems. - Fatemehsadat Mireshghallah; Mohammad Bakhshalipour; Mohammad Sadrosadati; Hamid Sarbazi-Azad (IEEE Trans. Computers, 2019)

Shredder: Learning Noise Distributions to Protect Inference Privacy. - Fatemehsadat Mireshghallah; Mohammadkazem Taram; Prakash Ramrakhyani; Ali Jalali; Dean M. Tullsen; Hadi Esmaeilzadeh (ASPLOS, 2020)

Neither Private Nor Fair: Impact of Data Imbalance on Utility and Fairness in Differential Privacy. - Tom Farrand; Fatemehsadat Mireshghallah; Sahib Singh; Andrew Trask (PPMLP@CCS, 2020)

Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks. - Ahmed Taha Elthakeb; Prannoy Pilligundla; Fatemeh Mireshghallah; Alexander Cloninger; Hadi Esmaeilzadeh (ICML, 2020)

Gradient-Based Deep Quantization of Neural Networks through Sinusoidal Adaptive Regularization. - Ahmed T. Elthakeb; Prannoy Pilligundla; Fatemehsadat Mireshghallah; Tarek Elgindi; Charles-Alban Deledalle; Hadi Esmaeilzadeh (CoRR, 2020)

A Principled Approach to Learning Stochastic Representations for Privacy in Deep Neural Inference. - Fatemehsadat Mireshghallah; Mohammadkazem Taram; Ali Jalali; Ahmed Taha Elthakeb; Dean M. Tullsen; Hadi Esmaeilzadeh (CoRR, 2020)

Privacy in Deep Learning: A Survey. - Fatemehsadat Mireshghallah; Mohammadkazem Taram; Praneeth Vepakomma; Abhishek Singh 005; Ramesh Raskar; Hadi Esmaeilzadeh (CoRR, 2020)

ReLeQ : A Reinforcement Learning Approach for Automatic Deep Quantization of Neural Networks. - Ahmed T. Elthakeb; Prannoy Pilligundla; Fatemehsadat Mireshghallah; Amir Yazdanbakhsh; Hadi Esmaeilzadeh (IEEE Micro, 2020)

Style Pooling: Automatic Text Style Obfuscation for Improved Classification Fairness. - Fatemehsadat Mireshghallah; Taylor Berg-Kirkpatrick (EMNLP (1), 2021)

U-Noise: Learnable Noise Masks for Interpretable Image Segmentation. - Teddy Koker; Fatemehsadat Mireshghallah; Tom Titcombe; Georgios Kaissis (ICIP, 2021)

Privacy Regularization: Joint Privacy-Utility Optimization in LanguageModels. - Fatemehsadat Mireshghallah; Huseyin A. Inan; Marcello Hasegawa; Victor Rühle; Taylor Berg-Kirkpatrick; Robert Sim (NAACL-HLT, 2021)

Not All Features Are Equal: Discovering Essential Features for Preserving Prediction Privacy. - Fatemehsadat Mireshghallah; Mohammadkazem Taram; Ali Jalali; Ahmed Taha Elthakeb; Dean M. Tullsen; Hadi Esmaeilzadeh (WWW, 2021)

Privacy Regularization: Joint Privacy-Utility Optimization in Language Models. - Fatemehsadat Mireshghallah; Huseyin A. Inan; Marcello Hasegawa; Victor Rühle; Taylor Berg-Kirkpatrick; Robert Sim (CoRR, 2021)

DP-SGD vs PATE: Which Has Less Disparate Impact on Model Accuracy? - Archit Uniyal; Rakshit Naidu; Sasikanth Kotti; Sahib Singh; Patrik Joslin Kenfack; Fatemehsadat Mireshghallah; Andrew Trask (CoRR, 2021)

When Differential Privacy Meets Interpretability: A Case Study. - Rakshit Naidu; Aman Priyanshu; Aadith Kumar; Sasikanth Kotti; Haofan Wang; Fatemehsadat Mireshghallah (CoRR, 2021)

Benchmarking Differential Privacy and Federated Learning for BERT Models. - Priyam Basu; Tiasa Singha Roy; Rakshit Naidu; Zümrüt Müftüoglu; Sahib Singh; Fatemehsadat Mireshghallah (CoRR, 2021)

Efficient Hyperparameter Optimization for Differentially Private Deep Learning. - Aman Priyanshu; Rakshit Naidu; Fatemehsadat Mireshghallah; Mohammad Malekzadeh (CoRR, 2021)

Mix and Match: Learning-free Controllable Text Generationusing Energy Language Models. - Fatemehsadat Mireshghallah; Kartik Goyal; Taylor Berg-Kirkpatrick (ACL (1), 2022)

Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks. - Fatemehsadat Mireshghallah; Kartik Goyal; Archit Uniyal; Taylor Berg-Kirkpatrick; Reza Shokri (EMNLP, 2022)

An Empirical Analysis of Memorization in Fine-tuned Autoregressive Language Models. - Fatemehsadat Mireshghallah; Archit Uniyal; Tianhao Wang 001; David E. Evans 001; Taylor Berg-Kirkpatrick (EMNLP, 2022)

What Does it Mean for a Language Model to Preserve Privacy? - Hannah Brown; Katherine Lee; Fatemehsadat Mireshghallah; Reza Shokri; Florian Tramèr (FAccT, 2022)

UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis. - Fatemehsadat Mireshghallah; Vaishnavi Shrivastava; Milad Shokouhi; Taylor Berg-Kirkpatrick; Robert Sim; Dimitrios Dimitriadis (NAACL-HLT, 2022)

Differentially Private Model Compression. - Fatemehsadat Mireshghallah; Arturs Backurs; Huseyin A. Inan; Lukas Wutschitz; Janardhan Kulkarni (NeurIPS, 2022)

Mix and Match: Learning-free Controllable Text Generation using Energy Language Models. - Fatemehsadat Mireshghallah; Kartik Goyal; Taylor Berg-Kirkpatrick (CoRR, 2022)

Memorization in NLP Fine-tuning Methods. - Fatemehsadat Mireshghallah; Archit Uniyal; Tianhao Wang 001; David Evans 001; Taylor Berg-Kirkpatrick (CoRR, 2022)

Non-Parametric Temporal Adaptation for Social Media Topic Classification. - Fatemehsadat Mireshghallah; Nikolai Vogler; Junxian He; Omar Florez; Ahmed El-Kishky; Taylor Berg-Kirkpatrick (CoRR, 2022)

Membership Inference Attacks against Language Models via Neighbourhood Comparison. - Justus Mattern; Fatemehsadat Mireshghallah; Zhijing Jin 001; Bernhard Schölkopf; Mrinmaya Sachan; Taylor Berg-Kirkpatrick (ACL (Findings), 2023)

Privacy-Preserving Domain Adaptation of Semantic Parsers. - Fatemehsadat Mireshghallah; Yu Su 001; Tatsunori Hashimoto; Jason Eisner; Richard Shin (ACL (1), 2023)

A Block Metropolis-Hastings Sampler for Controllable Energy-based Text Generation. - Jarad Forristal; Fatemehsadat Mireshghallah; Greg Durrett; Taylor Berg-Kirkpatrick (CoNLL, 2023)

Simple Temporal Adaptation to Changing Label Sets: Hashtag Prediction via Dense KNN. - Niloofar Mireshghallah; Nikolai Vogler; Junxian He; Omar Florez; Ahmed El-Kishky; Taylor Berg-Kirkpatrick (EMNLP, 2023)

Smaller Language Models are Better Black-box Machine-Generated Text Detectors. - Fatemehsadat Mireshghallah; Justus Mattern; Sicun Gao; Reza Shokri; Taylor Berg-Kirkpatrick (CoRR, 2023)

Are Chatbots Ready for Privacy-Sensitive Applications? An Investigation into Input Regurgitation and Prompt-Induced Sanitization. - Aman Priyanshu; Supriti Vijay; Ayush Kumar; Rakshit Naidu; Fatemehsadat Mireshghallah (CoRR, 2023)

LatticeGen: A Cooperative Framework which Hides Generated Text in a Lattice for Privacy-Aware Generation on Cloud. - Mengke Zhang; Tianxing He; Tianle Wang 003; Lu Mi; Fatemehsadat Mireshghallah; Binyi Chen; Hao Wang 014; Yulia Tsvetkov (CoRR, 2023)

Misusing Tools in Large Language Models With Visual Adversarial Examples. - Xiaohan Fu; Zihan Wang 001; Shuheng Li; Rajesh K. Gupta 001; Niloofar Mireshghallah; Taylor Berg-Kirkpatrick; Earlence Fernandes (CoRR, 2023)

Report of the 1st Workshop on Generative AI and Law. - A. Feder Cooper; Katherine Lee; James Grimmelmann; Daphne Ippolito; Christopher Callison-Burch; Christopher A. Choquette-Choo; Niloofar Mireshghallah; Miles Brundage; David Mimno; Madiha Zahrah Choksi; Jack M. Balkin; Nicholas Carlini; et al. (CoRR, 2023)

Auditing and Mitigating Safety Risks in Large Language Models - Niloofar Mireshghallah (2023)

Smaller Language Models are Better Zero-shot Machine-Generated Text Detectors. - Niloofar Mireshghallah; Justus Mattern; Sicun Gao; Reza Shokri; Taylor Berg-Kirkpatrick (EACL (2), 2024)

CopyBench: Measuring Literal and Non-Literal Reproduction of Copyright-Protected Text in Language Model Generation. - Tong Chen; Akari Asai; Niloofar Mireshghallah; Sewon Min; James Grimmelmann; Yejin Choi 001; Hannaneh Hajishirzi; Luke Zettlemoyer; Pang Wei Koh (EMNLP, 2024)

Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory. - Niloofar Mireshghallah; Hyunwoo Kim 002; Xuhui Zhou; Yulia Tsvetkov; Maarten Sap; Reza Shokri; Yejin Choi 001 (ICLR, 2024)

Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation. - Xinyu Tang 003; Richard Shin; Huseyin A. Inan; Andre Manoel; Fatemehsadat Mireshghallah; Zinan Lin 001; Sivakanth Gopi; Janardhan Kulkarni; Robert Sim (ICLR, 2024)

Position: A Roadmap to Pluralistic Alignment. - Taylor Sorensen; Jared Moore; Jillian Fisher; Mitchell L. Gordon; Niloofar Mireshghallah; Christopher Michael Rytting; Andre Ye; Liwei Jiang; Ximing Lu; Nouha Dziri; Tim Althoff; Yejin Choi 001 (ICML, 2024)

LatticeGen: Hiding Generated Text in a Lattice for Privacy-Aware Large Language Model Generation on Cloud. - Mengke Zhang; Tianxing He; Tianle Wang 003; Lu Mi; Niloofar Mireshghallah; Binyi Chen; Hao Wang 014; Yulia Tsvetkov (NAACL-HLT (Findings), 2024)

WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Models. - Liwei Jiang; Kavel Rao; Seungju Han 002; Allyson Ettinger; Faeze Brahman; Sachin Kumar 009; Niloofar Mireshghallah; Ximing Lu; Maarten Sap; Yejin Choi 001; Nouha Dziri (NeurIPS, 2024)

A Roadmap to Pluralistic Alignment. - Taylor Sorensen; Jared Moore; Jillian Fisher; Mitchell L. Gordon; Niloofar Mireshghallah; Christopher Michael Rytting; Andre Ye; Liwei Jiang; Ximing Lu; Nouha Dziri; Tim Althoff; Yejin Choi 001 (CoRR, 2024)

Do Membership Inference Attacks Work on Large Language Models? - Michael Duan; Anshuman Suri; Niloofar Mireshghallah; Sewon Min; Weijia Shi; Luke Zettlemoyer; Yulia Tsvetkov; Yejin Choi 001; David Evans 001; Hannaneh Hajishirzi (CoRR, 2024)

Breaking News: Case Studies of Generative AI's Use in Journalism. - Natalie Grace Brigham; Chongjiu Gao; Tadayoshi Kohno; Franziska Roesner; Niloofar Mireshghallah (CoRR, 2024)

Trust No Bot: Discovering Personal Disclosures in Human-LLM Conversations in the Wild. - Niloofar Mireshghallah; Maria Antoniak; Yash More; Yejin Choi 001; Golnoosh Farnadi (CoRR, 2024)

HAICOSYSTEM: An Ecosystem for Sandboxing Safety Risks in Human-AI Interactions. - Xuhui Zhou; Hyunwoo Kim 002; Faeze Brahman; Liwei Jiang; Hao Zhu 011; Ximing Lu; Frank Xu; Bill Yuchen Lin; Yejin Choi 001; Niloofar Mireshghallah; Ronan Le Bras 001; Maarten Sap (CoRR, 2024)

Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy, Research, and Practice. - A. Feder Cooper; Christopher A. Choquette-Choo; Miranda Bogen; Matthew Jagielski; Katja Filippova; Ken Ziyu Liu; Alexandra Chouldechova; Jamie Hayes; Yangsibo Huang; Niloofar Mireshghallah; Ilia Shumailov; Eleni Triantafillou; et al. (CoRR, 2024)

Privacy Ripple Effects from Adding or Removing Personal Information in Language Model Training. - Jaydeep Borkar; Matthew Jagielski; Katherine Lee; Niloofar Mireshghallah; David A. Smith; Christopher A. Choquette-Choo (ACL (Findings), 2025)

AI as Humanity's Salieri: Quantifying Linguistic Creativity of Language Models via Systematic Attribution of Machine Text against Web Text. - Ximing Lu; Melanie Sclar; Skyler Hallinan; Niloofar Mireshghallah; Jiacheng Liu 010; Seungju Han 002; Allyson Ettinger; Liwei Jiang; Khyathi Raghavi Chandu; Nouha Dziri; Yejin Choi 001 (ICLR, 2025)

ALPACA AGAINST VICUNA: Using LLMs to Uncover Memorization of LLMs. - Aly M. Kassem; Omar Mahmoud; Niloofar Mireshghallah; Hyunwoo Kim 002; Yulia Tsvetkov; Yejin Choi 001; Sherif Saad; Santu Rana (NAACL (Long Papers), 2025)

Differentially Private Learning Needs Better Model Initialization and Self-Distillation. - Ivoline C. Ngong; Joseph P. Near; Niloofar Mireshghallah (NAACL (Long Papers), 2025)

Information-Guided Identification of Training Data Imprint in (Proprietary) Large Language Models. - Abhilasha Ravichander; Jillian Fisher; Taylor Sorensen; Ximing Lu; Maria Antoniak; Bill Yuchen Lin; Niloofar Mireshghallah; Chandra Bhagavatula; Yejin Choi 001 (NAACL (Long Papers), 2025)

Synthetic Data Can Mislead Evaluations: Membership Inference as Machine Text Detection. - Ali Naseh; Niloofar Mireshghallah (CoRR, 2025)

ParaPO: Aligning Language Models to Reduce Verbatim Reproduction of Pre-training Data. - Tong Chen; Faeze Brahman; Jiacheng Liu 010; Niloofar Mireshghallah; Weijia Shi; Pang Wei Koh; Luke Zettlemoyer; Hannaneh Hajishirzi (CoRR, 2025)

A False Sense of Privacy: Evaluating Textual Data Sanitization Beyond Surface-level Privacy Leakage. - Rui Xin; Niloofar Mireshghallah; Shuyue Stella Li; Michael Duan; Hyunwoo Kim 002; Yejin Choi 001; Yulia Tsvetkov; Sewoong Oh; Pang Wei Koh (CoRR, 2025)

Can Large Language Models Really Recognize Your Name? - Dzung Pham 002; Peter Kairouz; Niloofar Mireshghallah; Eugene Bagdasarian; Chau Minh Pham; Amir Houmansadr (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)

Privacy-Preserving LLM Interaction with Socratic Chain-of-Thought Reasoning and Homomorphically Encrypted Vector Databases. - Yubeen Bae; Minchan Kim; Jaejin Lee; Sangbum Kim; Jaehyung Kim; Yejin Choi; Niloofar Mireshghallah (CoRR, 2025)

Bob's Confetti: Phonetic Memorization Attacks in Music and Video Generation. - Jaechul Roh; Zachary Novack; Yuefeng Peng; Niloofar Mireshghallah; Taylor Berg-Kirkpatrick; Amir Houmansadr (CoRR, 2025)

The Surprising Effectiveness of Membership Inference with Simple N-Gram Coverage. - Skyler Hallinan; Jaehun Jung; Melanie Sclar; Ximing Lu; Abhilasha Ravichander; Sahana Ramnath; Yejin Choi 001; Sai Praneeth Karimireddy; Niloofar Mireshghallah; Xiang Ren 001 (CoRR, 2025)