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We consider the problem of property testing for differential privacy: with black-box access to a purportedly private algorithm, can we verify its privacy guarantees? In particular, we show that any privacy guarantee that can be efficiently verified is also efficiently breakable in the sense that there exist two databases between which we can efficiently distinguish.
Differential privacy: A survey of results
Cynthia Dwork · 2008
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Privacy oracle: A system for finding application leaks with black box differential testing
Jaeyeon Jung, Anmol Sheth, Ben Greenstein, David Wetherall, Gabriel Maganis, and Tadayoshi Kohno · 2008
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A coincidence-based test for uniformity given very sparsely sampled discrete data
L. Paninski · 2008
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Privacy integrated queries: An extensible platform for privacy-preserving data analysis
Frank D. McSherry · 2009
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Taintdroid: An information-flow tracking system for realtime privacy monitoring on smartphones
William Enck, Peter Gilbert, Byung-Gon Chun, Landon P. Cox, Jaeyeon Jung, Patrick McDaniel, and Anmol N. Sheth · 2010
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Distance makes the types grow stronger: A calculus for differential privacy
Jason Reed and Benjamin C. Pierce · 2010
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Airavat: Security and privacy for mapreduce
Indrajit Roy, Srinath T. V. Setty, Ann Kilzer, Vitaly Shmatikov, and Emmett Witchel · 2010
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Differential privacy - a primer for the perplexed
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2011
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Formal verification of differential privacy for interactive systems (extended abstract)
Michael Carl Tschantz, Dilsun Kaynar, and Anupam Datta · 2011
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Testing symmetric properties of distributions
Paul Valiant · 2011
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Probabilistic relational reasoning for differential privacy
Gilles Barthe, Boris Köpf, Federico Olmedo, and Santiago Zanella Béguelin · 2012
Earlier work this paper cites.
Quantitative analysis for privacy leak software with privacy petri net
Lejun Fan, Yuanzhuo Wang, Xueqi Cheng, and Shuyuan Jin · 2012
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Verified computational differential privacy with applications to smart metering
Gilles Barthe, George Danezis, Benjamin Gregoire, Cesar Kunz, and Santiago Zanella-Beguelin · 2013
Cited alongside, same era.
Testing closeness of discrete distributions
Tuğkan Batu, Lance Fortnow, Ronitt Rubinfeld, Warren D. Smith, and Patrick White · 2013
Cited alongside, same era.
Testing the Lipschitz property over product distributions with applications to data privacy
Kashyap Dixit, Madhav Jha, Sofya Raskhodnikova, and Abhradeep Thakurta · 2013
Cited alongside, same era.
Linear dependent types for differential privacy
Marco Gaboardi, Andreas Haeberlen, Justin Hsu, Arjun Narayan, and Benjamin C. Pierce · 2013
Cited alongside, same era.
Proving differential privacy in hoare logic
Concentrated differential privacy
Cynthia Dwork and Guy N. Rothblum · 2016
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The complexity of differential privacy
Salil Vadhan · 2016
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Revisiting the economics of privacy: population statistics and confidentiality protection as public goods
John M. Abowd and Ian M. Schmutte · 2017
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What’s new in ios: ios 10.0
Apple · 2017
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Learning with privacy at scale
The Apple DP Team · 2017
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Collecting telemetry data privately
Bolin Ding, Janardhan Kulkarni, and Sergey Yekhanin · 2017
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Gilles Barthe, Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, César Kunz, and Pierre-Yves Strub · 2014
Cited alongside, same era.
The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
Cited alongside, same era.
Rappor: Randomized aggregatable privacy-preserving ordinal response
Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova · 2014
Cited alongside, same era.
Differential privacy and machine learning: a survey and review
Zhanglong Ji, Zachary C. Lipton, and Charles Elkan · 2014
Cited alongside, same era.
An automatic inequality prover and instance optimal identity testing
G. Valiant and P. Valiant · 2014
Cited alongside, same era.
Optimal testing for properties of distributions
Jayadev Acharya, Constantinos Daskalakis, and Gautam Kamath · 2015
Cited alongside, same era.
Near-optimal density estimation in near-linear time using variable-width histograms
Siu-On Chan, Ilias Diakonikolas, Rocco A. Servedio, and Xiaorui Sun
Cited in the paper.
Rényi differential privacy
I. Mironov · 2017
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Learning New Words
Abhradeep Thakurta, Andrew Vyrros, Umesh Vaishampayan, Gaurav Kapoor, Julien Freidiger, Vivek Sridhar, and Doug Davidson · 2017
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Which Distribution Distances are Sublinearly Testable? , pages 2747–2764
Constantinos Daskalakis, Gautam Kamath, and John Wright · 2018
Closest in time.
Toward detecting violations of differential privacy
Ding Ding, Yuxin Wang, Guanhong Wang, Danfeng Zhang, and Daniel Kifer · 2018
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A gentle introduction to concentration inequalities
Karthik Sridharan · 2018
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