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Differentially private synthetic data generation offers a recent solution to release analytically useful data while preserving the privacy of individuals in the data.
Graphical-model based estimation and inference for differential privacy
McKenna, R., Sheldon, D., and Miklau, G. (2019) · 1901
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Generating poisson-distributed differentially private synthetic data
Quick, H. (2019) · 1906
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Statistical analysis of masked data
Little, R. J. (1993) · 1993
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Statistical disclosure limitation
Rubin, D. B. (1993) · 1993
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Do higher rents discourage fertility? evidence from us cities, 1940–2000
Simon, C. J. and Tamura, R. (2009) · 2000
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Multiple imputation for statistical disclosure limitation
Raghunathan, T. E., Reiter, J. P., and Rubin, D. B. (2003) · 2003
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Arnold, C. and Neunhoeffer, M. (2020) · 2004
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Using cart to generate partially synthetic public use microdata
Reiter, J. P. (2005) · 2005
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A framework for evaluating the utility of data altered to protect confidentiality
Karr, A. F., Kohnen, C. N., Oganian, A., Reiter, J. P., and Sanil, A. P. (2006) · 2006
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Mechanism design via differential privacy
McSherry, F. and Talwar, K. (2007) · 2007
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Smooth sensitivity and sampling in private data analysis
Nissim, K., Raskhodnikova, S., and Smith, A. (2007) · 2007
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How protective are synthetic data?
Abowd, J. M. and Vilhuber, L. (2008) · 2008
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Privacy: Theory meets practice on the map
Machanavajjhala, A., Kifer, D., Abowd, J., Gehrke, J., and Vilhuber, L. (2008) · 2008
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Robust de-anonymization of large datasets (how to break anonymity of the netflix prize dataset)
Narayanan, A. and Shmatikov, V. (2008) · 2008
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Privacy integrated queries: an extensible platform for privacy-preserving data analysis
McSherry, F. D. (2009) · 2009
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Global measures of data utility for microdata masked for disclosure limitation
Woo, M.-J., Reiter, J. P., Oganian, A., and Karr, A. F. (2009) · 2009
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Pan-private streaming algorithms
Dwork, C., Naor, M., Pitassi, T., Rothblum, G. N., and Yekhanin, S. (2010) · 2010
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Boosting the accuracy of differentially-private queries through consistency
Hay, M., Rastogi, V., Miklau, G., and Suciu, D. (2010) · 2010
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Synthetic data for small area estimation
Sakshaug, J. W. and Raghunathan, T. E. (2010) · 2010
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A statistical framework for differential privacy
Wasserman, L. and Zhou, S. (2010) · 2010
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How can we analyze differentially-private synthetic datasets?
Charest, A.-S. (2011) · 2011
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Synthetic datasets for statistical disclosure control: theory and implementation
Drechsler, J. (2011) · 2011
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Differentially private data release for data mining
Mohammed, N., Chen, R., Fung, B., and Yu, P. S. (2011) · 2011
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Near-optimal differentially private principal components
Chaudhuri, K., Sarwate, A., and Sinha, K. (2012) · 2012
Cited alongside, same era.
Statistical disclosure control
Hundepool, A., Domingo-Ferrer, J., Franconi, L., Giessing, S., Nordholt, E. S., Spicer, K., and De Wolf, P.-P. (2012) · 2012
Cited alongside, same era.
Estimating identification disclosure risk using mixed membership models
Manrique-Vallier, D. and Reiter, J. P. (2012) · 2012
Cited alongside, same era.
Differential privacy and statistical disclosure risk measures: An investigation with binary synthetic data
McClure, D. and Reiter, J. P. (2012) · 2012
Cited alongside, same era.
Unique in the crowd: The privacy bounds of human mobility
De Montjoye, Y.-A., Hidalgo, C. A., Verleysen, M., and Blondel, V. D. (2013) · 2013
Cited alongside, same era.
How hard is it to’de-anonymize’cellphone data
Hardesty, L. (2013) · 2013
Datasynthesizer: Privacy-preserving synthetic datasets
Ping, H. and Stoyanovich, J. (2017) · 2017
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500 cities project local data for better health
Scally, C. P., L.S. Pettit, K., and Arena, O. (2017) · 2017
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Privbayes: Private data release via bayesian networks
Zhang, J., Cormode, G., Procopiuc, C. M., Srivastava, D., and Xiao, X. (2017) · 2017
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Differentially private data release via statistical election to partition sequentially
Bowen, C. M., Liu, F., and Su, B. (2018) · 2018
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The UK medical education database (ukmed) what is it? why and how might you use it?
Dowell, J., Cleland, J., Fitzpatrick, S., McManus, C., Nicholson, S., Oppé, T., Petty-Saphon, K., King, O. S., Smith, D., Thornton, S., et al. (2018) · 2018
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Cited alongside, same era.
Matching known patients to health records in washington state data
Sweeney, L. (2013) · 2013
Cited alongside, same era.
The algorithmic foundations of differential privacy
Dwork, C. and Roth, A. (2014) · 2014
Cited alongside, same era.
Priview: practical differentially private release of marginal contingency tables
Qardaji, W., Yang, W., and Li, N. (2014) · 2014
Cited alongside, same era.
Scalable privacy-preserving data sharing methodology for genome-wide association studies
Yu, F., Fienberg, S. E., Slavković, A. B., and Uhler, C. (2014) · 2014
Cited alongside, same era.
Unique in the shopping mall: On the reidentifiability of credit card metadata
De Montjoye, Y.-A., Radaelli, L., Singh, V. K., et al. (2015) · 2015
Cited alongside, same era.
Privacy for free: Posterior sampling and stochastic gradient monte carlo
Wang, Y.-X., Fienberg, S., and Smola, A. (2015) · 2015
Cited alongside, same era.
Towards matching user mobility traces in large-scale datasets
Kondor, D., Hashemian, B., de Montjoye, Y.-A., and Ratti, C. (2018) · 2018
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Differentially private model selection with penalized and constrained likelihood
Lei, J., Charest, A.-S., Slavkovic, A., Smith, A., and Fienberg, S. (2018) · 2018
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The personal genome project canada: findings from whole genome sequences of the inaugural 56 participants
Reuter, M. S., Walker, S., Thiruvahindrapuram, B., Whitney, J., Cohn, I., Sondheimer, N., Yuen, R. K., Trost, B., Paton, T. A., Pereira, S. L., et al. (2018) · 2018
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General and specific utility measures for synthetic data
Snoke, J., Raab, G. M., Nowok, B., Dibben, C., and Slavkovic, A. (2018) · 2018
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pmse mechanism: Differentially private synthetic data with maximal distributional similarity
Snoke, J. and Slavković, A. (2018) · 2018
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Social media cultivating perceptions of privacy: A 5-year analysis of privacy attitudes and self-disclosure behaviors among facebook users
Tsay-Vogel, M., Shanahan, J., and Signorielli, N. (2018) · 2018
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2018 differential privacy synthetic data challenge
Vendetti, B. (2018) · 2018
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Differential Privacy Synthetic Data Generation using WGANs
Alzantot, M. and Srivastava, M. (2019) · 2019
Closest in time.
Differential privacy for power grid obfuscation
Fioretto, F., Mak, T. W., and Van Hentenryck, P. (2019) · 2019
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Differential Privacy Synthetic Data Challenge Algorithm
Gardner, J. (2019) · 2019
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Global reactions to the cambridge analytica scandal: An inter-language social media study
González, F., Yu, Y., Figueroa, A., López, C., and Aragon, C. (2019) · 2019
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Differential privacy for eye-tracking data
Liu, A., Xia, L., Duchowski, A., Bailey, R., Holmqvist, K., and Jain, E. (2019) · 2019
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rmckenna - Differential Privacy Synthetic Data Challenge Algorithm
McKenna, R. (2019) · 2019
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Estimating the success of re-identifications in incomplete datasets using generative models
Rocher, L., Hendrickx, J. M., and De Montjoye, Y.-A. (2019) · 2019
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Differential privacy: What is it?
Snoke, J. and Bowen, C. M. (2019) · 2019
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Comparative study of differentially private data synthesis methods
Bowen, C. M. and Liu, F. (2020) · 2020
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Datasynthesizer
Ping, H. (2018) · 2020
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