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Preserving privacy of users is a key requirement of web-scale analytics and reporting applications, and has witnessed a renewed focus in light of recent data breaches and new regulations such as GDPR.
Randomized response: A survey technique for eliminating evasive answer bias
S. L. Warner · 1965
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Security-control methods for statistical databases: A comparative study
N. R. Adam and J. C. Worthmann · 1989
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Keying hash functions for message authentication
M. Bellare, R. Canetti, and H. Krawczyk · 1996
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Privacy-preserving data mining
R. Agrawal and R. Srikant · 2000
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Protecting respondents identities in microdata release
P. Samarati · 2001
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k-anonymity: A model for protecting privacy
L. Sweeney · 2002
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Privacy preserving association rule mining in vertically partitioned data
J. Vaidya and C. Clifton · 2002
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Limiting privacy breaches in privacy preserving data mining
A. Evfimievski, J. Gehrke, and R. Srikant · 2003
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Privacy-preserving distributed mining of association rules on horizontally partitioned data
M. Kantarcioglu and C. Clifton · 2004
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Two can keep a secret: A distributed architecture for secure database services
G. Aggarwal, M. Bawa, P. Ganesan, H. Garcia-Molina, K. Kenthapadi, R. Motwani, U. Srivastava, D. Thomas, and Y. Xu · 2005
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Simulatable auditing
K. Kenthapadi, N. Mishra, and K. Nissim · 2005
Earlier work this paper cites.
Our data, ourselves: Privacy via distributed noise generation
C. Dwork, K. Kenthapadi, F. McSherry, I. Mironov, and M. Naor · 2006
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
C. Dwork, F. McSherry, K. Nissim, and A. Smith · 2006
Cited alongside, same era.
Wherefore art thou r3579x?: Anonymized social networks, hidden patterns, and structural steganography
L. Backstrom, C. Dwork, and J. Kleinberg · 2007
Cited alongside, same era.
t-closeness: Privacy beyond k-anonymity and l-diversity
N. Li, T. Li, and S. Venkatasubramanian · 2007
Cited alongside, same era.
l-diversity: Privacy beyond k-anonymity
A. Machanavajjhala, D. Kifer, J. Gehrke, and M. Venkitasubramaniam · 2007
Cited alongside, same era.
Robust de-anonymization of large sparse datasets
A. Narayanan and V. Shmatikov · 2008
Cited alongside, same era.
Releasing search queries and clicks privately
A. Korolova, K. Kenthapadi, N. Mishra, and A. Ntoulas · 2009
Cited alongside, same era.
Real-time analytics at massive scale with Pinot, 2014
P. N. Naga · 2014
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Building a RAPPOR with the unknown: Privacy-preserving learning of associations and data dictionaries
G. Fanti, V. Pihur, and Ú. Erlingsson · 2016
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Apple’s ‘differential privacy’ is about collecting your data – but not your data
A. Greenberg · 2016
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Apple’s Machine Learning Journal
Learning with privacy at scale · 2017
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Practical locally private heavy hitters
R. Bassily, K. Nissim, U. Stemmer, and A. Thakurta · 2017
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Collecting telemetry data privately
B. Ding, J. Kulkarni, and S. Yekhanin · 2017
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Differential privacy under continual observation
C. Dwork, M. Naor, T. Pitassi, and G. N. Rothblum · 2010
Cited alongside, same era.
Private and continual release of statistics
T.-H. H. Chan, E. Shi, and D. Song · 2011
Cited alongside, same era.
Privacy violations using microtargeted ads: A case study
A. Korolova · 2011
Cited alongside, same era.
Kafka: A distributed messaging system for log processing
J. Kreps, N. Narkhede, and J. Rao · 2011
Cited alongside, same era.
The algorithmic foundations of differential privacy
C. Dwork and A. Roth · 2014
Cited alongside, same era.
RAPPOR: Randomized aggregatable privacy-preserving ordinal response
Ú. Erlingsson, V. Pihur, and A. Korolova · 2014
Cited alongside, same era.
Diffix: High-utility database anonymization
P. Francis, S. P. Eide, and R. Munz · 2017
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De-anonymizing web browsing data with social networks, 2017
J. Su, A. Shukla, S. Goel, and A. Narayanan · 2017
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The EU General Data Protection Regulation (GDPR): A Practical Guide
P. Voigt and A. von dem Bussche · 2017
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When the signal is in the noise: The limits of Diffix’s sticky noise
A. Gadotti, F. Houssiau, L. Rocher, and Y.-A. de Montjoye · 2018
Closest in time.
Towards practical differential privacy for SQL queries
N. Johnson, J. P. Near, and D. Song · 2018
Closest in time.
Privacy-preserving data mining in industry: Practical challenges and lessons learned
K. Kenthapadi, I. Mironov, and A. G. Thakurta · 2018
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