Learning Genomic Representations to Predict Clinical Outcomes in Cancer. - Safoora Yousefi; Congzheng Song; Nelson Nauata; Lee Cooper 001 (CoRR, 2016)

Machine Learning Models that Remember Too Much. - Congzheng Song; Thomas Ristenpart; Vitaly Shmatikov (CCS, 2017)

Membership Inference Attacks Against Machine Learning Models. - Reza Shokri; Marco Stronati; Congzheng Song; Vitaly Shmatikov (IEEE Symposium on Security and Privacy, 2017)

Kernel Distillation for Gaussian Processes. - Congzheng Song; Yiming Sun (CoRR, 2018)

Fooling OCR Systems with Adversarial Text Images. - Congzheng Song; Vitaly Shmatikov (CoRR, 2018)

Chiron: Privacy-preserving Machine Learning as a Service. - Tyler Hunt; Congzheng Song; Reza Shokri; Vitaly Shmatikov; Emmett Witchel (CoRR, 2018)

Inference Attacks Against Collaborative Learning. - Luca Melis; Congzheng Song; Emiliano De Cristofaro; Vitaly Shmatikov (CoRR, 2018)

The Natural Auditor: How To Tell If Someone Used Your Words To Train Their Model. - Congzheng Song; Vitaly Shmatikov (CoRR, 2018)

Auditing Data Provenance in Text-Generation Models. - Congzheng Song; Vitaly Shmatikov (KDD, 2019)

Exploiting Unintended Feature Leakage in Collaborative Learning. - Luca Melis; Congzheng Song; Emiliano De Cristofaro; Vitaly Shmatikov (IEEE Symposium on Security and Privacy, 2019)

Generalized Zero-shot ICD Coding. - Congzheng Song; Shanghang Zhang; Najmeh Sadoughi; Pengtao Xie; Eric P. Xing (CoRR, 2019)

Information Leakage in Embedding Models. - Congzheng Song; Ananth Raghunathan (CCS, 2020)

Membership Encoding for Deep Learning. - Congzheng Song; Reza Shokri (AsiaCCS, 2020)

Adversarial Semantic Collisions. - Congzheng Song; Alexander M. Rush; Vitaly Shmatikov (EMNLP (1), 2020)

Overlearning Reveals Sensitive Attributes. - Congzheng Song; Vitaly Shmatikov (ICLR, 2020)

Generalized Zero-Shot Text Classification for ICD Coding. - Congzheng Song; Shanghang Zhang; Najmeh Sadoughi; Pengtao Xie; Eric P. Xing (IJCAI, 2020)

You Autocomplete Me: Poisoning Vulnerabilities in Neural Code Completion. - Roei Schuster; Congzheng Song; Eran Tromer; Vitaly Shmatikov (USENIX Security Symposium, 2021)

FLAIR: Federated Learning Annotated Image Repository. - Congzheng Song; Filip Granqvist; Kunal Talwar (NeurIPS, 2022)

Training a Tokenizer for Free with Private Federated Learning. - Eugene Bagdasaryan; Congzheng Song; Rogier C. van Dalen; Matt Seigel; Áine Cahill (CoRR, 2022)

Private Federated Statistics in an Interactive Setting. - Audra McMillan; Omid Javidbakht; Kunal Talwar; Elliot Briggs; Mike Chatzidakis; Junye Chen; John C. Duchi; Vitaly Feldman; Yusuf Goren; Michael Hesse; Vojta Jina; Anil Katti; et al. (CoRR, 2022)

Training Large-Vocabulary Neural Language Models by Private Federated Learning for Resource-Constrained Devices. - Mingbin Xu; Congzheng Song; Ye Tian; Neha Agrawal; Filip Granqvist; Rogier C. van Dalen; Xiao Zhang; Arturo Argueta; Shiyi Han; Yaqiao Deng; Leo Liu; Anmol Walia; et al. (ICASSP, 2023)

Population Expansion for Training Language Models with Private Federated Learning. - Tatsuki Koga; Congzheng Song; Martin Pelikan; Mona Chitnis (CoRR, 2023)

Samplable Anonymous Aggregation for Private Federated Data Analysis. - Kunal Talwar; Shan Wang; Audra McMillan; Vojta Jina; Vitaly Feldman; Bailey Basile; Áine Cahill; Yi Sheng Chan; Mike Chatzidakis; Junye Chen; Oliver R. A. Chick; Mona Chitnis; et al. (CoRR, 2023)

Samplable Anonymous Aggregation for Private Federated Data Analysis. - Kunal Talwar; Shan Wang; Audra McMillan; Vitaly Feldman; Pansy Bansal; Bailey Basile; Áine Cahill; Yi Sheng Chan; Mike Chatzidakis; Junye Chen; Oliver R. A. Chick; Mona Chitnis; et al. (CCS, 2024)

pfl-research: simulation framework for accelerating research in Private Federated Learning. - Filip Granqvist; Congzheng Song; Áine Cahill; Rogier C. van Dalen; Martin Pelikan; Yi Sheng Chan; Xiaojun Feng; Natarajan Krishnaswami; Vojta Jina; Mona Chitnis (NeurIPS, 2024)

Momentum Approximation in Asynchronous Private Federated Learning. - Tao Yu; Congzheng Song; Jianyu Wang; Mona Chitnis (CoRR, 2024)

Private Federated Learning In Real World Application - A Case Study. - An Ji; Bortik Bandyopadhyay; Congzheng Song; Natarajan Krishnaswami; Prabal Vashisht; Rigel Smiroldo; Isabel Litton; Sayantan Mahinder; Mona Chitnis; Andrew W. Hill (CoRR, 2025)