About Me

Publications and Manuscripts

(* indicates equal contribution)

• Training a Tokenizer for Free with Private Federated Learning [pdf]
  E.Bagdasaryan, C.Song, R.van Dalen, M.Seigel, Á.Cahill
  In arXiv preprint, 2022


• You Autocomplete Me: Poisoning Vulnerabilities in Neural Code Completion [pdf]
  R.Schuster, C.Song, E. Tromer, V.Shmatikov
  In USENIX Security Symposium, 2021
  Distinguished Paper Award


• Cell-phone Traces Reveal Infection-associated Behavioral Change [pdf][code]
  Y.Vigfusson, T.A.Karlsson, D.Onken, C.Song, A.F.Einarsson, N.Kishore, R.M.Mitchell, E.Brooks-Pollock, G.Sigmundsdottir, L.Danon
  In Proceedings of the National Academy of Sciences of the United States of America (PNAS), 2021


• Adversarial Semantic Collisions [pdf][code]
  C.Song, A.Rush, V.Shmatikov
  In Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020


• Information Leakage in Embedding Models [pdf][code]
  C.Song, A.Raghunathan
  In ACM Conference on Computer and Communications Security (CCS), 2020


• Generalized Zero-Shot Text Classification for ICD Coding [pdf][code]
  C.Song, S.Zhang, N.Sadoughi, P.Xie, E.P.Xing
  In International Joint Conference on Artificial Intelligence (IJCAI), 2020


• Membership Encoding for Deep Learning [pdf]
  C.Song, R.Shokri
  In ACM ASIA Conference on Computer and Communications Security (AsiaCCS), 2020


• Overlearning Reveals Sensitive Attributes [pdf][code][slides]
  C.Song, V.Shmatikov
  In International Conference on Learning Representation (ICLR), 2020


• Auditing Data Provenance in Text-Generation Models [pdf][code][slides]
  C.Song, V.Shmatikov
  In ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2019
  Oral Presentation


• Exploiting Unintended Feature Leakage in Collaborative Learning [pdf][code][talk][slides]
  L.Melis*, C.Song*, E. De Cristofaro, V.Shmatikov
  In IEEE Symposium on Security and Privacy (Oakland), 2019
  


• What Are Machine Learning Models Hiding? [pdf]
  V.Shamtikov, C.Song
  In Workshop on Hot Topics in Privacy Enhancing Technologies (HotPETs), 2018


• Kernel Distillation for Fast Gaussian Processes Prediction [pdf][code]
  C.Song*, Y.Sun*
  In NeurIPS Workshop on All of Bayesian Nonparametrics (BNP@NeurIPS), 2018
  Spotlight Presentation


• Chiron: Privacy-preserving Machine Learning as a Service [pdf]
  T.Hunt, C.Song, R.Shokri, V.Shmatikov, E.Witchel
  In arXiv preprint, 2018


• Fooling OCR Systems with Adversarial Text Images [pdf][code by F.Tramèr et al]
  C.Song, V.Shmatikov
  In arXiv preprint, 2018


• Predicting Clinical Outcomes from Large Scale Cancer Genomic Profiles with Deep Survival Models [pdf][code]
  S.Yousefi, F.Amrollahi, M.Amgad, C.Dong, J.E.Lewis, C.Song, D.A.Gutman, S.H.Halani, J.E.V.Vega, D.J.Brat, L.A.D.Cooper
  In Scientific Reports (Nature), 2017


• Machine Learning Models that Remembers Too Much [pdf][code][talk][slides]
  C.Song, T.Risternpart, V.Shmatikov
  In ACM Conference on Computer and Communications Security (CCS), 2017


• Membership Inference Attacks Against Machine Learning Models [pdf][code][talk]
  R.Shokri, M.Stronati, C.Song, V.Shmatikov
  In IEEE Symposium on Security and Privacy (Oakland), 2017
  The Caspar Bowden Award for Outstanding Research in Privacy Enhancing Technologies 2018


• Learning Genomic Representations to Predict Clinical Outcomes in Cancer [pdf][code]
  S.Yousefi, C.Song, N.Nauata, L.Cooper
  In International Conference on Learning Representation Workshop (ICLRW), 2016

Past Experience