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Workshop

Theory and Practice of Differential Privacy

Rachel Cummings ⋅ Gautam Kamath
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Abstract

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Schedule

Timezone: Asia/Seoul
11:00 PM
Opening Remarks
Gautam Kamath ⋅ Rachel Cummings
11:45 PM
Poster Session 1
12:30 AM
Break
12:55 AM
Contributed Talks Session 1
Marika Swanberg ⋅ Samuel Haney ⋅ Peter Kairouz
2:40 AM
Contributed Talks Session 2
Saeed Sharifi-Malvajerdi ⋅ Audra McMillan ⋅ Ryan McKenna
4:10 AM
Break
6:00 AM
Social Hour
The Shape of Edge Differential Privacy
Siddharth Vishwanath ⋅ Jonathan Hehir
Flexible Accuracy for Differential Privacy
Aman Bansal ⋅ Rahul Chunduru ⋅ Deepesh Data ⋅ Manoj Prabhakaran
Differentially private sparse vectors with low error, optimal space, and fast access
Martin Aumüller ⋅ Christian Lebeda ⋅ Rasmus Pagh
Reproducibility in Learning
Russell Impagliazzo ⋅ Rex Lei ⋅ Jessica Sorrell
Robust and Differentially Private Covariance Estimation
Logan Gnanapragasam ⋅ Jonathan Hayase ⋅ Sewoong Oh
Non-Euclidean Differentially Private Stochastic Convex Optimization
Raef Bassily ⋅ Cristobal Guzman ⋅ Anupama Nandi
Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization
Pranav Subramani ⋅ Nicholas Vadivelu ⋅ Gautam Kamath
Randomized Response with Prior and Applications to Learning with Label Differential Privacy
Badih Ghazi ⋅ Noah Golowich ⋅ Ravi Kumar ⋅ Pasin Manurangsi ⋅ Chiyuan Zhang
Remember What You Want to Forget: Algorithms for Machine Unlearning
Ayush Sekhari ⋅ Ayush Sekhari ⋅ Jayadev Acharya ⋅ Gautam Kamath ⋅ Ananda Theertha Suresh
Differentially Private Bayesian Neural Network
Zhiqi Bu ⋅ Qiyiwen Zhang ⋅ Kan Chen ⋅ Qi Long
Concurrent Composition of Differential Privacy
Salil Vadhan ⋅ Tianhao Wang
User-Level Private Learning via Correlated Sampling
Badih Ghazi ⋅ Ravi Kumar ⋅ Pasin Manurangsi
Understanding Clipped FedAvg: Convergence and Client-Level Differential Privacy
xinwei zhang ⋅ Xiangyi Chen ⋅ Steven Wu ⋅ Mingyi Hong
Optimal Accounting of Differential Privacy via Characteristic Function
Yuqing Zhu ⋅ Jinshuo Dong ⋅ Yu-Xiang Wang
Bounded Space Differentially Private Quantiles
Daniel Alabi ⋅ Omri Ben-Eliezer ⋅ Anamay Chaturvedi
Benchmarking Differentially Private Graph Algorithms
Huiyi Ning ⋅ Sreeharsha Udayashankar ⋅ Sara Qunaibi ⋅ Karl Knopf ⋅ Xi He
Membership Inference Attacks are More Powerful Against Updated Models
Matthew Jagielski ⋅ Stanley Wu ⋅ Alina Oprea ⋅ Jonathan Ullman ⋅ Roxana Geambasu
Privacy-induced experimentation and private causal inference
Leon Yao ⋅ Naoise Holohan ⋅ David Arbour ⋅ Dean Eckles
The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection
Shubhankar Mohapatra ⋅ Shubhankar Mohapatra ⋅ Sajin Sasy ⋅ Gautam Kamath ⋅ Xi He ⋅ Om Dipakbhai Thakkar
Nonparametric Differentially Private Confidence Intervals for the Median
Joerg Drechsler ⋅ Ira Globus-Harris ⋅ Audra McMillan ⋅ Adam Smith ⋅ Jayshree Sarathy
On the Convergence of Deep Learning with Differential Privacy
Zhiqi Bu ⋅ Hua Wang ⋅ Qi Long ⋅ Weijie Su
On the Renyi Differential Privacy of the Shuffle Model
Antonious Girgis ⋅ Deepesh Data ⋅ Suhas Diggavi ⋅ Ananda Theertha Suresh ⋅ Peter Kairouz
Improved Privacy Filters and Odometers: Time-Uniform Bounds in Privacy Composition
Justin Whitehouse ⋅ Aaditya Ramdas ⋅ Ryan Rogers ⋅ Steven Wu
Mean Estimation with User-level Privacy under Data Heterogeneity
Rachel Cummings ⋅ Vitaly Feldman ⋅ Audra McMillan ⋅ Kunal Talwar
Unbiased Statistical Estimation and Valid Confidence Sets Under Differential Privacy
Christian Covington ⋅ Xi He ⋅ James Honaker ⋅ Gautam Kamath
Practical and Private (Deep) Learning without Sampling orShuffling
Peter Kairouz ⋅ Hugh B McMahan ⋅ Shuang Song ⋅ Om Dipakbhai Thakkar ⋅ Abhradeep Guha Thakurta ⋅ Zheng Xu
Multiclass versus Binary Differentially Private PAC Learning
Satchit Sivakumar ⋅ Mark Bun ⋅ Marco Gaboradi
Privacy Amplification by Bernoulli Sampling
Jacob Imola ⋅ Kamalika Chaudhuri
Label differential privacy via clustering
Hossein Esfandiari ⋅ Vahab Mirrokni ⋅ Umar Syed ⋅ Sergei Vassilvitskii
Learning with User-Level Privacy
Daniel A Levy ⋅ Ziteng Sun ⋅ Kareem Amin ⋅ Satyen Kale ⋅ Alex Kulesza ⋅ Mehryar Mohri ⋅ Ananda Theertha Suresh
A Shuffling Framework For Local Differential Privacy
Casey M Meehan ⋅ Amrita Roy Chowdhury ⋅ Kamalika Chaudhuri ⋅ Somesh Jha
Private Boosted Decision Trees via Smooth Re-Weighting: Simplicity is Useful
Marco Carmosino ⋅ Vahid Reza Asadi ⋅ Mohammad Mahdi Jahanara ⋅ Akbar Rafiey ⋅ Bahar Salamatian
Prior-Aware Distribution Estimation for Differential Privacy
Yuchao Tao ⋅ Johes Bater ⋅ Ashwin Machanavajjhala
Differentially Private Model Personalization
Prateek Jain ⋅ J K Rush ⋅ Adam Smith ⋅ Shuang Song ⋅ Abhradeep Guha Thakurta
Adaptive Machine Unlearning
Varun Gupta ⋅ Christopher Jung ⋅ Seth Neel ⋅ Aaron Roth ⋅ Saeed Sharifi-Malvajerdi ⋅ Chris Waites
When Is Memorization of Irrelevant Training Data Necessary for High-Accuracy Learning?
Gavin Brown ⋅ Mark Bun ⋅ Vitaly Feldman ⋅ Adam Smith ⋅ Kunal Talwar
Differentially Private Sampling from Distributions
Satchit Sivakumar ⋅ Marika Swanberg ⋅ Sofya Raskhodnikova ⋅ Adam Smith
Tight Accounting in the Shuffle Model of Differential Privacy
Antti Koskela ⋅ Mikko A Heikkilä ⋅ Antti Honkela
Differentially Private Hamiltonian Monte Carlo
Ossi Räisä ⋅ Antti Koskela ⋅ Antti Honkela
A Practitioners Guide to Differentially Private Convex Optimization
Ryan McKenna ⋅ Hristo Paskov ⋅ Kunal Talwar
Privately Learning Subspaces
Vikrant Singhal ⋅ Thomas Steinke
Differentially Private Quantiles
Jennifer Gillenwater ⋅ Matthew Joseph ⋅ Alex Kulesza
Shuffle Private Stochastic Convex Optimization
Albert Cheu ⋅ Matthew Joseph ⋅ Jieming Mao ⋅ Binghui Peng
Covariance-Aware Private Mean Estimation Without Private Covariance Estimation
Gavin Brown ⋅ Marco Gaboradi ⋅ Adam Smith ⋅ Jonathan Ullman ⋅ Lydia Zakynthinou
Improving Privacy-Preserving Deep Learning With Immediate Sensitivity
Timothy Stevens ⋅ David Darais ⋅ Ben U Gelman ⋅ David Slater ⋅ Joseph Near
Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates
Steve Chien ⋅ Prateek Jain ⋅ Walid Krichene ⋅ Steffen Rendle ⋅ Shuang Song ⋅ Abhradeep Guha Thakurta ⋅ Li Zhang
Sensitivity analysis in differentially private machine learning using hybrid automatic differentiation
Alexander Ziller ⋅ Dmitrii Usynin ⋅ Moritz Knolle ⋅ Kritika Prakash ⋅ Andrew Trask ⋅ Marcus Makowski ⋅ Rickmer Braren ⋅ Daniel Rueckert ⋅ Georgios Kaissis
Consistent Spectral Clustering of Network Block Models under Local Differential Privacy
Jonathan Hehir ⋅ Aleksandra Slavković ⋅ Xiaoyue Niu
Solo: Enforcing Differential Privacy Without Fancy Types
Chike Abuah ⋅ David Darais ⋅ Joseph Near
Differentially Private Algorithms for 2020 Decennial Census Detailed DHC Race \& Ethnicity
Samuel Haney ⋅ William Sexton ⋅ Ashwin Machanavajjhala ⋅ Michael Hay ⋅ Gerome Miklau
PEARL: Data Synthesis via Private Embeddings and Adversarial Reconstruction Learning
Seng Pei Liew ⋅ Tsubasa Takahashi ⋅ Michihiko Ueno
Differentially private training of neural networks with Langevin dynamics for calibrated predictive uncertainty
Moritz Knolle ⋅ Alexander Ziller ⋅ Dmitrii Usynin ⋅ Rickmer Braren ⋅ Marcus Makowski ⋅ Daniel Rueckert ⋅ Georgios Kaissis
Privately Learning Mixtures of Axis-Aligned Gaussians
Ishaq Aden-Ali ⋅ Hassan Ashtiani ⋅ Christopher Liaw
Privacy Amplification by Subsampling in Time Domain
Tatsuki Koga ⋅ Casey M Meehan ⋅ Kamalika Chaudhuri
Formalizing Distribution Inference Risks
Anshuman Suri ⋅ Anshuman Suri ⋅ David Evans
TEM: High Utility Metric Differential Privacy on Text
Ricardo Silva Carvalho ⋅ Theodore Vasiloudis ⋅ Oluwaseyi Feyisetan
Differentially Private Algorithms for Graphs UnderContinual Observation
Hendrik Fichtenberger ⋅ Monika Henzinger ⋅ Wolfgang Ost
Wide Network Learning with Differential Privacy
Huanyu Zhang ⋅ Ilya Mironov ⋅ Meisam Hejazinia
A Members First Approach to Enabling LinkedIn's Labor Market Insights at Scale
Ryan Rogers ⋅ Adrian Rivera Cardoso ⋅ Koray Mancuhan ⋅ Akash Kaura ⋅ Nikhil Gahlawat ⋅ Neha Jain ⋅ Paul Ko ⋅ Parvez Ahammad