Timezone: »
Local differential privacy (LDP) is a strong notion of privacy that often leads to a significant drop in utility. The original definition of LDP assumes that all the elements in the data domain are equally sensitive. However, in many real-life applications, some elements are more sensitive than others. We propose a context-aware framework for LDP that allows the privacy level to vary across the data domain, enabling system designers to place privacy constraints where they matter without paying the cost where they do not. For binary data domains, we provide a universally optimal privatization scheme and highlight its connections to Warner’s randomized response and Mangat’s improved response. Motivated by geo-location and web search applications, for k-ary data domains, we consider two special cases of context-aware LDP: block-structured LDP and high-low LDP. We study minimax discrete distribution estimation under both cases and provide communication-efficient, sample-optimal schemes, and information-theoretic lower bounds. We show, using worst-case analyses and experiments on Gowalla’s 3.6 million check-ins to 43,750 locations, that context-aware LDP achieves a far better accuracy under the same number of samples.
Author Information
Jayadev Acharya (Cornell University)
Kallista Bonawitz (Google)
Peter Kairouz (Google)
Daniel Ramage (Google)
Ziteng Sun (Cornell University)
More from the Same Authors
-
2021 : Neural Network-based Estimation of the MMSE »
Mario Diaz · Peter Kairouz · Lalitha Sankar -
2021 : The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation »
Peter Kairouz · Ziyu Liu · Thomas Steinke -
2021 : Remember What You Want to Forget: Algorithms for Machine Unlearning »
Ayush Sekhari · Ayush Sekhari · Jayadev Acharya · Gautam Kamath · Ananda Theertha Suresh -
2021 : On the Renyi Differential Privacy of the Shuffle Model »
Antonious Girgis · Deepesh Data · Suhas Diggavi · Ananda Theertha Suresh · Peter Kairouz -
2021 : Practical and Private (Deep) Learning without Sampling orShuffling »
Peter Kairouz · Hugh B McMahan · Shuang Song · Om Dipakbhai Thakkar · Abhradeep Guha Thakurta · Zheng Xu -
2021 : Learning with User-Level Privacy »
Daniel A Levy · Ziteng Sun · Kareem Amin · Satyen Kale · Alex Kulesza · Mehryar Mohri · Ananda Theertha Suresh -
2021 : Industrial Booth (Google) »
Zheng Xu · Peter Kairouz -
2022 : Fair Universal Representations using Adversarial Models »
Monica Welfert · Peter Kairouz · Jiachun Liao · Chong Huang · Lalitha Sankar -
2023 : Unleashing the Power of Randomization in Auditing Differentially Private ML »
Krishna Pillutla · Galen Andrew · Peter Kairouz · Hugh B McMahan · Alina Oprea · Sewoong Oh -
2023 : Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation »
Wei-Ning Chen · Dan Song · Ayfer Ozgur · Peter Kairouz -
2023 : Federated Heavy Hitter Recovery under Linear Sketching »
Adria Gascon · Peter Kairouz · Ziteng Sun · Ananda Suresh -
2023 : SpecTr: Fast Speculative Decoding via Optimal Transport »
Ziteng Sun · Ananda Suresh · Jae Ro · Ahmad Beirami · Himanshu Jain · Felix Xinnan Yu · Michael Riley · Sanjiv Kumar -
2023 : Panel Discussion »
Peter Kairouz · Song Han · Kamalika Chaudhuri · Florian Tramer -
2023 Workshop: Federated Learning and Analytics in Practice: Algorithms, Systems, Applications, and Opportunities »
Zheng Xu · Peter Kairouz · Bo Li · Tian Li · John Nguyen · Jianyu Wang · Shiqiang Wang · Ayfer Ozgur -
2023 Poster: Subset-Based Instance Optimality in Private Estimation »
Travis Dick · Alex Kulesza · Ziteng Sun · Ananda Suresh -
2023 Poster: Federated Heavy Hitter Recovery under Linear Sketching »
Adria Gascon · Peter Kairouz · Ziteng Sun · Ananda Suresh -
2023 Poster: Private Federated Learning with Autotuned Compression »
Enayat Ullah · Christopher Choquette-Choo · Peter Kairouz · Sewoong Oh -
2023 Poster: Algorithms for bounding contribution for histogram estimation under user-level privacy »
Yuhan Liu · Ananda Suresh · Wennan Zhu · Peter Kairouz · Marco Gruteser -
2023 Poster: User-level Private Stochastic Convex Optimization with Optimal Rates »
Raef Bassily · Ziteng Sun -
2022 Workshop: Updatable Machine Learning »
Ayush Sekhari · Gautam Kamath · Jayadev Acharya -
2022 Poster: The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning »
Wei-Ning Chen · Christopher Choquette Choo · Peter Kairouz · Ananda Suresh -
2022 Poster: The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation »
Wei-Ning Chen · Ayfer Ozgur · Peter Kairouz -
2022 Spotlight: The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning »
Wei-Ning Chen · Christopher Choquette Choo · Peter Kairouz · Ananda Suresh -
2022 Oral: The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation »
Wei-Ning Chen · Ayfer Ozgur · Peter Kairouz -
2022 Poster: Correlated Quantization for Distributed Mean Estimation and Optimization »
Ananda Suresh · Ziteng Sun · Jae Ro · Felix Xinnan Yu -
2022 Spotlight: Correlated Quantization for Distributed Mean Estimation and Optimization »
Ananda Suresh · Ziteng Sun · Jae Ro · Felix Xinnan Yu -
2021 : Industrial Panel »
Nathalie Baracaldo · Shiqiang Wang · Peter Kairouz · Zheng Xu · Kshitiz Malik · Tao Zhang -
2021 : Contributed Talks Session 1 »
Marika Swanberg · Samuel Haney · Peter Kairouz -
2021 Poster: Practical and Private (Deep) Learning Without Sampling or Shuffling »
Peter Kairouz · Brendan McMahan · Shuang Song · Om Dipakbhai Thakkar · Abhradeep Guha Thakurta · Zheng Xu -
2021 Poster: The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation »
Peter Kairouz · Ziyu Liu · Thomas Steinke -
2021 Spotlight: The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation »
Peter Kairouz · Ziyu Liu · Thomas Steinke -
2021 Spotlight: Practical and Private (Deep) Learning Without Sampling or Shuffling »
Peter Kairouz · Brendan McMahan · Shuang Song · Om Dipakbhai Thakkar · Abhradeep Guha Thakurta · Zheng Xu -
2021 Poster: Robust Testing and Estimation under Manipulation Attacks »
Jayadev Acharya · Ziteng Sun · Huanyu Zhang -
2021 Poster: Principal Bit Analysis: Autoencoding with Schur-Concave Loss »
Sourbh Bhadane · Aaron Wagner · Jayadev Acharya -
2021 Spotlight: Principal Bit Analysis: Autoencoding with Schur-Concave Loss »
Sourbh Bhadane · Aaron Wagner · Jayadev Acharya -
2021 Spotlight: Robust Testing and Estimation under Manipulation Attacks »
Jayadev Acharya · Ziteng Sun · Huanyu Zhang -
2019 Poster: Communication-Constrained Inference and the Role of Shared Randomness »
Jayadev Acharya · Clément Canonne · Himanshu Tyagi -
2019 Oral: Communication-Constrained Inference and the Role of Shared Randomness »
Jayadev Acharya · Clément Canonne · Himanshu Tyagi -
2019 Poster: Distributed Learning with Sublinear Communication »
Jayadev Acharya · Christopher De Sa · Dylan Foster · Karthik Sridharan -
2019 Oral: Distributed Learning with Sublinear Communication »
Jayadev Acharya · Christopher De Sa · Dylan Foster · Karthik Sridharan -
2019 Poster: Communication Complexity in Locally Private Distribution Estimation and Heavy Hitters »
Jayadev Acharya · Ziteng Sun -
2019 Oral: Communication Complexity in Locally Private Distribution Estimation and Heavy Hitters »
Jayadev Acharya · Ziteng Sun -
2018 Poster: INSPECTRE: Privately Estimating the Unseen »
Jayadev Acharya · Gautam Kamath · Ziteng Sun · Huanyu Zhang -
2018 Oral: INSPECTRE: Privately Estimating the Unseen »
Jayadev Acharya · Gautam Kamath · Ziteng Sun · Huanyu Zhang -
2017 Poster: A Unified Maximum Likelihood Approach for Estimating Symmetric Properties of Discrete Distributions »
Jayadev Acharya · Hirakendu Das · Alon Orlitsky · Ananda Suresh -
2017 Talk: A Unified Maximum Likelihood Approach for Estimating Symmetric Properties of Discrete Distributions »
Jayadev Acharya · Hirakendu Das · Alon Orlitsky · Ananda Suresh