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:
- Bryan Cai, Constantinos Daskalakis and Gautam Kamath. Priv'IT: Private and Sample Efficient Identity Testing
- Garrett Bernstein, Ryan McKenna, Tao Sun, Daniel Sheldon, Michael Hay and Gerome Miklau. Differentially Private Learning of Undirected Graphical Models using CGMs
- Marko Mitrovic, Amin Karbasi, Andreas Krause and Mark Bun. Differentially Private Submodular Maximization: Data Summarization in Disguise
- Aleksandra Korolova. The Hybrid Model for Privacy and its Benefits
- Nathalie Baracaldo, Bryant Chen and Heiko Ludwig. Detecting Causative Attacks using Data Provenance
- 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:
- Martine De Cock, Rafael Dowsley, Nick McKinney, Anderson Nascimento and Dongrui Wu. Privacy Preserving Machine Learning with EEG Data
- 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
- Adria Gascon, Phillipp Schoppmann, Borja Balle, Mariana Raykova, Jack Doerner, Samee Zahur and David Evans. Privacy-Preserving Distributed Linear Regression on High-Dimensional Data
- 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
- Le Trieu Phong, Yoshinori Aono, Takuya Hayashi, Lihua Wang and Shiho Moriai. Privacy-Preserving Deep Learning: Revisited and Enhanced
- Adria Gascon, Phillipp Schoppmann and Borja Balle. Private Multi-Party Document Classification
- Joonas Jälkö, Onur Dikmen and Antti Honkela. Differentially Private Variational Inference for Non-conjugate Models
- Maria-Florina Balcan, Travis Dick and Ellen Vitercik. Differentially private algorithm configuration
- Mikko Heikkilä, Eemil Lagerspetz, Samuel Kaski, Kana Shimizu, Sasu Tarkoma and Antti Honkela. Differentially Private Bayesian Learning on Distributed Data
- Yu-Xiang Wang. Per-instance Differential Privacy and the Adaptivity of Posterior Sampling in Linear and Ridge regression