Program

Abstracts are published at this page.


Invited talks:

  • Peeter Laud. Privacy-preserving machine learning and data mining using the Sharemind platform
  • Ilya Mironov. Deep Learning with Differential Privacy: Two Approaches


Contributed talks:

  1. Bryan Cai, Constantinos Daskalakis and Gautam Kamath. Priv'IT: Private and Sample Efficient Identity Testing
  2. Garrett Bernstein, Ryan McKenna, Tao Sun, Daniel Sheldon, Michael Hay and Gerome Miklau. Differentially Private Learning of Undirected Graphical Models using CGMs
  3. Marko Mitrovic, Amin Karbasi, Andreas Krause and Mark Bun. Differentially Private Submodular Maximization: Data Summarization in Disguise
  4. Aleksandra Korolova. The Hybrid Model for Privacy and its Benefits
  5. Nathalie Baracaldo, Bryant Chen and Heiko Ludwig. Detecting Causative Attacks using Data Provenance
  6. Mentari Djatmiko, Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Maximilian Ott, Giorgio Patrini, Guillaume Smith, Brian Thorne and Dongyao Wu. Privacy-preserving entity resolution and logistic regression on encrypted data


Posters:

  1. Martine De Cock, Rafael Dowsley, Nick McKinney, Anderson Nascimento and Dongrui Wu. Privacy Preserving Machine Learning with EEG Data
  2. Takuya Hayashi, Shohei Kuri, Toshiaki Omori, Seiichi Ozawa, Yoshinori Aono, Le Trieu Phong, Lihua Wang and Shiho Moriai. Privacy-preserving machine learning via additively homomorphic encryption: the case of linear and logistic regressions
  3. Adria Gascon, Phillipp Schoppmann, Borja Balle, Mariana Raykova, Jack Doerner, Samee Zahur and David Evans. Privacy-Preserving Distributed Linear Regression on High-Dimensional Data
  4. Martine De Cock, Rafael Dowsley, Caleb Horst, Raj Katti, Anderson Nascimento, Wing-Sea Poon and Stacey Truex. Efficient Privacy-Preserving Scoring of Decision Trees, SVM and Logistic Regression Models
  5. Le Trieu Phong, Yoshinori Aono, Takuya Hayashi, Lihua Wang and Shiho Moriai. Privacy-Preserving Deep Learning: Revisited and Enhanced
  6. Adria Gascon, Phillipp Schoppmann and Borja Balle. Private Multi-Party Document Classification
  7. Joonas Jälkö, Onur Dikmen and Antti Honkela. Differentially Private Variational Inference for Non-conjugate Models
  8. Maria-Florina Balcan, Travis Dick and Ellen Vitercik. Differentially private algorithm configuration
  9. Mikko Heikkilä, Eemil Lagerspetz, Samuel Kaski, Kana Shimizu, Sasu Tarkoma and Antti Honkela. Differentially Private Bayesian Learning on Distributed Data
  10. Yu-Xiang Wang. Per-instance Differential Privacy and the Adaptivity of Posterior Sampling in Linear and Ridge regression