Timezone: »
We study and provide instance-optimal algorithms in differential privacy by extending and approximating the inverse sensitivity mechanism. We provide two approximation frameworks, one which only requires knowledge of local sensitivities, and a gradient-based approximation for optimization problems, which are efficiently computable for a broad class of functions. We complement our analysis with instance-specific lower bounds for vector-valued functions, which demonstrate that our mechanisms are (nearly) instance-optimal under certain assumptions and that minimax lower bounds may not provide an accurate estimate of the hardness of a problem in general: our algorithms can significantly outperform minimax bounds for well-behaved instances. Finally, we use our approximation framework to develop private mechanisms for unbounded-range mean estimation, principal component analysis, and linear regression. For PCA, our mechanisms give an efficient (pure) differentially private algorithm with near-optimal rates.
Author Information
Hilal Asi (Stanford University)
John Duchi (Stanford)
More from the Same Authors
-
2021 : Private Confidence Sets »
Karan Chadha · John Duchi · Rohith Kuditipudi -
2022 : adaStar: A Method for Adapting to Interpolation »
Gary Cheng · John Duchi -
2023 Poster: Collaboratively Learning Linear Models with Structured Missing Data »
Chen Cheng · Gary Cheng · John Duchi -
2022 Workshop: OPT 2022: Optimization for Machine Learning »
Courtney Paquette · Sebastian Stich · Quanquan Gu · Cristóbal Guzmán · John Duchi -
2022 Poster: Subspace Recovery from Heterogeneous Data with Non-isotropic Noise »
John Duchi · Vitaly Feldman · Lunjia Hu · Kunal Talwar -
2021 Poster: Stochastic Bias-Reduced Gradient Methods »
Hilal Asi · Yair Carmon · Arun Jambulapati · Yujia Jin · Aaron Sidford -
2021 Poster: Adapting to function difficulty and growth conditions in private optimization »
Hilal Asi · Daniel Levy · John Duchi -
2020 Poster: Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems »
Aman Sinha · Matthew O'Kelly · Russ Tedrake · John Duchi -
2020 Poster: Conic Descent and its Application to Memory-efficient Optimization over Positive Semidefinite Matrices »
John Duchi · Oliver Hinder · Andrew Naber · Yinyu Ye -
2020 Poster: Large-Scale Methods for Distributionally Robust Optimization »
Daniel Levy · Yair Carmon · John Duchi · Aaron Sidford -
2020 Poster: Minibatch Stochastic Approximate Proximal Point Methods »
Hilal Asi · Karan Chadha · Gary Cheng · John Duchi -
2020 Spotlight: Minibatch Stochastic Approximate Proximal Point Methods »
Hilal Asi · Karan Chadha · Gary Cheng · John Duchi -
2019 : Poster Session »
Gergely Flamich · Shashanka Ubaru · Charles Zheng · Josip Djolonga · Kristoffer Wickstrøm · Diego Granziol · Konstantinos Pitas · Jun Li · Robert Williamson · Sangwoong Yoon · Kwot Sin Lee · Julian Zilly · Linda Petrini · Ian Fischer · Zhe Dong · Alexander Alemi · Bao-Ngoc Nguyen · Rob Brekelmans · Tailin Wu · Aditya Mahajan · Alexander Li · Kirankumar Shiragur · Yair Carmon · Linara Adilova · SHIYU LIU · Bang An · Sanjeeb Dash · Oktay Gunluk · Arya Mazumdar · Mehul Motani · Julia Rosenzweig · Michael Kamp · Marton Havasi · Leighton P Barnes · Zhengqing Zhou · Yi Hao · Dylan Foster · Yuval Benjamini · Nati Srebro · Michael Tschannen · Paul Rubenstein · Sylvain Gelly · John Duchi · Aaron Sidford · Robin Ru · Stefan Zohren · Murtaza Dalal · Michael A Osborne · Stephen J Roberts · Moses Charikar · Jayakumar Subramanian · Xiaodi Fan · Max Schwarzer · Nicholas Roberts · Simon Lacoste-Julien · Vinay Prabhu · Aram Galstyan · Greg Ver Steeg · Lalitha Sankar · Yung-Kyun Noh · Gautam Dasarathy · Frank Park · Ngai-Man (Man) Cheung · Ngoc-Trung Tran · Linxiao Yang · Ben Poole · Andrea Censi · Tristan Sylvain · R Devon Hjelm · Bangjie Liu · Jose Gallego-Posada · Tyler Sypherd · Kai Yang · Jan Nikolas Morshuis -
2019 : Poster Session »
Eduard Gorbunov · Alexandre d'Aspremont · Lingxiao Wang · Liwei Wang · Boris Ginsburg · Alessio Quaglino · Camille Castera · Saurabh Adya · Diego Granziol · Rudrajit Das · Raghu Bollapragada · Fabian Pedregosa · Martin Takac · Majid Jahani · Sai Praneeth Karimireddy · Hilal Asi · Balint Daroczy · Leonard Adolphs · Aditya Rawal · Nicolas Brandt · Minhan Li · Giuseppe Ughi · Orlando Romero · Ivan Skorokhodov · Damien Scieur · Kiwook Bae · Konstantin Mishchenko · Rohan Anil · Vatsal Sharan · Aditya Balu · Chao Chen · Zhewei Yao · Tolga Ergen · Paul Grigas · Chris Junchi Li · Jimmy Ba · Stephen J Roberts · Sharan Vaswani · Armin Eftekhari · Chhavi Sharma -
2019 : Spotlight talks »
Diego Granziol · Fabian Pedregosa · Hilal Asi -
2019 Poster: Unlabeled Data Improves Adversarial Robustness »
Yair Carmon · Aditi Raghunathan · Ludwig Schmidt · John Duchi · Percy Liang -
2019 Poster: Necessary and Sufficient Geometries for Gradient Methods »
Daniel Levy · John Duchi -
2019 Oral: Necessary and Sufficient Geometries for Gradient Methods »
Daniel Levy · John Duchi -
2018 Poster: Analysis of Krylov Subspace Solutions of Regularized Non-Convex Quadratic Problems »
Yair Carmon · John Duchi -
2018 Oral: Analysis of Krylov Subspace Solutions of Regularized Non-Convex Quadratic Problems »
Yair Carmon · John Duchi -
2018 Poster: Generalizing to Unseen Domains via Adversarial Data Augmentation »
Riccardo Volpi · Hongseok Namkoong · Ozan Sener · John Duchi · Vittorio Murino · Silvio Savarese -
2018 Poster: Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation »
Matthew O'Kelly · Aman Sinha · Hongseok Namkoong · Russ Tedrake · John Duchi -
2017 Poster: Variance-based Regularization with Convex Objectives »
Hongseok Namkoong · John Duchi -
2017 Oral: Variance-based Regularization with Convex Objectives »
Hongseok Namkoong · John Duchi -
2017 Poster: Unsupervised Transformation Learning via Convex Relaxations »
Tatsunori Hashimoto · Percy Liang · John Duchi -
2016 Poster: Local Minimax Complexity of Stochastic Convex Optimization »
sabyasachi chatterjee · John Duchi · John Lafferty · Yuancheng Zhu -
2016 Poster: Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences »
Hongseok Namkoong · John Duchi -
2016 Poster: Learning Kernels with Random Features »
Aman Sinha · John Duchi -
2015 Poster: Asynchronous stochastic convex optimization: the noise is in the noise and SGD don't care »
Sorathan Chaturapruek · John Duchi · Christopher Ré -
2013 Poster: Information-theoretic lower bounds for distributed statistical estimation with communication constraints »
Yuchen Zhang · John Duchi · Michael Jordan · Martin J Wainwright -
2013 Oral: Information-theoretic lower bounds for distributed statistical estimation with communication constraints »
Yuchen Zhang · John Duchi · Michael Jordan · Martin J Wainwright -
2013 Poster: Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation »
John Duchi · Martin J Wainwright · Michael Jordan -
2013 Poster: Estimation, Optimization, and Parallelism when Data is Sparse »
John Duchi · Michael Jordan · Brendan McMahan -
2012 Workshop: Big Learning : Algorithms, Systems, and Tools »
Sameer Singh · John Duchi · Yucheng Low · Joseph E Gonzalez -
2012 Poster: Privacy Aware Learning »
John Duchi · Michael Jordan · Martin J Wainwright -
2012 Poster: Communication-Efficient Algorithms for Statistical Optimization »
Yuchen Zhang · John Duchi · Martin J Wainwright -
2012 Oral: Privacy Aware Learning »
John Duchi · Michael Jordan · Martin J Wainwright -
2012 Poster: Finite Sample Convergence Rates of Zero-Order Stochastic Optimization Methods »
John Duchi · Michael Jordan · Martin J Wainwright · Andre Wibisono -
2011 Poster: Distributed Delayed Stochastic Optimization »
Alekh Agarwal · John Duchi -
2010 Workshop: Learning on Cores, Clusters, and Clouds »
Alekh Agarwal · Lawrence Cayton · Ofer Dekel · John Duchi · John Langford -
2010 Spotlight: Distributed Dual Averaging In Networks »
John Duchi · Alekh Agarwal · Martin J Wainwright -
2010 Poster: Distributed Dual Averaging In Networks »
John Duchi · Alekh Agarwal · Martin J Wainwright -
2009 Poster: Efficient Learning using Forward-Backward Splitting »
John Duchi · Yoram Singer -
2009 Oral: Efficient Learning using Forward-Backward Splitting »
John Duchi · Yoram Singer -
2006 Poster: Using Combinatorial Optimization within Max-Product Belief Propagation »
John Duchi · Danny Tarlow · Gal Elidan · Daphne Koller -
2006 Spotlight: Using Combinatorial Optimization within Max-Product Belief Propagation »
John Duchi · Danny Tarlow · Gal Elidan · Daphne Koller