Timezone: »

 
Workshop
Privacy Preserving Machine Learning - PriML and PPML Joint Edition
Borja Balle · James Bell · Aurélien Bellet · Kamalika Chaudhuri · Adria Gascon · Antti Honkela · Antti Koskela · Casey Meehan · Olga Ohrimenko · Mi Jung Park · Mariana Raykova · Mary Anne Smart · Yu-Xiang Wang · Adrian Weller

Fri Dec 11 01:20 AM -- 01:25 PM (PST) @ None
in Workshops Fri »
Event URL: https://ppml-workshop.github.io/ »

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.
Welcome & Introduction (Live Intro)
Fri 1:30 a.m. - 2:00 a.m.
Invited Talk #1: Reza Shokri (National University of Singapore) (Invited Talk) Video
Reza Shokri
Fri 2:00 a.m. - 2:30 a.m.
Invited Talk #2: Katrina Ligett (Hebrew University) (Invited Talk) Video
Katrina Ligett
Fri 2:30 a.m. - 3:00 a.m.
Invited Talk Q&A with Reza and Katrina (Q&A Session)
Fri 3:00 a.m. - 3:10 a.m.
Break
Fri 3:10 a.m. - 3:25 a.m.
Contributed Talk #1: POSEIDON: Privacy-Preserving Federated Neural Network Learning (Oral) Video
Sinem Sav
Fri 3:25 a.m. - 3:30 a.m.
Contributed Talk Q&A (Q&A Session)
Fri 3:30 a.m. - 5:00 a.m.
Poster Session & Social on Gather.Town (Poster Session)
Fri 8:30 a.m. - 8:40 a.m.
Welcome & Introduction (Live Intro)
Fri 8:40 a.m. - 9:00 a.m.
Invited Talk #3: Carmela Troncoso (EPFL) (Invited Talk) Video
Carmela Troncoso
Fri 9:00 a.m. - 9:30 a.m.
Invited Talk #4: Dan Boneh (Stanford University) (Invited Talk) Video
Dan Boneh
Fri 9:30 a.m. - 10:00 a.m.
Invited Talk Q&A with Carmela and Dan (Q&A Session)
Fri 10:00 a.m. - 10:10 a.m.
Break
Fri 10:10 a.m. - 11:10 a.m.
Poster Session & Social on Gather.Town (Poster Session)
Fri 11:10 a.m. - 11:20 a.m.
Break
Fri 11:20 a.m. - 11:35 a.m.
Contributed Talk #2: On the (Im)Possibility of Private Machine Learning through Instance Encoding (Oral)
Nicholas Carlini
Fri 11:35 a.m. - 11:50 a.m.
Contributed Talk #3: Poirot: Private Contact Summary Aggregation (Oral) Video
Chenghong Wang
Fri 11:50 a.m. - 12:05 p.m.
Contributed Talk #4: Greenwoods: A Practical Random Forest Framework for Privacy Preserving Training and Prediction (Oral) Video
Harsh Chaudhari
Fri 12:05 p.m. - 12:20 p.m.
Contributed Talks Q&A (Q&A Session)
Fri 12:20 p.m. - 12:25 p.m.
Break
Fri 12:25 p.m. - 12:40 p.m.
Contributed Talk #5: Shuffled Model of Federated Learning: Privacy, Accuracy, and Communication Trade-offs (Oral) Video
Deepesh Data
Fri 12:40 p.m. - 12:55 p.m.
Contributed Talk #6: Sample-efficient proper PAC learning with approximate differential privacy (Oral) Video
Badih Ghazi
Fri 12:55 p.m. - 1:10 p.m.
Contributed Talk #7: Training Production Language Models without Memorizing User Data (Oral) Video
Swaroop Ramaswamy, Om Thakkar
Fri 1:10 p.m. - 1:25 p.m.
Contributed Talks Q&A (Q&A Session)
-
Towards General-purpose Infrastructure for Protecting Scientific Data Under Study (Poster)
Kritika Prakash
-
Robust and Private Learning of Halfspaces (Poster) [ Video ] Video
Badih Ghazi
-
Randomness Beyond Noise: Differentially Private Optimization Improvement through Mixup (Poster) [ Video ] Video
Hanshen Xiao
-
Generative Adversarial User Privacy in Lossy Single-Server Information Retrieval (Poster) [ Video ] Video
Mark Weng
-
Privacy Preserving Chatbot Conversations (Poster) [ Video ] Video
Debmalya Biswas
-
Distributed Differentially Private Averaging with Improved Utility and Robustness to Malicious Parties (Poster) [ Video ] Video
Aurélien Bellet
-
Twinify: A software package for differentially private data release (Poster) [ Video ] Video
Joonas Jälkö
-
DAMS: Meta-estimation of private sketch data structures for differentially private contact tracing (Poster)
Praneeth Vepakomma
-
Secure Medical Image Analysis with CrypTFlow (Poster) [ Video ] Video
Javier Alvarez-Valle
-
Multi-Headed Global Model for handling Non-IID data (Poster)
Himanshu Arora
-
Individual Privacy Accounting via a Rényi Filter (Poster) [ Video ] Video
Vitaly Feldman
-
Does Domain Generalization Provide Inherent Membership Privacy (Poster) [ Video ] Video
Divyat Mahajan
-
Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling (Poster) [ Video ] Video
Vitaly Feldman
-
SparkFHE: Distributed Dataflow Framework with Fully Homomorphic Encryption (Poster) [ Video ] Video
Peizhao Hu
-
Enabling Fast Differentially Private SGD via Static Graph Compilation and Batch-Level Parallelism (Poster) [ Video ] Video
Pranav Subramani
-
Local Differentially Private Regret Minimization in Reinforcement Learning (Poster) [ Video ] Video
Evrard Garcelon
-
SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning (Poster) [ Video ] Video
Nishat Koti
-
Differentially Private Stochastic Coordinate Descent (Poster) [ Video ] Video
Georgios Damaskinos
-
MP2ML: A Mixed-Protocol Machine LearningFramework for Private Inference (Poster) [ Video ] Video
Fabian Boemer
-
Dataset Inference: Ownership Resolution in Machine Learning (Poster) [ Video ] Video
Nicolas Papernot
-
Privacy-preserving XGBoost Inference (Poster) [ Video ] Video
Xianrui Meng
-
New Challenges for Fully Homomorphic Encryption (Poster) [ Video ] Video
Marc Joye
-
Differentially Private Bayesian Inference For GLMs (Poster) [ Video ] Video
Joonas Jälkö
-
Robustness Threats of Differential Privacy (Poster)
Ivan Oseledets
-
Asymmetric Private Set Intersection with Applications to Contact Tracing and Private Vertical Federated Machine Learning (Poster) [ Video ] Video
Bogdan Cebere
-
Characterizing Private Clipped Gradient Descent on Convex Generalized Linear Problems (Poster) [ Video ] Video
Shuang Song
-
Adversarial Attacks and Countermeasures on Private Training in MPC (Poster)
Matthew Jagielski
-
Optimal Client Sampling for Federated Learning (Poster) [ Video ] Video
Samuel Horváth
-
Data Appraisal Without Data Sharing (Poster) [ Video ] Video
Mimee Xu
-
Dynamic Channel Pruning for Privacy (Poster)
Abhishek Singh
-
Mitigating Leakage in Federated Learning with Trusted Hardware (Poster) [ Video ] Video
Javad Ghareh Chamani
-
Unifying Privacy Loss for Data Analytics (Poster) [ Video ] Video
Ryan Rogers
-
Differentially Private Generative Models Through Optimal Transport (Poster) [ Video ] Video
Karsten Kreis
-
A Principled Approach to Learning Stochastic Representations for Privacy in Deep Neural Inference (Poster) [ Video ] Video
FatemehSadat Mireshghallah
-
Challenges of Differentially Private Prediction in Healthcare Settings (Poster)
Nicolas Papernot
-
Machine Learning with Membership Privacy via Knowledge Transfer (Poster) [ Video ] Video
Virat Shejwalkar
-
Secure Single-Server Aggregation with (Poly)Logarithmic Overhead (Poster)
James Bell
-
PrivAttack: A Membership Inference AttackFramework Against Deep Reinforcement LearningAgents (Poster) [ Video ] Video
maziar gomrokchi
-
Effectiveness of MPC-friendly Softmax Replacement (Poster) [ Video ] Video
Marcel Keller
-
Revisiting Membership Inference Under Realistic Assumptions (Poster)
Bargav Jayaraman
-
DYSAN: Dynamically sanitizing motion sensor data against sensitive inferences through adversarial networks (Poster) [ Video ] Video
Théo JOURDAN
-
Fairness in the Eyes of the Data: Certifying Machine-Learning Models (Poster) [ Video ] Video
Carsten Baum
-
Privacy in Multi-armed Bandits: Fundamental Definitions and Lower Bounds on Regret (Poster) [ Video ] Video
Debabrota Basu
-
Accuracy, Interpretability and Differential Privacy via Explainable Boosting (Poster) [ Video ] Video
Harsha Nori
-
Privacy Amplification by Decentralization (Poster) [ Video ] Video
Aurélien Bellet
-
Privacy Risks in Embedded Deep Learning (Poster) [ Video ] Video
Virat Shejwalkar
-
Quantifying Privacy Leakage in Graph Embedding (Poster) [ Video ] Video
Antoine Boutet
-
Understanding Unintended Memorization in Federated Learning (Poster) [ Video ] Video
Om Thakkar
-
Network Generation with Differential Privacy (Poster) [ Video ] Video
Xu Zheng
-
Privacy Regularization: Joint Privacy-Utility Optimization in Language Models (Poster) [ Video ] Video
FatemehSadat Mireshghallah
-
Tight Approximate Differential Privacy for Discrete-Valued Mechanisms Using FFT (Poster) [ Video ] Video
Antti Koskela
-
Differentially private cross-silo federated learning (Poster) [ Video ] Video
Mikko Heikkilä
-
CrypTen: Secure Multi-Party Computation Meets Machine Learning (Poster)
Shubho Sengupta
-
On Polynomial Approximations for Privacy-Preserving and Verifiable ReLU Networks (Poster) [ Video ] Video
Salman Avestimehr
-
Data-oblivious training for XGBoost models (Poster) [ Video ] Video
Chester Leung
-
Privacy Attacks on Machine Unlearning (Poster) [ Video ] Video
Ji Gao
-
SOTERIA: In Search of Efficient Neural Networks for Private Inference (Poster) [ Video ] Video
Reza Shokri
-
On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians (Poster) [ Video ] 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 Senior Research Fellow in Machine Learning at the University of Cambridge, and at the Leverhulme Centre for the Future of Intelligence where he leads the project on Trust and Transparency. 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.

More from the Same Authors