Christopher A. Choquette-Choo

Christopher A. Choquette-Choo

I am a Research Scientist in Google Deepmind, on the Brain Privacy and Security team. My focuses are privacy-preserving and adversarial machine learning. I mainly work on DP algorithms and studying memorization and harms from language models; but, I've also worked on privacy auditing techniques, collaborative learning approaches, and methods for ownership-verification.


Previously, I was an AI Resident at Google and a researcher in the CleverHans Lab at the Vector Institute. I graduated from the University of Toronto, where I had a full scholarship.

Email: choquette[dot]christopher[at]gmail[dot]com

Research

I'm broadly interested in Machine Learning, with a focus on its intersection with security and privacy. Specific areas include adversarial ML, data privacy, memorization in language models, edeep learning, and collaborative learning. See my google scholar for an up-to-date list.

Technical Reports and Production Deployments
Gemini: A Family of Highly Capable Multimodal Modelspreprint
PaLM 2 Technical Report: Led memorization analysis. preprint
Federated Learning of Gboard Language Models with Differential Privacy. Association for Computational Linguistics 2023. Conference
Scalable Extraction of Training Data from (Production) Language Models
Milad Nasr, Nicholas Carlini, Jonathan Hayase, Matthew Jagielski, A. Feder Cooper, Daphne Ippolito, Christopher A. Choquette-Choo, Eric Wallace, Florian Tramèr, Katherine Lee preprint

We show that production aligned models can be provide a false-sense of privacy.We show a vulnerability in Chat-GPT that lets us extract a lot of data. We scalably test for extractable memorization in many open-source models.

Privacy Amplification for Matrix Mechanisms
Christopher A. Choquette-Choo, Arun Ganesh, Thomas Steinke, Abhradeep Guha Thakurta
International Conference on Learning Representations (ICLR), 2024conference (+spotlight)

We prove a generic privacy amplification technique for DP-FTRL.

Correlated Noise Provably Beats Independent Noise for Differentially Private Learning
Christopher A. Choquette-Choo, Krishnamurthy Dj Dvijotham, Krishna Pillutla, Arun Ganesh, Thomas Steinke, Abhradeep Guha Thakurta
International Conference on Learning Representations (ICLR), 2024conference
International Workshop on Federated Learning in the Age of Foundation Models (FL@FM-NeurIPS)2023workshop

We prove bounds on DP-FTRL that show when it is better than DP-SGD. We design new algorithms that are more efficient and as performant.

Teach LLMs to Phish: Stealing Private Information from Language Models
Ashwinee Panda, Christopher A. Choquette-Choo, Zhengming Zhang, Yaoqing Yang, Prateek Mittal
International Conference on Learning Representations (ICLR), 2024conference

We show new attacks that can extract sensitive PII from LLMs with high success and minimal assumptions.

User Inference Attacks on Large Language Models
Nikhil Kandpal, Krishna Pillutla, Alina Oprea, Peter Kairouz, Christopher A. Choquette-Choo, Zheng Xu
Socially Responsible Language Modelling Research (SoLaR), 2023workshop
International Workshop on Federated Learning in the Age of Foundation Models in Conjunction with NeurIPS (FL@FM-NeurIPS), 2023workshop

We show new attacks against user's for LLMs tuned on sensitive user data that achieve high success. We call these user inference attacks.

Privacy Side-Channels in Machine Learning
Edoardo Debenedetti, Giorgio Severi, Milad Nasr, Christopher A. Choquette-Choo, Matthew Jagielski, Eric Wallace, Nicholas Carlini, Florian Tramèr
preprint

We show that an entire system---not just an ML model---need to be considered to ensure privacy. We introduce new attacks against a variety of systems components that can invalidate guarantees on just an ML model.

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, ...
preprint

We discuss and provide insights/opinions on the intersection of generative AI and law.

MADLAD-400: Multilingual And Document-Level Large Audited Dataset
Sneha Kudugunta, Isaac Caswell, Biao Zhang, Xavier Garcia, Christopher A. Choquette-Choo, Katherine Lee, Derrick Xin, Aditya Kusupati, Romi Stella, Ankur Bapna, Orhan Firat
Thirty-seventh Conference on Neural Information Processing Systems (Neurips), 2023conference

We introduce a new multilingual dataset, improve multilingual models, and show that translation tasks can be vulnerable to new privacy attacks.

Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning
Christopher A. Choquette-Choo, H. Brendan McMahan, Keith Rush, Abhradeep Thakurta
40th International Conference on Machine Learning (ICML), 2023conference (+oral)

We achieve new state-of-the-art privacy-utility tradeoffs in DP ML. We outperform DP-SGD without any need for privacy amplification.

Private Federated Learning with Autotuned Compression
Enayat Ullah*, Christopher A. Choquette-Choo*, Peter Kairouz*, Sewoong Oh*
40th International Conference on Machine Learning (ICML), 2023conference

We show how to achieve impressive compression rates in the online setting, i.e., without prior tuning. Our best algorithms can achieve close to the best-known compression rates.

(Amplified) Banded Matrix Factorization: A unified approach to private training
Christopher A. Choquette-Choo, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, Keith Rush, Abhradeep Guha Thakurta, Zheng Xu
Thirty-seventh Conference on Neural Information Processing Systems (Neurips), 2023conference

We show that DP-FTRL is strictly better than DP-SGD. In doing so, we get new state-of-the-art.

Are aligned neural networks adversarially aligned?
Nicholas Carlini, Milad Nasr, Christopher A. Choquette-Choo, Matthew Jagielski, Irena Gao, Anas Awadalla, Pang Wei Koh, Daphne Ippolito, Katherine Lee, Florian Tramèr, Ludwig Schmidt
Thirty-seventh Conference on Neural Information Processing Systems (Neurips), 2023conference

We introduce attacks against multi-modal models that subvert alignment techniques. In doing so, we show that current NLP attacks are not sufficiently powerful to evaluate adversarial alignment.

Poisoning Web-Scale Training Datasets is Practical
Nicholas Carlini, Matthew Jagielski, Christopher A. Choquette-Choo, Daniel Paleka, Will Pearce, Hyrum Anderson, Andreas Terzis, Kurt Thomas, Florian Tramèr
IEEE Symposium on Security and Privacy (IEEE S&P), 2024conference

We show that modern large-scale datasets can be cost-effectively poisoned.

Students Parrot Their Teachers: Membership Inference on Model Distillation
Matthew Jagielski, Milad Nasr, Katherine Lee, Christopher A. Choquette-Choo, Nicholas Carlini
conference (+oral)

We show that, without formal guarantees, distillation provides limited privacy benefits. We also show the first attack where a model can leak information about private data without ever having been queried on that private data.

Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy
Daphne Ippolito, Florian Tramèr*, Milad Nasr*, Chiyuan Zhang*, Matthew Jagielski*, Katherine Lee*, Christopher A. Choquette-Choo*, Nicholas Carlini
* Equal contribution. The names are ordered randomly. Proceedings of the 15th International Natural Language Generation Conference (INLG) 2023conference (best-paper runner-up)

We show that models can paraphrase memorizations. Thus, models can evade filters designed specifically to prevent verbatim memorization, like those implemented for CoPilot.

Proof-of-Learning is Currently more Broken than you Think
Congyu Fang*, Hengrui Jia*, Anvith Thudi, Mohammad Yaghini, Christopher A. Choquette-Choo, Natalie Dullerud, Varun Chandrasekaran, Nicolas Papernot
* Equal contribution. Proceedings of the 8th IEEE Euro S&P 2023conference

We break current Proof-of-Learning schemes. Though we design schemes that are robust to all current attacks, we show that it is an open problem to provide formal guarantees.

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
Thirty-seventh Conference on Neural Information Processing Systems (Neurips) 2023conference

Private Multi-Winner Voting for Machine Learning
Adam Dziedzic, Christopher A. Choquette-Choo, Natalie Dullerud, Vinith Menon Suriyakumar, Ali Shahin Shamsabadi, Muhammad Ahmad Kaleem, Somesh Jha, Nicolas Papernot, Xiao Wang
Proceedings on 23rd Privacy Enhancing Technologies Symposium (PETS 2023)conference

Fine-tuning with differential privacy necessitates an additional hyperparameter search
Yannis Cattan, Christopher A Choquette-Choo, Nicolas Papernot, Abhradeep Thakurta
preprint

Do proper hyper-parameter tuning with DP. Check how many layers you should tune.

The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning
Wei-Ning Chen*, Christopher A. Choquette-Choo*, Peter Kairouz*, Ananda Theertha Suresh*
* Equal contribution. The names are ordered alphabetically.
International Conference on Machine Learning (ICML), 2022 conference
Theory and Practice of Differential Privacy (TPDP) Workshop, 2022 workshop

We characterize the fundamental communication costs of Federated Learning (FL) under Secure Aggregation (SecAgg) and Differential Privacy (DP), two privacy-preserving mechanisms that are commonly used with FL. We prove our optimality for worst-case settings which provides significant improvements over prior work, and show that improvements can be made under additional assumptions, e.g., data sparisty. Extensive empirical evaluations support our claims, showing costs as low as 1.2 bits per parameter on Stack Overflow with <4% relative decrease in test-time model accuracy.

Communication Efficient Federated Learning with Secure Aggregation and Differential Privacy
Wei-Ning Chen*, Christopher A. Choquette-Choo*, Peter Kairouz*
Privacy in Machine Learning Workshop at Neurips, 2021 workshop
* Equal contribution. The names are ordered alphabetically.

We show that in the worst-case, differentially-private federated learning with secure aggregation requires Ω(d) bits. Despite this, we discuss how to leverage near-sparsity to compress updates by more than 50x with modest noise multipliers of 0.4 by using sketching.

Proof-of-Learning: Definitions
Hengrui Jia^, Mohammad Yaghini^, Christopher A. Choquette-Choo*, Natalie Dullerud*, Anvith Thudi*, Varun Chandrasekaran, Nicolas Papernot
Proceedings of the 42nd IEEE Symposium on Security and Privacy, San Francisco, CA, 2021 conference
^,* Equal contribution. The names are ordered alphabetically.

How can we prove that a machine learning model owner trained their model? We define the problem of Proof-of-Learning (PoL) in machine learning and provide a method for it that is robust to several spoofing attacks. This protocol enables model ownership verification and robustness against byzantines workers (in a distributed learning setting).

CaPC Learning: Confidential and Private Collaborative Learning
Christopher A. Choquette-Choo*, Natalie Dullerud*, Adam Dziedzic*, Yunxiang Zhang*, Somesh Jha, Nicolas Papernot, Xiao Wang
9th International Conference on Learning Representations (ICLR), 2021 conference
* Equal contribution. The names are ordered alphabetically.

We design a protocol for collaborative learning that ensures both the privacy of the training data and confidentiality of the test data in a collaborative setup. Our protocol can provide several percentage-points improvement to models, especially on subpopulations that the model underperforms on, with a modest privacy budget usage of less than 20. Unlike prior work, we enable collaborative learning of heterogeneous models amongst participants. Unlike differentially private federated learning, which requires ~1 million participants, our protocol can work in regimes of ~100 participants.

Label-Only Membership Inference Attacks
Christopher A. Choquette-Choo, Florian Tramèr, Nicholas Carlini, Nicolas Papernot
38th International Conference on Machine Learning (ICML), 2021 conference

What defenses properly defend against all membership inference threats? We expose and show that confidence-masking -- defensive obfuscation of confidence-vectors -- is not a viable defense to Membership Inference. We do this by introducing (3) label-only attacks, which bypass this defense and match typical confidence-vector attacks. In an extensive evaluation of defenses,including the first evaluation of data augmentations and transfer learning as defenses, we further show that Differential Privacy can defend against average- and worse-case Membership Inference attacks.

Entangled Watermarks as a Defense against Model Extraction
Hengrui Jia, Christopher A. Choquette-Choo, Varun Chandrasekaran, Nicolas Papernot
Proceedings of 30th USENIX Security, 2021 conference

How can we enable an IP owner to reliably claim ownership of a stolen model? We explore entangling of watermarks to task data to ensure that stolen models learn these watermarks as well. Our improved watermarks enable IP owners to claim ownership with 95% confidence in less than 10 queries to the stolen model.

Machine Unlearning
Lucas Bourtoule*, Varun Chandrasekaran*, Christopher A. Choquette-Choo*, Hengrui Jia*, Adelin Travers*, Baiwu Zhang*, David Lie, Nicolas Papernot
Proceedings of the 42nd IEEE Symposium on Security and Privacy, San Francisco, CA, 2021 conference
* Equal contribution. The names are ordered alphabetically.

How can we enable efficient and guaranteed retraining of machine learning models? We define requirements for machine unlearning and study a stricter unlearning requirement whereby unlearning a datapoint is guaranteed to be equivalent to as if we had never trained on it. To this end, we improve on the naive retraining-from-scratch approach to provide a better accuracy-efficieny tradeoff. We also study how a priori knowledge of the distribution of requests can further improve efficiency.

A multi-label, dual-output deep neural network for automated bug triaging
Christopher A. Choquette-Choo, David Sheldon, Jonny Proppe, John Alphonso-Gibbs, Harsha Gupta
ICMLA, 2019  |  DOI: 10.1109/ICMLA.2019.00161 conference

How can we triage bugs more effectively? By utilizing a model's own knowledge of an analogous lower-dimensionality solution-space (predicting the correct team assignment), we can achieve higher accuracies on the higher-dimensionality solution-space (predicting the correct engineer assignment).


Paper Talks

Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning.
Presented at ICML 2023
ICML Website or SlidesLive archive
The Fundamental Price of Secure Aggregation in Differentially Private Machine Learning
Presented at ICML 2022
ICML Website
Label-Only Membership Inference Attacks
Presented at ICML 2021
ICML Website
Proof-of-Learning Definitions and Practices
Presented at IEEE S&P 2021
S&P Youtube Channel
Machine Unlearning
Presented at IEEE S&P 2021
S&P Youtube Channel

Invited Talks

Privacy Considerations of Production Machine Learning.
Presented at Ml Ops World: New York Area Summit
Contact me for slides. Unfortunately, the video is not publicly available :(.
Adversarial Machine Learning: Ensuring Security and Privacy of ML Models and Sensitive Data.
Presented at the REWORK Responsible AI Summit 2019.
Available as a part of the Privacy and Security in Machine Learning package.

Professional Services

  • PC for the 2024 IEEE Security and Privacy (S&P) conference.
  • PC for the 2023 Generative AI + Law (GenLaw)'23 Workshop at Neurips.
  • Reviewer for the 2023 Neural Information Processing Systems (Neurips).
  • Reviewer for 2023 International Conference on Machine Learning (ICML)
  • Invited Reviewer for Nature Machine Intelligence Journal 2023
  • Session Chair of DL: Robustness for the 2022 International Conference on Machine Learning (ICML).
  • Reviewer for the 2022 Neural Information Processing Systems (Neurips).
  • Invited Reviewer for Nature Machine Intelligence Journal 2022
  • Outstanding Reviewer for 2022 International Conference on Machine Learning (ICML)
  • Invited Reviewer for 2022 IEEE Transactions on Emerging Topics in Computing
  • External Reviewer for 2022 USENIX Security Symposium
  • External Reviewer for 2022 IEEE Symposium on Security and Privacy
  • Reviewer for 2021 Journal of Machine Learning Research
  • External Reviewer for 2021 International Conference on Machine Learning (ICML)
  • External Reviewer for 2021 USENIX Security Symposium
  • External Reviewer for 2021 IEEE Symposium on Security and Privacy
  • Reviewer for the 2020 Machine Learning for the Developing World (ML4D) workshop at NeurIPS