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Preserving the utility of published datasets while simultaneously providing provable privacy guarantees is a well-known challenge.
Communications through unspecified additive noise
William L Root · 1961
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Randomized response: A survey technique for eliminating evasive answer bias
Stanley L Warner · 1965
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On single-sample robust detection of known signals with additive unknown-mean amplitude-bounded random interference
J Morris · 1980
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On single-sample robust detection of known signals with additive unknown-mean amplitude-bounded random interference–ii: The randomized decision rule solution (corresp.)
J Morris · 1981
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A source coding problem for sources with additional outputs to keep secret from the receiver or wiretappers
H. Yamamoto · 1983
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Information processing in dynamical systems: Foundations of harmony theory
Paul Smolensky · 1986
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Deeprotect: Enabling inference-based access control on mobile sensing applications
Changchang Liu, Supriyo Chakraborty, and Prateek Mittal · 1987
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A random-threshold decision rule for known signals with additive amplitude-bounded nonstationary random interference
Joel M Morris and Neville E Dennis · 1990
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Learning factorial codes by predictability minimization
Jürgen H Schmidhuber · 1992
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Worst-case power-constrained noise for binary-input channels
Shlomo Shamai and Sergio Verdú · 1992
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On solving constrained optimization problems with neural networks: A penalty method approach
Walter E Lillo, Mei Heng Loh, Stefen Hui, and Stanislaw H Zak · 1993
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Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression
Pierangela Samarati and Latanya Sweeney · 1998
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Neural networks for classification: a survey
Guoqiang Peter Zhang · 2000
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Protecting respondents identities in microdata release
Pierangela Samarati · 2001
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Transforming data to satisfy privacy constraints
Vijay S Iyengar · 2002
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k-anonymity: A model for protecting privacy
Latanya Sweeney · 2002
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Normal distribution
Eric W Weisstein · 2002
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Detection, estimation, and modulation theory
Harry L Van Trees · 2004
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Data privacy through optimal k-anonymization
Roberto J Bayardo and Rakesh Agrawal · 2005
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Incognito: Efficient full-domain k-anonymity
Kristen LeFevre, David J DeWitt, and Raghu Ramakrishnan · 2005
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Differential privacy
C. Dwork · 2006
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Anonymizing classification data for privacy preservation
Benjamin CM Fung, Ke Wang, and S Yu Philip · 2007
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t-closeness: Privacy beyond k-anonymity and l-diversity
Ninghui Li, Tiancheng Li, and Suresh Venkatasubramanian · 2007
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Handicapping attacker’s confidence: an alternative to k-anonymization
Ke Wang, Benjamin CM Fung, and S Yu Philip · 2007
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Differential privacy: A survey of results
C. Dwork · 2008
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Robust de-anonymization of large sparse datasets
Arvind Narayanan and Vitaly Shmatikov · 2008
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Deep belief networks
Geoffrey E Hinton · 2009
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From t-Closeness-Like Privacy to Postrandomization via Information Theory
D. Rebollo-Monedero, J. Forne, and J. Domingo-Ferrer · 2009
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Differential Privacy and the Risk-Utility Tradeoff for Multi-dimensional Contingency Tables , pages 187–199
Stephen E. Fienberg, Alessandro Rinaldo, and Xiaolin Yang · 2010
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Privacy-preserving data publishing: A survey of recent developments
Benjamin Fung, Ke Wang, Rui Chen, and Philip S Yu · 2010
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Competitive privacy in the smart grid: An information-theoretic approach
L. Sankar, S. K. Kar, R. Tandon, and H. V. Poor · 2011
Cited alongside, same era.
Smart meter privacy using a rechargeable battery: Minimizing the rate of information leakage
D. Varodayan and A. Khisti · 2011
Cited alongside, same era.
Privacy against statistical inference
F. P. Calmon and N. Fawaz · 2012
Cited alongside, same era.
Augmented lagrangian and alternating direction methods for convex optimization: A tutorial and some illustrative computational results
Jonathan Eckstein and W Yao · 2012
Cited alongside, same era.
Smart meter privacy: A theoretical framework
L. Sankar, S. Raj Rajagopalan, S. Mohajer, and H. V. Poor · 2012
Cited alongside, same era.
Bounds on inference
Flávio Pin Calmon, Mayank Varia, Muriel Médard, Mark M. Christiansen, Ken R. Duffy, and Stefano Tessaro · 2013
Learning to protect communications with adversarial neural cryptography
Martín Abadi and David G Andersen · 2016
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Tensorflow: Large-scale machine learning on heterogeneous distributed systems
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, et al · 2016
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Privacy-aware MMSE estimation
S. Asoodeh, F. Alajaji, and T. Linder · 2016
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On privacy-utility tradeoffs for constrained data release mechanisms
Y. O. Basciftci, Y. Wang, and P. Ishwar · 2016
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Secure data outsourcing with adversarial data dependency constraints
Boxiang Dong, Wendy Wang, and Jie Yang · 2016
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Cited alongside, same era.
Local privacy and statistical minimax rates
John C Duchi, Michael I Jordan, and Martin J Wainwright · 2013
Cited alongside, same era.
Luis G. Sanchez Giraldo and Jose C. Principe · 2013
Cited alongside, same era.
Algorithms for direct 0–1 loss optimization in binary classification
Tan Nguyen and Scott Sanner · 2013
Cited alongside, same era.
Measuring statistical dependence via the mutual information dimension
Mahito Sugiyama and Karsten M Borgwardt · 2013
Cited alongside, same era.
Identifying participants in the personal genome project by name (a re-identification experiment)
Latanya Sweeney, Akua Abu, and Julia Winn · 2013
Cited alongside, same era.
Deep learning using linear support vector machines
Yichuan Tang · 2013
Cited alongside, same era.
John Duchi, Martin Wainwright, and Michael Jordan · 2016
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Minimax filter: Learning to preserve privacy from inference attacks
Jihun Hamm · 2016
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Quantification of private information leakage from phenotype-genotype data: linking attacks
Arif Harmanci and Mark Gerstein · 2016
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An operational measure of information leakage
Ibrahim Issa, Sudeep Kamath, and Aaron B. Wagner · 2016
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On the fine asymptotics of information theoretic privacy
K. Kalantari, O. Kosut, and L. Sankar · 2016
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Inference using noisy degrees: Differentially private β \beta -model and synthetic graphs
Vishesh Karwa and Aleksandra Slavković · 2016
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Hypothesis testing in the high privacy regime
J. Liao, L. Sankar, V. F. Tan, and F. du Pin Calmon · 2016
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National privacy research strategy
National Science and Technology Council Networking and Information Technology Research and Development Program · 2016
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Engineering privacy for your user
WWDC 2016 · 2016
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Deep variational information bottleneck
Alex Alemi, Ian Fischer, Josh Dillon, and Kevin Murphy · 2017
Closest in time.
Privacy-aware guessing efficiency
S. Asoodeh, M. Diaz, F. Alajaji, and T. Linder · 2017
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The world’s most valuable resource is no longer oil, but data
The Economist · 2017
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The EU general data protection regulation (GDPR)
EUGDPR · 2017
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LOGAN: Evaluating Privacy Leakage of Generative Models Using Generative Adversarial Networks
J. Hayes, L. Melis, G. Danezis, and E. De Cristofaro · 2017
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Operational definitions for some common information leakage metrics
I. Issa and A. B. Wagner · 2017
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On information-theoretic privacy with general distortion cost functions
K. Kalantari, L. Sankar, and O. Kosut · 2017
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Ensemble estimation of mutual information
K. R. Moon, K. Sricharan, and A. O. Hero · 2017
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Privacy-guaranteed two-agent interactions using information-theoretic mechanisms
B. Moraffah and L. Sankar · 2017
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Protecting visual secrets using adversarial nets
Nisarg Raval, Ashwin Machanavajjhala, and Landon P Cox · 2017
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Privacy loss in apple’s implementation of differential privacy on macos 10.12
Jun Tang, Aleksandra Korolova, Xiaolong Bai, Xueqiang Wang, and Xiaofeng Wang · 2017
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Lossy image compression with compressive autoencoders
L. Theis, W. Shi, A. Cunningham, and F. Huszár · 2017
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Cleaning the null space: A privacy mechanism for predictors
Ke Xu, Tongyi Cao, Swair Shah, Crystal Maung, and Haim Schweitzer · 2017
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Optimal schemes for discrete distribution estimation under local differential privacy
M. Ye and A. Barg · 2017
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Information potential auto-encoders
Yan Zhang, Mete Ozay, Zhun Sun, and Takayuki Okatani · 2017
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