TPDP 2020 is a workshop focused on differential privacy. As such, it’s a great place to learn about recent developments in the DP research community. It will be held on 13 November and is co-located with CCS, but, of course, it’s virtual this year. Registration is only US$35 if you register by Friday, 30 October. Check out the 8 excellent talks and 71 posters below – wow, the workshop has grown!

Please let us know if there are any errors or omissions.

Invited Talks

• OpenDP: A Community Effort to Build Trustworthy Differential Privacy Software.
  Salil Vadhan

• Implementation with Base-2 DP or: How I learned to stop worrying and love floating point. Christina Ilvento

Contributed Talks

• Private Reinforcement Learning with PAC and Regret Guarantees
  Giuseppe Vietri, Borja Balle, Akshay Krishnamurthy, Z. Steven Wu

• Auditing Differentially Private Machine Learning: How Private is Private SGD?
  Matthew Jagielski, Jonathan Ullman, Alina Oprea

• Characterizing Private Clipped Gradient Descent on Convex Generalized Linear Problems
  Shuang Song, Om Thakkar, Abhradeep Thakurta

• Private Query Release Assisted by Public Data
  Raef Bassily, Albert Cheu, Shay Moran, Aleksandar Nikolov, Jonathan Ullman, Z. Steven Wu

• An Equivalence Between Private Classification and Online Prediction
  Mark Bun, Roi Livni, Shay Moran

• Differentially Private Set Union
  Sivakanth Gopi, Pankaj Gulhane, Janardhan Kulkarni, Judy Hanwen Shen, Milad Shokouhi, Sergey Yekhanin

Posters

• The Discrete Gaussian for Differential Privacy
  Clément Canonne, Gautam Kamath, Thomas Steinke

• Locally Private Hypothesis Selection
  Sivakanth Gopi, Gautam Kamath, Janardhan Kulkarni, Aleksandar Nikolov, Z. Steven Wu, Huanyu Zhang

• LinkedIn’s Audience Engagements API: A Privacy Preserving Data Analytics System at Scale
  Ryan Rogers, Subbu Subramaniam, Sean Peng, David Durfee, Seunghyun Lee, Santosh Kumar Kancha, Shraddha Sahay, Parvez Ahammad

• Differentially Private Decomposable Submodular Maximization
  Anamay Chaturvedi, Huy Nguyen, Lydia Zakynthinou

• Private Post-GAN Boosting
  Marcel Neunhoeffer, Z. Steven Wu, Cynthia Dwork

• Efficient, Noise-Tolerant, and Private Learning via Boosting
  Marco Carmosino, Mark Bun, Jessica Sorrell

• Closure Properties for Private Classification and Online Prediction
  by Noga Alon, Amos Beimel, Shay Moran, Uri Stemmer

• Overlook: Differentially Private Exploratory Visualization for Big Data
  Pratiksha Thaker, Mihai Budiu, Parikshit Gopalan, Udi Wieder, Matei Zaharia

• On the Equivalence between Online and Private Learnability beyond Binary Classification
  Young Hun Jung, Baekjin Kim and Ambuj Tewari

• Cache Me If You Can: Accuracy-Aware Inference Engine for Differentially Private Data Exploration
  Miti Mazmudar, Thomas Humphries, Matthew Rafuse, Xi He

• Bounded Leakage Differential Privacy
  Katrina Ligett, Charlotte Peale, Omer Reingold

• A Knowledge Transfer Framework for Differentially Private Sparse Learning
  Lingxiao Wang, Quanquan Gu

• Consistent Integer, Non-Negative, Hierarchical Histograms without Integer Programming
  Cynthia Dwork, Christina Ilvento

• An Empirical Study on the Intrinsic Privacy of Stochastic Gradient Descent
  Stephanie Hyland, Shruti Tople

• Encode, Shuffle, Analyze Privacy Revisited: Formalizations and Empirical Evaluation
  Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Shuang Song, Kunal Talwar, Abhradeep Thakurta

• Improving Sparse Vector Technique with Renyi Differential Privacy
  Yuqing Zhu and Yu-Xiang Wang

• Breaking the Communication-Privacy-Accuracy Trilemma
  Wei-Ning Chen, Peter Kairouz, Ayfer Özgür

• Budget Sharing for Multi-Analyst Differential Privacy
  David Pujol, Yikai Wu, Brandon Fain, Ashwin Machanavajjhala

• AdaCliP: Adaptive Clipping for Private SGD
  Venkatadheeraj Pichapati, Ananda Theertha Suresh, Felix X. Yu, Sashank J. Reddi, Sanjiv Kumar

• Private Optimization Without Constraint Violation
  Andrés Muñoz Medina, Umar Syed, Sergei Vassilvitskii, Ellen Vitercik

• Efficient Per-Example Gradient Computations in Convolutional Neural Networks
  Gaspar Rochette, Andre Manoel, Eric Tramel

• Controlling Privacy Loss in Survey Sampling
  Audra McMillan, Mark Bun, Marco Gaboardi, Joerg Drechsler

• Privacy-Preserving Community Detection under the Stochastic Block Model
  Jonathan Hehir, Aleksandra Slavkovic, Xiaoyue Niu

• Smoothed Analysis of Differentially Private and Online Learning
  Nika Haghtalab, Tim Roughgarden, Abhishek Shetty

• Private Mean Estimation for Heavy-Tailed Distributions
  Gautam Kamath, Vikrant Singhal, Jonathan Ullman

• Private Posterior Inference Consistent with Public Information: a Case Study in Small Area Estimation from Synthetic Census Data
  Jeremy Seeman, Aleksandra Slavkovic, Matthew Reimherr

• Reasoning About Generalization via Conditional Mutual Information
  Thomas Steinke, Lydia Zakynthinou

• Understanding Gradient Clipping in Private SGD: A Geometric Perspective
  Xiangyi Chen, Z. Steven Wu, Mingyi Hong

• Privacy Amplification via Random Check-Ins
  Borja Balle, Peter Kairouz, Brendan McMahan, Om Thakkar, Abhradeep Thakurta

• Permute-and-flip: a new mechanism for differentially-private selection
  Ryan McKenna, Daniel Sheldon

• Descent-to-Delete: Gradient-Based Methods for Machine Unlearning
  Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi

• A Better Bound Gives a Hundred Rounds: Enhanced Privacy Guarantees via f-Divergences
  Shahab Asoodeh, Jiachun Liao, Flavio Calmon, Oliver Kosut, Lalitha Sankar

• Census TopDown and the Redistricting Use Case
  Aloni Cohen, Moon Duchin, JN Matthews, Bhushan Suwal, Peter Wayner

• Interaction is Necessary for Distributed Learning with Privacy or Communication Constraints
  Yuval Dagan, Vitaly Feldman

• Fisher information under local differential privacy
  Leighton Barnes, Wei-Ning Chen, Ayfer Ozgur

• Differentially Private Assouad, Fano, and Le Cam
  Jayadev Acharya, Ziteng Sun, Huanyu Zhang

• Learning discrete distributions: user vs item-level privacy
  Yuhan Liu, Ananda Theertha Suresh, Felix Yu, Sanjiv Kumar, Michael Riley

• Efficient Privacy-Preserving Stochastic Nonconvex Optimization
  Lingxiao Wang, Bargav Jayaraman, David Evans, Quanquan Gu

• Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification
  Yingxue Zhou, Zhiwei Steven Wu, Arindam Banerjee

• Differentially private partition selection
  Damien Desfontaines, Bryant Gipson, Chinmoy Mandayam, James Voss

• Differentially Private Clustering: Tight Approximation Ratios
  Badih Ghazi, Ravi Kumar, Pasin Manurangsi

• Let’s not make a fuzz about it
  Elisabet Lobo Vesga, Alejandro Russo, Marco Gaboardi

• PAPRIKA: Private Online False Discovery Rate Control
  Wanrong Zhang, Gautam Kamath, Rachel Cummings

• Oblivious Sampling Algorithms for Private Data Analysis
  Sajin Sasy, Olga Ohrimenko

• SOGDB-epsilon: Secure Outsourced Growing Database with Differentially Private Record Update
  Chenghong Wang, Kartik Nayak, Ashwin Machanavajjhala

• Differentially Private Normalizing Flows for Privacy-Preserving Density Estimation
  Chris Waites, Rachel Cummings

• Differentially Private Sublinear Average Degree Approximation
  Harry Sivasubramaniam, Haonan Li, Xi He

• Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning
  Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, Yu-Xiang Wang

• CoinPress: Practical Private Mean and Covariance Estimation
  Sourav Biswas, Yihe Dong, Gautam Kamath, Jonathan Ullman

• Connecting Robust Shuffle Privacy and Pan-Privacy
  Victor Balcer, Albert Cheu, Matthew Joseph, Jieming Mao

• Differentially Private Variational Autoencoders with Term-wise Gradient Aggregation
  Tsubasa Takahashi, Shun Takagi, Hajime Ono, Tatsuya Komatsu

• Understanding Unintended Memorization in Federated Learning
  Om Thakkar, Swaroop Ramaswamy, Rajiv Mathews, Francoise Beaufays

• Near Instance-Optimality in Differential Privacy
  Hilal Asi, John Duchi

• Implementing differentially private integer partitions via the exponential mechanism and Implementing Sparse Vector
  Christina Ilvento

• Differentially Private Simple Linear Regression
  Audra McMillan, Daniel Alabi, Jayshree Sarathy, Adam Smith, Salil Vadhan

• New Oracle-Efficient Algorithms for Private Synthetic Data Release
  Giuseppe Vietri, Grace Tian, Mark Bun, Thomas Steinke, Z. Steven Wu

• General-Purpose Differentially-Private Confidence Intervals
  Cecilia Ferrando, Shufan Wang, Daniel Sheldon

• Central Limit Theorem and Uncertainty Principles for Differentially Private Query Answering
  Jinshuo Dong, Linjun Zhang, Weijie Su

• Private Stochastic Non-Convex Optimization: Adaptive Algorithms and Tighter Generalization Bounds
  Yingxue Zhou, Xiangyi Chen, Mingyi Hong, Z. Steven Wu, Arindam Banerjee

• Minimax Rates of Estimating Approximate Differential Privacy
  Xiyang Liu, Sewoong Oh

• Really Useful Synthetic Data – A Framework to Evaluate the Quality of Differentially Private Synthetic Data
  Christian Arnold, Marcel Neunhoeffer

• PAC learning with stable and private predictions
  Yuval Dagan, Vitaly Feldman

• Computing Local Sensitivities of Counting Queries with Joins
  Yuchao Tao, Xi He, Ashwin Machanavajjhala, Sudeepa Roy

• Efficient Reductions for Differentially Private Multi-objective Regression
  Julius Adebayo, Daniel Alabi

• The Pitfalls of Differentially Private Prediction in Healthcare
  Vinith Suriyakumar, Nicolas Papernot, Anna Goldenberg, Marzyeh Ghassemi

• Tempered Sigmoid Activations for Deep Learning with Differential Privacy
  Nicolas Papernot, Abhradeep Thakurta, Shuang Song, Steve Chien, Úlfar Erlingsson

• Revisiting Membership Inference Under Realistic Assumptions
  Bargav Jayaraman, Lingxiao Wang, David Evans, Quanquan Gu

• Attribute Privacy: Framework and Mechanisms
  Wanrong Zhang, Olga Ohrimenko, Rachel Cummings

• DuetSGX: Differential Privacy with Secure Hardware
  Phillip Nguyen, Alex Silence, David Darais, Joseph Near

• A Programming Framework for OpenDP
  Marco Gaboardi, Michael Hay, Salil Vadhan

• A One-Pass Private Sketch for Most Machine Learning Tasks
  Benjamin Coleman, Anshumali Shrivastava

• Model-Agnostic Private Learning with Domain Adaptation
  Yuqing Zhu, Chong Liu, Yu-Xiang Wang