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Randomized Aggregatable Privacy-Preserving Ordinal Response, or RAPPOR, is a technology for crowdsourcing statistics from end-user client software, anonymously, with strong privacy guarantees.
Randomized response: A survey technique for eliminating evasive answer bias
Stanley L. Warner · 1965
Earlier work this paper cites.
Space/time trade-offs in hash coding with allowable errors
Burton H. Bloom · 1970
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Regression shrinkage and selection via the Lasso
Robert Tibshirani · 1994
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Controlling the false discovery rate: A practical and powerful approach to multiple testing
Yoav Benjamini and Yosef Hochberg · 1995
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Network applications of Bloom filters: A Survey
Andrei Z. Broder and Michael Mitzenmacher · 2003
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Our data, ourselves: Privacy via distributed noise generation
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor · 2006
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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Privacy via pseudorandom sketches
Nina Mishra and Mark Sandler · 2006
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On privacy-preservation of text and sparse binary data with sketches
Charu C. Aggarwal and Philip S. Yu · 2007
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Mechanism design via differential privacy
Frank McSherry and Kunal Talwar · 2007
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Differential privacy under continual observation
Cynthia Dwork, Moni Naor, Toniann Pitassi, and Guy N. Rothblum · 2010
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Pan-private streaming algorithms
Cynthia Dwork, Moni Naor, Toniann Pitassi, Guy N. Rothblum, and Sergey Yekhanin · 2010
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Interactive privacy via the median mechanism
Aaron Roth and Tim Roughgarden · 2010
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A firm foundation for private data analysis
Cynthia Dwork · 2011
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No free lunch in data privacy
Daniel Kifer and Ashwin Machanavajjhala · 2011
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Pan-private algorithms via statistics on sketches
Differentially private continual monitoring of heavy hitters from distributed streams
T.-H. Hubert Chan, Mingfei Li, Elaine Shi, and Wenchang Xu · 2012
Later among the works it cites.
Towards statistical queries over distributed private user data
Ruichuan Chen, Alexey Reznichenko, Paul Francis, and Johannes Gehrke · 2012
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Distributed private heavy hitters
Justin Hsu, Sanjeev Khanna, and Aaron Roth · 2012
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Cloud-enabled privacy-preserving collaborative learning for mobile sensing
Bin Liu, Yurong Jiang, Fei Sha, and Ramesh Govindan · 2012
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On significance of the least significant bits for differential privacy
Ilya Mironov · 2012
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Privacy via the Johnson-Lindenstrauss transform
Krishnaram Kenthapadi, Aleksandra Korolova, Ilya Mironov, and Nina Mishra · 2013
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Darakhshan J. Mir, S. Muthukrishnan, Aleksandar Nikolov, and Rebecca N. Wright · 2011
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Non-tracking web analytics
Istemi Ekin Akkus, Ruichuan Chen, Michaela Hardt, Paul Francis, and Johannes Gehrke · 2012
Cited alongside, same era.
‘Better Than Nothing’ privacy with Bloom filters: To what extent?
Giuseppe Bianchi, Lorenzo Bracciale, and Pierpaolo Loreti · 2012
Cited alongside, same era.
Design Documents: RAPPOR (Randomized Aggregatable Privacy Preserving Ordinal Responses)
Chromium.org
Cited in the paper.
Randomized response
Wikipedia
Cited in the paper.
Later among the works it cites.
Differential privacy: An economic method for choosing epsilon
Justin Hsu, Marco Gaboardi, Andreas Haeberlen, Sanjeev Khanna, Arjun Narayan, Benjamin C. Pierce, and Aaron Roth · 2014
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Monitoring distributed, heterogeneous data streams: The emergence of safe zones
Daniel Keren, Guy Sagy, Amir Abboud, David Ben-David, Assaf Schuster, Izchak Sharfman, and Antonios Deligiannakis · 2014
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