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This one day workshop focuses on privacy preserving techniques for machine learning and disclosure in large scale data analysis, both in the distributed and centralized settings, and on scenarios that highlight the importance and need for these techniques (e.g., via privacy attacks). There is growing interest from the Machine Learning (ML) community in leveraging cryptographic techniques such as Multi-Party Computation (MPC) and Homomorphic Encryption (HE) for privacy preserving training and inference, as well as Differential Privacy (DP) for disclosure. Simultaneously, the systems security and cryptography community has proposed various secure frameworks for ML. We encourage both theory and application-oriented submissions exploring a range of approaches listed below. Additionally, given the tension between the adoption of machine learning technologies and ethical, technical and regulatory issues about privacy, as highlighted during the COVID-19 pandemic, we invite submissions for the special track on this topic.
Fri 1:20 a.m. - 1:30 a.m.
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Welcome & Introduction
(
Live Intro
)
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🔗 |
Fri 1:30 a.m. - 2:00 a.m.
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Invited Talk #1: Reza Shokri (National University of Singapore)
(
Invited Talk
)
SlidesLive Video » |
Reza Shokri 🔗 |
Fri 2:00 a.m. - 2:30 a.m.
|
Invited Talk #2: Katrina Ligett (Hebrew University)
(
Invited Talk
)
SlidesLive Video » |
Katrina Ligett 🔗 |
Fri 2:30 a.m. - 3:00 a.m.
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Invited Talk Q&A with Reza and Katrina
(
Q&A Session
)
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🔗 |
Fri 3:00 a.m. - 3:10 a.m.
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Break
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🔗 |
Fri 3:10 a.m. - 3:25 a.m.
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Contributed Talk #1: POSEIDON: Privacy-Preserving Federated Neural Network Learning
(
Oral
)
SlidesLive Video » |
Sinem Sav 🔗 |
Fri 3:25 a.m. - 3:30 a.m.
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Contributed Talk Q&A
(
Q&A Session
)
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🔗 |
Fri 3:30 a.m. - 5:00 a.m.
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Poster Session & Social on Gather.Town ( Poster Session ) link » | 🔗 |
Fri 8:30 a.m. - 8:40 a.m.
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Welcome & Introduction
(
Live Intro
)
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🔗 |
Fri 8:40 a.m. - 9:00 a.m.
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Invited Talk #3: Carmela Troncoso (EPFL)
(
Invited Talk
)
SlidesLive Video » |
Carmela Troncoso 🔗 |
Fri 9:00 a.m. - 9:30 a.m.
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Invited Talk #4: Dan Boneh (Stanford University)
(
Invited Talk
)
SlidesLive Video » |
Dan Boneh 🔗 |
Fri 9:30 a.m. - 10:00 a.m.
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Invited Talk Q&A with Carmela and Dan
(
Q&A Session
)
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🔗 |
Fri 10:00 a.m. - 10:10 a.m.
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Break
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🔗 |
Fri 10:10 a.m. - 11:10 a.m.
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Poster Session & Social on Gather.Town ( Poster Session ) link » | 🔗 |
Fri 11:10 a.m. - 11:20 a.m.
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Break
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🔗 |
Fri 11:20 a.m. - 11:35 a.m.
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Contributed Talk #2: On the (Im)Possibility of Private Machine Learning through Instance Encoding
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Oral
)
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Nicholas Carlini 🔗 |
Fri 11:35 a.m. - 11:50 a.m.
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Contributed Talk #3: Poirot: Private Contact Summary Aggregation
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Oral
)
SlidesLive Video » |
Chenghong Wang 🔗 |
Fri 11:50 a.m. - 12:05 p.m.
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Contributed Talk #4: Greenwoods: A Practical Random Forest Framework for Privacy Preserving Training and Prediction
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Oral
)
SlidesLive Video » |
Harsh Chaudhari 🔗 |
Fri 12:05 p.m. - 12:20 p.m.
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Contributed Talks Q&A
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Q&A Session
)
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🔗 |
Fri 12:20 p.m. - 12:25 p.m.
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Break
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🔗 |
Fri 12:25 p.m. - 12:40 p.m.
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Contributed Talk #5: Shuffled Model of Federated Learning: Privacy, Accuracy, and Communication Trade-offs
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Oral
)
SlidesLive Video » |
Deepesh Data 🔗 |
Fri 12:40 p.m. - 12:55 p.m.
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Contributed Talk #6: Sample-efficient proper PAC learning with approximate differential privacy
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Oral
)
SlidesLive Video » |
Badih Ghazi 🔗 |
Fri 12:55 p.m. - 1:10 p.m.
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Contributed Talk #7: Training Production Language Models without Memorizing User Data
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Oral
)
SlidesLive Video » |
Swaroop Ramaswamy · Om Thakkar 🔗 |
Fri 1:10 p.m. - 1:25 p.m.
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Contributed Talks Q&A
(
Q&A Session
)
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🔗 |
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Towards General-purpose Infrastructure for Protecting Scientific Data Under Study
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Poster
)
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Kritika Prakash 🔗 |
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Robust and Private Learning of Halfspaces
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Poster
)
SlidesLive Video » |
Badih Ghazi 🔗 |
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Randomness Beyond Noise: Differentially Private Optimization Improvement through Mixup
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Poster
)
SlidesLive Video » |
Hanshen Xiao 🔗 |
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Generative Adversarial User Privacy in Lossy Single-Server Information Retrieval
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Poster
)
SlidesLive Video » |
Chung-Wei Weng 🔗 |
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Privacy Preserving Chatbot Conversations
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Poster
)
SlidesLive Video » |
Debmalya Biswas 🔗 |
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Distributed Differentially Private Averaging with Improved Utility and Robustness to Malicious Parties
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Poster
)
SlidesLive Video » |
Aurélien Bellet 🔗 |
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Twinify: A software package for differentially private data release
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Poster
)
SlidesLive Video » |
Joonas Jälkö 🔗 |
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DAMS: Meta-estimation of private sketch data structures for differentially private contact tracing
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Poster
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Praneeth Vepakomma 🔗 |
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Secure Medical Image Analysis with CrypTFlow
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Poster
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SlidesLive Video » |
Javier Alvarez-Valle 🔗 |
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Multi-Headed Global Model for handling Non-IID data
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Poster
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Himanshu Arora 🔗 |
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Individual Privacy Accounting via a Rényi Filter
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Poster
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SlidesLive Video » |
Vitaly Feldman 🔗 |
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Does Domain Generalization Provide Inherent Membership Privacy
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Poster
)
SlidesLive Video » |
Divyat Mahajan 🔗 |
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Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling
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Poster
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SlidesLive Video » |
Vitaly Feldman 🔗 |
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SparkFHE: Distributed Dataflow Framework with Fully Homomorphic Encryption
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Poster
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SlidesLive Video » |
Peizhao Hu 🔗 |
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Enabling Fast Differentially Private SGD via Static Graph Compilation and Batch-Level Parallelism
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Poster
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SlidesLive Video » |
Pranav Subramani 🔗 |
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Local Differentially Private Regret Minimization in Reinforcement Learning
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Poster
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SlidesLive Video » |
Evrard Garcelon 🔗 |
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SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning
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Poster
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SlidesLive Video » |
Nishat Koti 🔗 |
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Differentially Private Stochastic Coordinate Descent
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Poster
)
SlidesLive Video » |
Georgios Damaskinos 🔗 |
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MP2ML: A Mixed-Protocol Machine LearningFramework for Private Inference
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Poster
)
SlidesLive Video » |
Fabian Boemer 🔗 |
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Dataset Inference: Ownership Resolution in Machine Learning
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Poster
)
SlidesLive Video » |
Nicolas Papernot 🔗 |
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Privacy-preserving XGBoost Inference
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Poster
)
SlidesLive Video » |
Xianrui Meng 🔗 |
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New Challenges for Fully Homomorphic Encryption
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Poster
)
SlidesLive Video » |
Marc Joye 🔗 |
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Differentially Private Bayesian Inference For GLMs
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Poster
)
SlidesLive Video » |
Joonas Jälkö 🔗 |
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Robustness Threats of Differential Privacy
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Poster
)
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Ivan Oseledets 🔗 |
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Asymmetric Private Set Intersection with Applications to Contact Tracing and Private Vertical Federated Machine Learning
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Poster
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SlidesLive Video » |
Bogdan Cebere 🔗 |
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Characterizing Private Clipped Gradient Descent on Convex Generalized Linear Problems
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Poster
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SlidesLive Video » |
Shuang Song 🔗 |
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Adversarial Attacks and Countermeasures on Private Training in MPC
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Poster
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Matthew Jagielski 🔗 |
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Optimal Client Sampling for Federated Learning
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Poster
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SlidesLive Video » |
Samuel Horváth 🔗 |
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Data Appraisal Without Data Sharing
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Poster
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SlidesLive Video » |
Mimee Xu 🔗 |
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Dynamic Channel Pruning for Privacy
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Poster
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Abhishek Singh 🔗 |
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Mitigating Leakage in Federated Learning with Trusted Hardware
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Poster
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SlidesLive Video » |
Javad Ghareh Chamani 🔗 |
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Unifying Privacy Loss for Data Analytics
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Poster
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SlidesLive Video » |
Ryan Rogers 🔗 |
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Differentially Private Generative Models Through Optimal Transport
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Poster
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SlidesLive Video » |
Karsten Kreis 🔗 |
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A Principled Approach to Learning Stochastic Representations for Privacy in Deep Neural Inference
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Poster
)
SlidesLive Video » |
FatemehSadat Mireshghallah 🔗 |
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Challenges of Differentially Private Prediction in Healthcare Settings
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Poster
)
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Nicolas Papernot 🔗 |
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Machine Learning with Membership Privacy via Knowledge Transfer
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Poster
)
SlidesLive Video » |
Virat Shejwalkar 🔗 |
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Secure Single-Server Aggregation with (Poly)Logarithmic Overhead
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Poster
)
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James Bell 🔗 |
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PrivAttack: A Membership Inference AttackFramework Against Deep Reinforcement LearningAgents
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Poster
)
SlidesLive Video » |
Maziar Gomrokchi 🔗 |
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Effectiveness of MPC-friendly Softmax Replacement
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Poster
)
SlidesLive Video » |
Marcel Keller 🔗 |
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Revisiting Membership Inference Under Realistic Assumptions
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Poster
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Bargav Jayaraman 🔗 |
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DYSAN: Dynamically sanitizing motion sensor data against sensitive inferences through adversarial networks
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Poster
)
SlidesLive Video » |
Théo JOURDAN 🔗 |
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Fairness in the Eyes of the Data: Certifying Machine-Learning Models
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Poster
)
SlidesLive Video » |
Carsten Baum 🔗 |
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Privacy in Multi-armed Bandits: Fundamental Definitions and Lower Bounds on Regret
(
Poster
)
SlidesLive Video » |
Debabrota Basu 🔗 |
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Accuracy, Interpretability and Differential Privacy via Explainable Boosting
(
Poster
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SlidesLive Video » |
Harsha Nori 🔗 |
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Privacy Amplification by Decentralization
(
Poster
)
SlidesLive Video » |
Aurélien Bellet 🔗 |
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Privacy Risks in Embedded Deep Learning
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Poster
)
SlidesLive Video » |
Virat Shejwalkar 🔗 |
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Quantifying Privacy Leakage in Graph Embedding
(
Poster
)
SlidesLive Video » |
Antoine Boutet 🔗 |
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Understanding Unintended Memorization in Federated Learning
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Poster
)
SlidesLive Video » |
Om Thakkar 🔗 |
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Network Generation with Differential Privacy
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Poster
)
SlidesLive Video » |
Xu Zheng 🔗 |
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Privacy Regularization: Joint Privacy-Utility Optimization in Language Models
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Poster
)
SlidesLive Video » |
FatemehSadat Mireshghallah 🔗 |
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Tight Approximate Differential Privacy for Discrete-Valued Mechanisms Using FFT
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Poster
)
SlidesLive Video » |
Antti Koskela 🔗 |
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Differentially private cross-silo federated learning
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Poster
)
SlidesLive Video » |
Mikko Heikkilä 🔗 |
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CrypTen: Secure Multi-Party Computation Meets Machine Learning
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Poster
)
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Shubho Sengupta 🔗 |
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On Polynomial Approximations for Privacy-Preserving and Verifiable ReLU Networks
(
Poster
)
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Salman Avestimehr 🔗 |
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Data-oblivious training for XGBoost models
(
Poster
)
SlidesLive Video » |
Chester Leung 🔗 |
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Privacy Attacks on Machine Unlearning
(
Poster
)
SlidesLive Video » |
Ji Gao 🔗 |
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SOTERIA: In Search of Efficient Neural Networks for Private Inference
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Poster
)
SlidesLive Video » |
Reza Shokri 🔗 |
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On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians
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Poster
)
SlidesLive Video » |
Ishaq Aden-Ali 🔗 |
Author Information
Borja Balle (DeepMind)
James Bell (Alan Turing Institute)
Aurélien Bellet (INRIA)
Kamalika Chaudhuri (UCSD)
Adria Gascon (Alan Turing Institute and Warwick university)
Antti Honkela (University of Helsinki)
Antti Koskela (University of Helsinki)
Casey Meehan (University of California, San Diego)
Olga Ohrimenko (The University of Melbourne)
Mi Jung Park (MPI-IS Tuebingen)
Mariana Raykova (Google)
Mary Anne Smart (University of California, San Diego)
Yu-Xiang Wang (UC Santa Barbara)
Adrian Weller (Cambridge, Alan Turing Institute)
Adrian Weller is Programme Director for AI at The Alan Turing Institute, the UK national institute for data science and AI, where he is also a Turing Fellow leading work on safe and ethical AI. He is a Principal Research Fellow in Machine Learning at the University of Cambridge, and at the Leverhulme Centre for the Future of Intelligence where he is Programme Director for Trust and Society. His interests span AI, its commercial applications and helping to ensure beneficial outcomes for society. He serves on several boards including the Centre for Data Ethics and Innovation. Previously, Adrian held senior roles in finance.
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2020 Session: Orals & Spotlights Track 10: Social/Privacy »
Yanan Sui · Aurélien Bellet -
2019 : Audrey Durand, Douwe Kiela, Kamalika Chaudhuri moderated by Yann Dauphin »
Audrey Durand · Kamalika Chaudhuri · Yann Dauphin · Orhan Firat · Dilan Gorur · Douwe Kiela -
2019 : Kamalika Chaudhuri - A Three Sample Test to Detect Data Copying in Generative Models »
Kamalika Chaudhuri -
2019 : Poster Session »
Clement Canonne · Kwang-Sung Jun · Seth Neel · Di Wang · Giuseppe Vietri · Liwei Song · Jonathan Lebensold · Huanyu Zhang · Lovedeep Gondara · Ang Li · FatemehSadat Mireshghallah · Jinshuo Dong · Anand D Sarwate · Antti Koskela · Joonas Jälkö · Matt Kusner · Dingfan Chen · Mi Jung Park · Ashwin Machanavajjhala · Jayashree Kalpathy-Cramer · · Vitaly Feldman · Andrew Tomkins · Hai Phan · Hossein Esfandiari · Mimansa Jaiswal · Mrinank Sharma · Jeff Druce · Casey Meehan · Zhengli Zhao · Hsiang Hsu · Davis Railsback · Abraham Flaxman · · Julius Adebayo · Aleksandra Korolova · Jiaming Xu · Naoise Holohan · Samyadeep Basu · Matthew Joseph · My Thai · Xiaoqian Yang · Ellen Vitercik · Michael Hutchinson · Chenghong Wang · Gregory Yauney · Yuchao Tao · Chao Jin · Si Kai Lee · Audra McMillan · Rauf Izmailov · Jiayi Guo · Siddharth Swaroop · Tribhuvanesh Orekondy · Hadi Esmaeilzadeh · Kevin Procopio · Alkis Polyzotis · Jafar Mohammadi · Nitin Agrawal -
2019 : QUOTIENT: Two-Party Secure Neural Network Training & Prediction »
Nitin Agrawal · Matt Kusner · Adria Gascon -
2019 Workshop: Privacy in Machine Learning (PriML) »
Borja Balle · Kamalika Chaudhuri · Antti Honkela · Antti Koskela · Casey Meehan · Mi Jung Park · Mary Anne Smart · Mary Anne Smart · Adrian Weller -
2019 : Poster Session »
Jonathan Scarlett · Piotr Indyk · Ali Vakilian · Adrian Weller · Partha P Mitra · Benjamin Aubin · Bruno Loureiro · Florent Krzakala · Lenka Zdeborová · Kristina Monakhova · Joshua Yurtsever · Laura Waller · Hendrik Sommerhoff · Michael Moeller · Rushil Anirudh · Shuang Qiu · Xiaohan Wei · Zhuoran Yang · Jayaraman Thiagarajan · Salman Asif · Michael Gillhofer · Johannes Brandstetter · Sepp Hochreiter · Felix Petersen · Dhruv Patel · Assad Oberai · Akshay Kamath · Sushrut Karmalkar · Eric Price · Ali Ahmed · Zahra Kadkhodaie · Sreyas Mohan · Eero Simoncelli · Carlos Fernandez-Granda · Oscar Leong · Wesam Sakla · Rebecca Willett · Stephan Hoyer · Jascha Sohl-Dickstein · Sam Greydanus · Gauri Jagatap · Chinmay Hegde · Michael Kellman · Jonathan Tamir · Nouamane Laanait · Ousmane Dia · Mirco Ravanelli · Jonathan Binas · Negar Rostamzadeh · Shirin Jalali · Tiantian Fang · Alex Schwing · Sébastien Lachapelle · Philippe Brouillard · Tristan Deleu · Simon Lacoste-Julien · Stella Yu · Arya Mazumdar · Ankit Singh Rawat · Yue Zhao · Jianshu Chen · Xiaoyang Li · Hubert Ramsauer · Gabrio Rizzuti · Nikolaos Mitsakos · Dingzhou Cao · Thomas Strohmer · Yang Li · Pei Peng · Gregory Ongie -
2019 Workshop: Workshop on Human-Centric Machine Learning »
Plamen P Angelov · Nuria Oliver · Adrian Weller · Manuel Rodriguez · Isabel Valera · Silvia Chiappa · Hoda Heidari · Niki Kilbertus -
2019 Poster: Oblivious Sampling Algorithms for Private Data Analysis »
Olga Ohrimenko · Sajin Sasy -
2019 Poster: Privacy Amplification by Mixing and Diffusion Mechanisms »
Borja Balle · Gilles Barthe · Marco Gaboardi · Joseph Geumlek -
2019 Poster: An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors »
Janardhan Kulkarni · Olga Ohrimenko · Bolin Ding · Sergey Yekhanin · Joshua Allen · Harsha Nori -
2019 Poster: Differentially Private Markov Chain Monte Carlo »
Mikko Heikkilä · Joonas Jälkö · Onur Dikmen · Antti Honkela -
2019 Spotlight: Differentially Private Markov Chain Monte Carlo »
Mikko Heikkilä · Joonas Jälkö · Onur Dikmen · Antti Honkela -
2019 Poster: The Label Complexity of Active Learning from Observational Data »
Songbai Yan · Kamalika Chaudhuri · Tara Javidi -
2019 Poster: Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models »
Yunfei Teng · Wenbo Gao · François Chalus · Anna Choromanska · Donald Goldfarb · Adrian Weller -
2019 Poster: Capacity Bounded Differential Privacy »
Kamalika Chaudhuri · Jacob Imola · Ashwin Machanavajjhala -
2018 : Poster Session »
Phillipp Schoppmann · Patrick Yu · Valerie Chen · Travis Dick · Marc Joye · Ningshan Zhang · Frederik Harder · Olli Saarikivi · Théo Ryffel · Yunhui Long · Théo JOURDAN · Di Wang · Antonio Marcedone · Negev Shekel Nosatzki · Yatharth A Dubey · Antti Koskela · Peter Bloem · Aleksandra Korolova · Martin Bertran · Hao Chen · Galen Andrew · Natalia Martinez · Janardhan Kulkarni · Jonathan Passerat-Palmbach · Guillermo Sapiro · Amrita Roy Chowdhury -
2018 : Invited talk 3: Challenges in the Privacy-Preserving Analysis of Structured Data »
Kamalika Chaudhuri -
2018 : Plenary Talk 2 »
Kamalika Chaudhuri -
2018 Workshop: Machine Learning Open Source Software 2018: Sustainable communities »
Heiko Strathmann · Viktor Gal · Ryan Curtin · Antti Honkela · Sergey Lisitsyn · Cheng Soon Ong -
2018 Workshop: Privacy Preserving Machine Learning »
Adria Gascon · Aurélien Bellet · Niki Kilbertus · Olga Ohrimenko · Mariana Raykova · Adrian Weller -
2018 : Aurélien Bellet »
Aurélien Bellet -
2018 Workshop: Workshop on Security in Machine Learning »
Nicolas Papernot · Jacob Steinhardt · Matt Fredrikson · Kamalika Chaudhuri · Florian Tramer -
2018 Poster: Geometrically Coupled Monte Carlo Sampling »
Mark Rowland · Krzysztof Choromanski · François Chalus · Aldo Pacchiano · Tamas Sarlos · Richard Turner · Adrian Weller -
2018 Spotlight: Geometrically Coupled Monte Carlo Sampling »
Mark Rowland · Krzysztof Choromanski · François Chalus · Aldo Pacchiano · Tamas Sarlos · Richard Turner · Adrian Weller -
2018 Poster: Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences »
Borja Balle · Gilles Barthe · Marco Gaboardi -
2017 : Personalized and Private Peer-to-Peer Machine Learning »
Aurélien Bellet · Rachid Guerraoui · Marc Tommasi -
2017 : Invited talk: Differential privacy and Bayesian learning »
Antti Honkela -
2017 : Poster Session (encompasses coffee break) »
Beidi Chen · Borja Balle · Daniel Lee · iuri frosio · Jitendra Malik · Jan Kautz · Ke Li · Masashi Sugiyama · Miguel A. Carreira-Perpinan · Ramin Raziperchikolaei · Theja Tulabandhula · Yung-Kyun Noh · Adams Wei Yu -
2017 : Invited talk: Challenges for Transparency »
Adrian Weller -
2017 : Analyzing Robustness of Nearest Neighbors to Adversarial Examples »
Kamalika Chaudhuri -
2017 : Closing remarks »
Adrian Weller -
2017 Symposium: Kinds of intelligence: types, tests and meeting the needs of society »
José Hernández-Orallo · Zoubin Ghahramani · Tomaso Poggio · Adrian Weller · Matthew Crosby -
2017 Poster: From Parity to Preference-based Notions of Fairness in Classification »
Muhammad Bilal Zafar · Isabel Valera · Manuel Rodriguez · Krishna Gummadi · Adrian Weller -
2017 Poster: Renyi Differential Privacy Mechanisms for Posterior Sampling »
Joseph Geumlek · Shuang Song · Kamalika Chaudhuri -
2017 Poster: Approximation and Convergence Properties of Generative Adversarial Learning »
Shuang Liu · Olivier Bousquet · Kamalika Chaudhuri -
2017 Spotlight: Approximation and Convergence Properties of Generative Adversarial Learning »
Shuang Liu · Olivier Bousquet · Kamalika Chaudhuri -
2017 Poster: The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings »
Krzysztof Choromanski · Mark Rowland · Adrian Weller -
2017 Poster: Uprooting and Rerooting Higher-Order Graphical Models »
Mark Rowland · Adrian Weller -
2017 Poster: Hierarchical Methods of Moments »
Matteo Ruffini · Guillaume Rabusseau · Borja Balle -
2017 Poster: Multitask Spectral Learning of Weighted Automata »
Guillaume Rabusseau · Borja Balle · Joelle Pineau -
2017 Poster: Differentially private Bayesian learning on distributed data »
Mikko Heikkilä · Eemil Lagerspetz · Samuel Kaski · Kana Shimizu · Sasu Tarkoma · Antti Honkela -
2017 Tutorial: Differentially Private Machine Learning: Theory, Algorithms and Applications »
Kamalika Chaudhuri · Anand D Sarwate -
2016 Workshop: Private Multi-Party Machine Learning »
Borja Balle · Aurélien Bellet · David Evans · Adrià Gascón -
2016 Workshop: Reliable Machine Learning in the Wild »
Dylan Hadfield-Menell · Adrian Weller · David Duvenaud · Jacob Steinhardt · Percy Liang -
2016 Symposium: Machine Learning and the Law »
Adrian Weller · Thomas D. Grant · Conrad McDonnell · Jatinder Singh -
2016 Poster: On Graph Reconstruction via Empirical Risk Minimization: Fast Learning Rates and Scalability »
Guillaume Papa · Aurélien Bellet · Stephan Clémençon -
2016 Poster: Active Learning from Imperfect Labelers »
Songbai Yan · Kamalika Chaudhuri · Tara Javidi -
2015 : Genome-wide modelling of transcription kinetics reveals patterns of RNA production delays »
Antti Honkela -
2015 : Kamalika Chaudhuri »
Kamalika Chaudhuri -
2015 Workshop: Non-convex Optimization for Machine Learning: Theory and Practice »
Anima Anandkumar · Niranjan Uma Naresh · Kamalika Chaudhuri · Percy Liang · Sewoong Oh -
2015 Symposium: Algorithms Among Us: the Societal Impacts of Machine Learning »
Michael A Osborne · Adrian Weller · Murray Shanahan -
2015 Poster: Active Learning from Weak and Strong Labelers »
Chicheng Zhang · Kamalika Chaudhuri -
2015 Poster: Spectral Learning of Large Structured HMMs for Comparative Epigenomics »
Chicheng Zhang · Jimin Song · Kamalika Chaudhuri · Kevin Chen -
2015 Poster: Convergence Rates of Active Learning for Maximum Likelihood Estimation »
Kamalika Chaudhuri · Sham Kakade · Praneeth Netrapalli · Sujay Sanghavi -
2015 Poster: SGD Algorithms based on Incomplete U-statistics: Large-Scale Minimization of Empirical Risk »
Guillaume Papa · Stéphan Clémençon · Aurélien Bellet -
2015 Poster: Extending Gossip Algorithms to Distributed Estimation of U-statistics »
Igor Colin · Aurélien Bellet · Joseph Salmon · Stéphan Clémençon -
2015 Spotlight: Extending Gossip Algorithms to Distributed Estimation of U-statistics »
Igor Colin · Aurélien Bellet · Joseph Salmon · Stéphan Clémençon -
2014 Poster: Clamping Variables and Approximate Inference »
Adrian Weller · Tony Jebara -
2014 Oral: Clamping Variables and Approximate Inference »
Adrian Weller · Tony Jebara -
2014 Poster: Beyond Disagreement-Based Agnostic Active Learning »
Chicheng Zhang · Kamalika Chaudhuri -
2014 Poster: Rates of Convergence for Nearest Neighbor Classification »
Kamalika Chaudhuri · Sanjoy Dasgupta -
2014 Spotlight: Beyond Disagreement-Based Agnostic Active Learning »
Chicheng Zhang · Kamalika Chaudhuri -
2014 Spotlight: Rates of Convergence for Nearest Neighbor Classification »
Kamalika Chaudhuri · Sanjoy Dasgupta -
2014 Poster: The Large Margin Mechanism for Differentially Private Maximization »
Kamalika Chaudhuri · Daniel Hsu · Shuang Song -
2013 Workshop: Machine Learning Open Source Software: Towards Open Workflows »
Antti Honkela · Cheng Soon Ong -
2013 Poster: A Stability-based Validation Procedure for Differentially Private Machine Learning »
Kamalika Chaudhuri · Staal A Vinterbo -
2012 Poster: Near-optimal Differentially Private Principal Components »
Kamalika Chaudhuri · Anand D Sarwate · Kaushik Sinha -
2011 Poster: Spectral Methods for Learning Multivariate Latent Tree Structure »
Anima Anandkumar · Kamalika Chaudhuri · Daniel Hsu · Sham M Kakade · Le Song · Tong Zhang -
2010 Poster: Rates of convergence for the cluster tree »
Kamalika Chaudhuri · Sanjoy Dasgupta -
2009 Poster: A Parameter-free Hedging Algorithm »
Kamalika Chaudhuri · Yoav Freund · Daniel Hsu -
2008 Poster: Privacy-preserving logistic regression »
Kamalika Chaudhuri · Claire Monteleoni