Collecting Telemetry Data Privately

5 December 2017

Papers citing "Collecting Telemetry Data Privately"

50 / 346 papers shown

Title
BRR: Preserving Privacy of Text Data Efficiently on Device Ricardo Silva Carvalho Theodore Vasiloudis Oluwaseyi Feyisetan 106 7 0 16 Jul 2021
Optimizing the Numbers of Queries and Replies in Federated Learning with Differential Privacy Yipeng Zhou Xuezheng Liu Yao Fu Di Wu Chao Li Shui Yu FedML 66 2 0 05 Jul 2021
Asymptotically Optimal Locally Private Heavy Hitters via Parameterized Sketches Hao Wu Anthony Wirth FedML 36 5 0 15 Jun 2021
A Shuffling Framework for Local Differential Privacy Casey Meehan A. Chowdhury Kamalika Chaudhuri Somesh Jha 43 0 0 11 Jun 2021
Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead Badih Ghazi Ravi Kumar Pasin Manurangsi Rasmus Pagh FedML 91 50 0 08 Jun 2021
Generalized Linear Bandits with Local Differential Privacy Yuxuan Han Zhipeng Liang Yang Wang Jiheng Zhang 85 32 0 07 Jun 2021
Privately Learning Mixtures of Axis-Aligned Gaussians Ishaq Aden-Ali H. Ashtiani Christopher Liaw FedML 77 12 0 03 Jun 2021
Instance-optimal Mean Estimation Under Differential Privacy Ziyue Huang Yuting Liang K. Yi 71 57 0 01 Jun 2021
Locally private online change point detection Thomas B. Berrett Yi Yu 61 13 0 22 May 2021
The Permute-and-Flip Mechanism is Identical to Report-Noisy-Max with Exponential Noise Zeyu Ding Daniel Kifer S. Saghaian Thomas Steinke Yuxin Wang Yingtai Xiao Qiang Yan 79 29 0 15 May 2021
On the Renyi Differential Privacy of the Shuffle Model Antonious M. Girgis Deepesh Data Suhas Diggavi A. Suresh Peter Kairouz 97 44 0 11 May 2021
Locally Private k-Means in One Round Alisa Chang Badih Ghazi Ravi Kumar Pasin Manurangsi 89 32 0 20 Apr 2021
The Role of Cross-Silo Federated Learning in Facilitating Data Sharing in the Agri-Food Sector A. Durrant Milan Markovic David Matthews David May J. Enright Georgios Leontidis FedML 72 72 0 14 Apr 2021
Preventing Manipulation Attack in Local Differential Privacy using Verifiable Randomization Mechanism Fumiyuki Kato Yang Cao Masatoshi Yoshikawa AAML 58 31 0 14 Apr 2021
Frequency Estimation Under Multiparty Differential Privacy: One-shot and Streaming Ziyue Huang Yuan Qiu K. Yi Graham Cormode 67 25 0 05 Apr 2021
High-Dimensional Differentially-Private EM Algorithm: Methods and Near-Optimal Statistical Guarantees Zhe Zhang Linjun Zhang FedML 63 3 0 01 Apr 2021
Differentially private inference via noisy optimization Marco Avella-Medina Casey Bradshaw Po-Ling Loh FedML 72 31 0 19 Mar 2021
Wide Network Learning with Differential Privacy Huanyu Zhang Ilya Mironov Meisam Hejazinia 68 27 0 01 Mar 2021
Asymmetric Differential Privacy Shun Takagi Yang Cao Masatoshi Yoshikawa 127 7 0 01 Mar 2021
Discrete Distribution Estimation with Local Differential Privacy: A Comparative Analysis Ba-Dung Le T. Zia 13 2 0 25 Feb 2021
Lossless Compression of Efficient Private Local Randomizers Vitaly Feldman Kunal Talwar 65 40 0 24 Feb 2021
Learning with User-Level Privacy Daniel Levy Ziteng Sun Kareem Amin Satyen Kale Alex Kulesza M. Mohri A. Suresh FedML 106 91 0 23 Feb 2021
Differential Privacy for Government Agencies -- Are We There Yet? Joerg Drechsler 97 22 0 17 Feb 2021
Revenue Attribution on iOS 14 using Conversion Values in F2P Games Frederick Ayala-Gómez Ismo Horppu Erlin Gülbenkoğlu Vesa Siivola Balázs Pejó 23 0 0 16 Feb 2021
Private Prediction Sets Anastasios Nikolas Angelopoulos Stephen Bates Tijana Zrnic Michael I. Jordan 86 12 0 11 Feb 2021
Deep Learning with Label Differential Privacy Badih Ghazi Noah Golowich Ravi Kumar Pasin Manurangsi Chiyuan Zhang 118 153 0 11 Feb 2021
Federated Learning with Local Differential Privacy: Trade-offs between Privacy, Utility, and Communication Muah Kim Onur Gunlu Rafael F. Schaefer FedML 166 120 0 09 Feb 2021
Dopamine: Differentially Private Federated Learning on Medical Data Mohammad Malekzadeh Burak Hasircioglu N. Mital K. Katarya M. E. Ozfatura Deniz Gündüz OOD FedML 101 51 0 27 Jan 2021
Randori: Local Differential Privacy for All Boel Nelson 16 2 0 27 Jan 2021
Differentially Private SGD with Non-Smooth Losses Puyu Wang Yunwen Lei Yiming Ying Hai Zhang 73 28 0 22 Jan 2021
On the Practicality of Differential Privacy in Federated Learning by Tuning Iteration Times Yao Fu Yipeng Zhou Di Wu Shui Yu Yonggang Wen Chao Li FedML 51 10 0 11 Jan 2021
Secure Hot Path Crowdsourcing with Local Differential Privacy under Fog Computing Architecture Mengmeng Yang Ivan Tjuawinata Kwok Yan Lam Jun Zhao Lin Sun FedML 48 24 0 26 Dec 2020
Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling Vitaly Feldman Audra McMillan Kunal Talwar FedML 86 162 0 23 Dec 2020
Research Challenges in Designing Differentially Private Text Generation Mechanisms Oluwaseyi Feyisetan Abhinav Aggarwal Zekun Xu Nathanael Teissier 124 8 0 10 Dec 2020
Compressive Sensing Approaches for Sparse Distribution Estimation Under Local Privacy Zhongzheng Xiong Jialin Sun Xiaojun Mao Jian Wang Sha Ying Zengfeng Huang 13 2 0 03 Dec 2020
Free Gap Estimates from the Exponential Mechanism, Sparse Vector, Noisy Max and Related Algorithms Zeyu Ding Yuxin Wang Yingtai Xiao Guanhong Wang Qiang Yan Daniel Kifer 56 8 0 02 Dec 2020
Robust and Private Learning of Halfspaces Badih Ghazi Ravi Kumar Pasin Manurangsi Thao Nguyen 69 12 0 30 Nov 2020
Differentially Private Synthetic Data: Applied Evaluations and Enhancements Lucas Rosenblatt Xiao-Yang Liu Samira Pouyanfar Eduardo de Leon Anuj M. Desai Joshua Allen SyDa 65 66 0 11 Nov 2020
The Cost of Privacy in Generalized Linear Models: Algorithms and Minimax Lower Bounds T. Tony Cai Yichen Wang Linjun Zhang FedML 96 21 0 08 Nov 2020
Strongly universally consistent nonparametric regression and classification with privatised data Thomas B. Berrett László Gyorfi Harro Walk 50 16 0 31 Oct 2020
Differentially Private ADMM Algorithms for Machine Learning Tao Xu Fanhua Shang Yuanyuan Liu Hongying Liu Longjie Shen Maoguo Gong 76 18 0 31 Oct 2020
A Members First Approach to Enabling LinkedIn's Labor Market Insights at Scale Ryan M. Rogers Adrian Rivera Cardoso Koray Mancuhan Akash Kaura Nikhil T. Gahlawat Neha Jain Paul Ko P. Ahammad 80 11 0 27 Oct 2020
Federated Bandit: A Gossiping Approach Zhaowei Zhu Jingxuan Zhu Ji Liu Yang Liu FedML 248 85 0 24 Oct 2020
Representation Learning for High-Dimensional Data Collection under Local Differential Privacy Alex Mansbridge G. Barbour Davide Piras Michael Murray Christopher Frye Ilya Feige David Barber 58 1 0 23 Oct 2020
On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data Di Wang Hanshen Xiao S. Devadas Jinhui Xu 76 57 0 21 Oct 2020
On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians Ishaq Aden-Ali H. Ashtiani Gautam Kamath 121 44 0 19 Oct 2020
Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization P. Subramani Nicholas Vadivelu Gautam Kamath 101 83 0 18 Oct 2020
Locally Differentially Private Analysis of Graph Statistics Jacob Imola Takao Murakami Kamalika Chaudhuri 134 96 0 17 Oct 2020
Toward Evaluating Re-identification Risks in the Local Privacy Model Takao Murakami Kenta Takahashi AAML 62 10 0 16 Oct 2020
A Comprehensive Survey on Local Differential Privacy Toward Data Statistics and Analysis Teng Wang Xuefeng Zhang Xuefeng Zhang Xinyu Yang 90 88 0 11 Oct 2020