Fetching the paper…
Reading the bibliography…
The shuffle model of differential privacy provides promising privacy-utility balances in decentralized, privacy-preserving data analysis.
Randomized response: A survey technique for eliminating evasive answer bias
Stanley L Warner. 1965 · 1965
Earlier work this paper cites.
The meaning and measurement of race in the US census: Glimpses into the future
Charles Hirschman, Richard Alba, and Reynolds Farley. 2000 · 2000
Earlier work this paper cites.
Differential privacy. In Automata, Languages and Programming: 33rd International Colloquium, ICALP 2006, Venice, Italy, July 10-14, 2006, Proceedings, Part II 33 . Springer, 1–12
Cynthia Dwork. 2006 · 2006
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis. In Theory of Cryptography: Third Theory of Cryptography Conference, TCC 2006, New York, NY, USA, March 4-7, 2006. Proceedings 3 . Springer, 265–284
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. 2006 · 2006
Earlier work this paper cites.
The netflix prize. In Proceedings of KDD cup and workshop , Vol. 2007. 35
James Bennett, Stan Lanning, et al · 2007
Earlier work this paper cites.
Mechanism Design via Differential Privacy
Frank McSherry and Kunal Talwar. 2007 · 2007
Earlier work this paper cites.
Differential privacy: A survey of results. In Theory and Applications of Models of Computation: 5th International Conference, TAMC 2008, Xi’an, China, April 25-29, 2008. Proceedings 5 . Springer, 1–19
Cynthia Dwork. 2008 · 2008
Earlier work this paper cites.
A practical differentially private random decision tree classifier. In 2009 IEEE International Conference on Data Mining Workshops . IEEE, 114–121
Geetha Jagannathan, Krishnan Pillaipakkamnatt, and Rebecca N Wright. 2009 · 2009
Earlier work this paper cites.
Computational differential privacy. In Advances in Cryptology-CRYPTO 2009: 29th Annual International Cryptology Conference, Santa Barbara, CA, USA, August 16-20, 2009. Proceedings . Springer, 126–142
Ilya Mironov, Omkant Pandey, Omer Reingold, and Salil Vadhan. 2009 · 2009
Earlier work this paper cites.
Incomplete beta functions
RB Paris. 2010 · 2010
Earlier work this paper cites.
Publishing set-valued data via differential privacy
Rui Chen, Noman Mohammed, Benjamin CM Fung, Bipin C Desai, and Li Xiong. 2011 · 2011
Earlier work this paper cites.
What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith. 2011 · 2011
Earlier work this paper cites.
Geo-indistinguishability: Differential privacy for location-based systems. In CCS. ACM
Miguel E Andrés, Nicolás E Bordenabe, Konstantinos Chatzikokolakis, and Catuscia Palamidessi. 2013 · 2013
Earlier work this paper cites.
Mining frequent patterns with differential privacy
Luca Bonomi and Li Xiong. 2013 · 2013
Earlier work this paper cites.
Broadening the scope of Differential Privacy using metrics. In PETS. Springer
Konstantinos Chatzikokolakis, Miguel E Andrés, Nicolás Emilio Bordenabe, and Catuscia Palamidessi. 2013 · 2013
Earlier work this paper cites.
Local privacy and statistical minimax rates. In Foundations of Computer Science (FOCS), 2013 IEEE 54th Annual Symposium on . IEEE, 429–438
John C Duchi, Michael I Jordan, and Martin J Wainwright. 2013 · 2013
Earlier work this paper cites.
Correlated network data publication via differential privacy
Rui Chen, Benjamin CM Fung, Philip S Yu, and Bipin C Desai. 2014 · 2014
Earlier work this paper cites.
Rappor: Randomized aggregatable privacy-preserving ordinal response. In Proceedings of the 2014 ACM SIGSAC conference on computer and communications security . ACM, 1054–1067
Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova. 2014 · 2014
Earlier work this paper cites.
Extremal mechanisms for local differential privacy
Peter Kairouz, Sewoong Oh, and Pramod Viswanath. 2014 · 2014
Earlier work this paper cites.
A Data-and Workload-Aware Algorithm for Range Queries Under Differential Privacy
Chao Li, Michael Hay, Gerome Miklau, and Yue Wang. 2014 · 2014
Earlier work this paper cites.
Big data analytics in healthcare: Promise and potential
Wullianallur Raghupathi and Viju Raghupathi. 2014 · 2014
Earlier work this paper cites.
Differential privacy in telco big data platform
Xueyang Hu, Mingxuan Yuan, Jianguo Yao, Yu Deng, Lei Chen, Qiang Yang, Haibing Guan, and Jia Zeng. 2015 · 2015
Earlier work this paper cites.
The matrix mechanism: optimizing linear counting queries under differential privacy
Chao Li, Gerome Miklau, Michael Hay, Andrew McGregor, and Vibhor Rastogi. 2015 · 2015
Earlier work this paper cites.
Data breaches of protected health information in the United States
Vincent Liu, Mark A Musen, and Timothy Chou. 2015 · 2015
Earlier work this paper cites.
Discrete Distribution Estimation under Local Privacy. In International Conference on Machine Learning . 2436–2444
Peter Kairouz, Keith Bonawitz, and Daniel Ramage. 2016 · 2016
Earlier work this paper cites.
Collecting and analyzing data from smart device users with local differential privacy
Thông T Nguyên, Xiaokui Xiao, Yin Yang, Siu Cheung Hui, Hyejin Shin, and Junbum Shin. 2016 · 2016
Earlier work this paper cites.
Heavy hitter estimation over set-valued data with local differential privacy. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security . ACM, 192–203
Zhan Qin, Yin Yang, Ting Yu, Issa Khalil, Xiaokui Xiao, and Kui Ren. 2016 · 2016
Earlier work this paper cites.
Practical locally private heavy hitters
Raef Bassily, Kobbi Nissim, Uri Stemmer, and Abhradeep Guha Thakurta. 2017 · 2017
Earlier work this paper cites.
Prochlo: Strong privacy for analytics in the crowd. In Proceedings of the 26th symposium on operating systems principles . 441–459
Andrea Bittau, Úlfar Erlingsson, Petros Maniatis, Ilya Mironov, Ananth Raghunathan, David Lie, Mitch Rudominer, Ushasree Kode, Julien Tinnes, and Bernhard Seefeld. 2017 · 2017
Earlier work this paper cites.
Practical secure aggregation for privacy-preserving machine learning. In proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security . 1175–1191
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth. 2017 · 2017
Earlier work this paper cites.
Rényi differential privacy. In 2017 IEEE 30th computer security foundations symposium (CSF) . IEEE, 263–275
Ilya Mironov. 2017 · 2017
Earlier work this paper cites.
The eu general data protection regulation (gdpr)
Paul Voigt and Axel Von dem Bussche. 2017 · 2017
Cited alongside, same era.
Locally differentially private protocols for frequency estimation. In Proc. of the 26th USENIX Security Symposium . 729–745
Tianhao Wang, Jeremiah Blocki, Ninghui Li, and Somesh Jha. 2017 · 2017
Cited alongside, same era.
Hadamard Response: Estimating Distributions Privately, Efficiently, and with Little Communication
Jayadev Acharya, Ziteng Sun, and Huanyu Zhang. 2018 · 2018
Cited alongside, same era.
Local differential privacy on metric spaces: optimizing the trade-off with utility. In 2018 IEEE 31st Computer Security Foundations Symposium (CSF) . IEEE, 262–267
Mário Alvim, Konstantinos Chatzikokolakis, Catuscia Palamidessi, and Anna Pazii. 2018 · 2018
Cited alongside, same era.
Privacy amplification by subsampling: Tight analyses via couplings and divergences
Borja Balle, Gilles Barthe, and Marco Gaboardi. 2018 · 2018
PCKV: Locally Differentially Private Correlated Key-Value Data Collection with Optimized Utility
Xiaolan Gu, Ming Li, Yueqiang Cheng, Li Xiong, and Yang Cao. 2020 · 2020
Later among the works it cites.
Computing tight differential privacy guarantees using fft. In International Conference on Artificial Intelligence and Statistics . PMLR, 2560–2569
Antti Koskela, Joonas Jälkö, and Antti Honkela. 2020 · 2020
Later among the works it cites.
LDP-Fed: Federated learning with local differential privacy. In Proceedings of the Third ACM International Workshop on Edge Systems, Analytics and Networking . 61–66
Stacey Truex, Ling Liu, Ka-Ho Chow, Mehmet Emre Gursoy, and Wenqi Wei. 2020 · 2020
Later among the works it cites.
Set-valued data publication with local privacy: tight error bounds and efficient mechanisms
Shaowei Wang, Yuqiu Qian, Jiachun Du, Wei Yang, Liusheng Huang, and Hongli Xu. 2020 · 2020
Later among the works it cites.
Collecting and analyzing data jointly from multiple services under local differential privacy
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Protection against reconstruction and its applications in private federated learning
Abhishek Bhowmick, John Duchi, Julien Freudiger, Gaurav Kapoor, and Ryan Rogers. 2018 · 2018
Cited alongside, same era.
Marginal release under local differential privacy. In Proceedings of the 2018 International Conference on Management of Data . ACM, 131–146
Graham Cormode, Tejas Kulkarni, and Divesh Srivastava. 2018 · 2018
Cited alongside, same era.
Towards practical differential privacy for SQL queries
Noah Johnson, Joseph P Near, and Dawn Song. 2018 · 2018
Cited alongside, same era.
LoPub: high-dimensional crowdsourced data publication with local differential privacy
Xuebin Ren, Chia-Mu Yu, Weiren Yu, Shusen Yang, Xinyu Yang, Julie A McCann, and S Yu Philip. 2018 · 2018
Cited alongside, same era.
Empirical risk minimization in non-interactive local differential privacy revisited
Di Wang, Marco Gaboardi, and Jinhui Xu. 2018a · 2018
Cited alongside, same era.
PrivSet: Set-Valued Data Analyses with Locale Differential Privacy. In IEEE INFOCOM 2018-IEEE Conference on Computer Communications . IEEE, 1088–1096
Shaowei Wang, Liusheng Huang, Yiwen Nie, Pengzhan Wang, Hongli Xu, and Wei Yang. 2018b · 2018
Cited alongside, same era.
Locally differentially private frequent itemset mining. In 2018 IEEE Symposium on Security and Privacy (SP) . IEEE, 127–143
Tianhao Wang, Ninghui Li, and Somesh Jha. 2018c · 2018
Cited alongside, same era.
Min Xu, Bolin Ding, Tianhao Wang, and Jingren Zhou. 2020 · 2020
Later among the works it cites.
Connecting robust shuffle privacy and pan-privacy. In Proceedings of the 2021 ACM-SIAM Symposium on Discrete Algorithms (SODA) . SIAM, 2384–2403
Victor Balcer, Albert Cheu, Matthew Joseph, and Jieming Mao. 2021 · 2021
Later among the works it cites.
CGM: an enhanced mechanism for streaming data collection with local differential privacy
Ergute Bao, Yin Yang, Xiaokui Xiao, and Bolin Ding. 2021 · 2021
Later among the works it cites.
AHEAD: adaptive hierarchical decomposition for range query under local differential privacy. In Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security . 1266–1288
Linkang Du, Zhikun Zhang, Shaojie Bai, Changchang Liu, Shouling Ji, Peng Cheng, and Jiming Chen. 2021 · 2021
Later among the works it cites.
Hiding among the clones: A simple and nearly optimal analysis of privacy amplification by shuffling. In 2021 IEEE 62nd Annual Symposium on Foundations of Computer Science (FOCS) . IEEE, 954–964
Vitaly Feldman, Audra McMillan, and Kunal Talwar. 2022 · 2021
Later among the works it cites.
Renyi differential privacy of the subsampled shuffle model in distributed learning
Antonious Girgis, Deepesh Data, and Suhas Diggavi. 2021a · 2021
Later among the works it cites.
Shuffled model of federated learning: Privacy, accuracy and communication trade-offs
Antonious M Girgis, Deepesh Data, Suhas Diggavi, Peter Kairouz, and Ananda Theertha Suresh. 2021c · 2021
Later among the works it cites.
On the rényi differential privacy of the shuffle model. In Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security . 2321–2341
Antonious M Girgis, Deepesh Data, Suhas Diggavi, Ananda Theertha Suresh, and Peter Kairouz. 2021d · 2021
Later among the works it cites.
Projected federated averaging with heterogeneous differential privacy
Junxu Liu, Jian Lou, Li Xiong, Jinfei Liu, and Xiaofeng Meng. 2021 · 2021
Later among the works it cites.
Differentially private multi-armed bandits in the shuffle model
Jay Tenenbaum, Haim Kaplan, Yishay Mansour, and Uri Stemmer. 2021 · 2021
Later among the works it cites.
Hiding Numerical Vectors in Local Private and Shuffled Messages.. In IJCAI . 3706–3712
Shaowei Wang, Jin Li, Yuqiu Qian, Jiachun Du, Wenqing Lin, and Wei Yang. 2021 · 2021
Later among the works it cites.
Citizens’ data privacy in China: The state of the art of the Personal Information Protection Law (PIPL)
Igor Calzada. 2022 · 2022
Later among the works it cites.
Differentially private histograms in the shuffle model from fake users. In 2022 IEEE Symposium on Security and Privacy (SP) . IEEE, 440–457
Albert Cheu and Maxim Zhilyaev. 2022 · 2022
Later among the works it cites.
Privacy amplification via shuffling for linear contextual bandits. In International Conference on Algorithmic Learning Theory . PMLR, 381–407
Evrard Garcelon, Kamalika Chaudhuri, Vianney Perchet, and Matteo Pirotta. 2022 · 2022
Later among the works it cites.
Numerical Accounting in the Shuffle Model of Differential Privacy
Antti Koskela, Mikko A Heikkilä, and Antti Honkela. 2022 · 2022
Later among the works it cites.
Frequency Estimation in the Shuffle Model with Almost a Single Message. In Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security . 2219–2232
Qiyao Luo, Yilei Wang, and Ke Yi. 2022 · 2022
Later among the works it cites.
LDP-IDS: Local differential privacy for infinite data streams. In Proceedings of the 2022 international conference on management of data . 1064–1077
Xuebin Ren, Liang Shi, Weiren Yu, Shusen Yang, Cong Zhao, and Zongben Xu. 2022 · 2022
Later among the works it cites.
Analyzing Preference Data With Local Privacy: Optimal Utility and Enhanced Robustness
Shaowei Wang, Xuandi Luo, Yuqiu Qian, Jiachun Du, Wenqing Lin, and Wei Yang. 2022 · 2022
Later among the works it cites.
A survey on differential privacy for unstructured data content
Ying Zhao and Jinjun Chen. 2022 · 2022
Later among the works it cites.
Optimal accounting of differential privacy via characteristic function. In International Conference on Artificial Intelligence and Statistics . PMLR, 4782–4817
Yuqing Zhu, Jinshuo Dong, and Yu-Xiang Wang. 2022 · 2022
Later among the works it cites.
Stronger privacy amplification by shuffling for Rényi and approximate differential privacy. In Proceedings of the 2023 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) . SIAM, 4966–4981
Vitaly Feldman, Audra McMillan, and Kunal Talwar. 2023 · 2023
Closest in time.
Distributed Mean Estimation for Multi-Message Shuffled Privacy. In Federated Learning and Analytics in Practice: Algorithms, Systems, Applications, and Opportunities
Antonious M Girgis and Suhas Diggavi. 2023 · 2023
Closest in time.
Privacy enhancement via dummy points in the shuffle model
Xiaochen Li, Weiran Liu, Hanwen Feng, Kunzhe Huang, Yuke Hu, Jinfei Liu, Kui Ren, and Zhan Qin. 2023 · 2023
Closest in time.
Metric differential privacy in the shuffle model
Shaowei Wang, Jin Li, Yuntong Li, Xuandi Luo, Wei Yang, Hongyang Yan, and Changyu Dong. 2023a · 2023
Closest in time.
Shuffle Differential Private Data Aggregation for Random Population
Shaowei Wang, Xuandi Luo, Yuqiu Qian, Youwen Zhu, Kongyang Chen, Qi Chen, Bangzhou Xin, and Wei Yang. 2023b · 2023
Closest in time.
Local differential private data aggregation for discrete distribution estimation
Shaowei Wang, Liusheng Huang, Yiwen Nie, Xinyuan Zhang, Pengzhan Wang, Hongli Xu, and Wei Yang. 2019b · 2059
Closest in time.