Fetching the paper…
Reading the bibliography…
When sharing data among researchers or releasing data for public use, there is a risk of exposing sensitive information of individuals in the data set.
Qardaji, W., Yang, W. & Li, N. (2013), ‘Understanding hierarchical methods for differentially private histograms’, Proceedings of the VLDB Endowment
1965
Earlier work this paper cites.
Macleod, A. J. & Henderson, G. R. (1984), ‘Bounds for the sample standard deviation’, Teaching Statistics
1984
Earlier work this paper cites.
Little, R. (1993), ‘Statistical analysis of masked data’, Journal of the Official Statistics
1993
Earlier work this paper cites.
Rubin, D. B. (1993), ‘Discussion statistical disclosure limitation’, Journal of official Statistics
1993
Earlier work this paper cites.
Hoeting, J. A., Madigan, D., Raftery, A. E. & Volinsky, C. T. (1999), ‘Bayesian model averaging: a tutorial’, Statistical science
1999
Earlier work this paper cites.
E., R. T., M., L. J., J., V. & P., S. (2001), ‘A multivariate technique for multiply imputing missing values using a sequence of regression models’, Survey Methodology
2001
Earlier work this paper cites.
Reiter, J. P. (2002), ‘Satisfying disclosure restrictions with synthetic data sets’, Journal of Official Statistics
2002
Earlier work this paper cites.
Liu, F. & Little, R. (2003), ‘Smike vs. data swapping and pram for statistical disclosure limitation in microdata: A simulation study’, Proceedings of 2003 American Statistical Association Joint Statistical Meeting
2003
Earlier work this paper cites.
Raghunathan, T. E., Reiter, J. P. & Rubin, D. B. (2003), ‘Multiple imputation for statistical disclosure limitation’, Journal of official Statistics
2003
Earlier work this paper cites.
Reiter, J. P. (2003), ‘Inference for partially synthetic, public use microdata sets’, Survey Methodology
2003
Earlier work this paper cites.
Little, R., Liu, F. & Raghunathan, T. (2004), Statistical disclosure techniques based on multiple imputation, in
2004
Earlier work this paper cites.
Reiter, J. P. (2005), ‘Estimating risks of identification disclosure in microdata’, Journal of the American Statistical Association
2005
Earlier work this paper cites.
Dwork, C., Kenthapadi, K., McSherry, F., Mironov, I. & Naor, M. (2006), Our data, ourselves: Privacy via distriuted noise generation, in
2006
Earlier work this paper cites.
Dwork, C., McSherry, F., Nissim, K. & Smith, A. (2006), Calibrating noise to sensitivity in private data analysis, in
2006
Earlier work this paper cites.
Barak, B., Chaudhuri, K., Dwork, C., Kale, S., McSherry, F. & Talwar, K. (2007), Privacy, accuracy, and consistency too: a holistic solution to contingency table release, in
2007
Earlier work this paper cites.
McSherry, F. & Talwar, K. (2007), Mechanism design via differential privacy, in
2007
Earlier work this paper cites.
Nissim, K., Raskhodnikova, S. & Smith, A. (2007), ‘Smooth sensitivity and sampling in private data analysis’, In Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
2007
Earlier work this paper cites.
Abowd, J. M. & Vilhuber, L. (2008), How protective are synthetic data?, in
2008
Earlier work this paper cites.
Dwork, C. (2008 a
2008
Earlier work this paper cites.
Dwork, C. (2008 b
2008
Earlier work this paper cites.
Homer, N., Szelinger, S., Redmann, M., Duggan, D., Tembe, W., Muehling, J., Pearson, J., Stephan, D., Nelson, S. & Craig, D. (2008), ‘Resolving individuals contributing trace amounts of dna to highly complex mixtures using high-density snp genotyping microarrays’, PLoS Genet
2008
Earlier work this paper cites.
Machanavajjhala, A., Kifer, D., Abowd, J., Gehrke, J. & Vilhuber, L. (2008), ‘Privacy: Theory meets practice on the map’, IEEE ICDE IEEE 24th International Conference
2008
Earlier work this paper cites.
Narayanan, A. & Shmatikov, V. (2008), ‘Robust de anonymization of large sparse datasets’, IEEE Symposium on Security and Privacy
2008
Earlier work this paper cites.
Chaudhuri, K. & Monteleoni, C. (2009), Privacy-preserving logistic regression, in
2009
Earlier work this paper cites.
McSherry, F. (2009), Privacy integrated queries: an extensible platform for privacy-preserving data analysis, in
2009
Earlier work this paper cites.
Reiter, J. P. (2009), ‘Using multiple imputation to integrate and disseminate confidential microdata’, International Statistical Review
2009
Earlier work this paper cites.
2009
Earlier work this paper cites.
Charest, A. S. (2010), ‘How can we analyze differentially private synthetic datasets’, Journal of Privacy and Confidentiality
2010
Earlier work this paper cites.
Hardt, M. & Rothblum, G. N. (2010), A multiplicative weights mechanism for privacy-preserving data analysis, in
2010
Earlier work this paper cites.
Hardt, M. & Talwar, K. (2010), On the geometry of differential privacy, in
2010
Earlier work this paper cites.
Hay, M., Rastogi, V., Miklau, G. & Suciu, D. (2010), ‘Boosting the accuracy of differentially private histograms through consistency’, Proceedings of the VLDB Endowment
2010
Earlier work this paper cites.
Roth, A. & Roughgarden, T. (2010), Interactive privacy via the median mechanism, in
2010
Earlier work this paper cites.
Wasserman, L. & Zhou, S. (2010), ‘A statistical framework for differential privacy’, Journal of the American Statistical Association
2010
Earlier work this paper cites.
Chaudhuri, K., Monteleoni, C. & Sarwate, A. D. (2011), ‘Differentially private empirical risk minimization’, JMLR
2011
Earlier work this paper cites.
Ding, B., Winslett, M., Han, J. & Li, Z. (2011), Differentially private data cubes: Optimizing noise sources and consistency, in
2011
Cited alongside, same era.
Drechsler, J. (2011), Synthetic datasets for Statistical Disclosure Control
2011
Cited alongside, same era.
Dwork, C. (2011), Differential privacy, in
2011
Cited alongside, same era.
Kerman, J. (2011), ‘Neutral noninformative and informative conjugate beta and gamma prior distributions’, Electronic Journal of Statistics
2011
Cited alongside, same era.
Kinney, S. K., Reiter, J. P., Reznek, A. P., Miranda, J., Jarmin, R. S. & Abowd, J. M. (2011), ‘Towards unrestricted public use business microdata: The synthetic longitudinal business database’, International Statistical Review
2011
Cited alongside, same era.
Zhang, J., Cormode, G., Procopiuc, C. M., Srivastava, D. & Xiao, X. (2014), Privbayes: Private data release via bayesian networks, in
2014
Later among the works it cites.
https://digitalcommons.ilr.cornell.edu/ldi/22/
Abowd, J. M. & Schmutte, I. M. (2015), ‘Revisiting the economics of privacy: Population statistics and confidentiality protection as public goods’, Cornell University ILR School · 2015
Later among the works it cites.
He, X., Cormode, G., Machanavajjhala, A., Procopiuc, C. M. & Srivastava, D. (2015), ‘Dpt: Differentially private trajectory synthesis using hierarchical reference systems’, Proceedings of the VLDB Endowment
2015
Later among the works it cites.
2015
Later among the works it cites.
Scott, D. W. (2015), Multivariate density estimation: theory, practice, and visualization
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Lee, J. & Clifton, C. (2011), How much is enough? choosing for differential privacy, in
2011
Cited alongside, same era.
Mohammed, N., Chen, R., Fung, B. & Yu, P. S. (2011), Differentially private data release for data mining, in
2011
Cited alongside, same era.
Acs, G., Castelluccia, C. & Chen, R. (2012), Differentially private histogram publishing through lossy compression, in
2012
Cited alongside, same era.
Chaudhuri, K., Sarwate, A. & Sinha, K. (2012), Near-optimal differentially private principal components, in
2012
Cited alongside, same era.
Gil, D., Girela, J. L., De Juan, J., Gomez-Torres, M. J. & Johnsson, M. (2012), ‘Predicting seminal quality with artificial intelligence methods’, Expert Systems with Applications
2012
Cited alongside, same era.
Götz, M., Machanavajjhala, A., Wang, G., Xiao, X. & Gehrke, J. (2012), ‘Publishing search logs - a comparative study of privacy guarantees’, IEEE Trans. Knowl. Data Eng
2012
Cited alongside, same era.
Hardt, M., Ligett, K. & McSherry, F. (2012), A simple and practical algorithm for differentially private data release, in
2012
Cited alongside, same era.
2015
Later among the works it cites.
Sheffet, O. (2015), ‘Differentially private ordinary least squares’, arXiv preprint arXiv:1507.02482
2015
Later among the works it cites.
Wang, Y.-X., Fienberg, S. & Smola, A. (2015), Privacy for free: Posterior sampling and stochastic gradient monte carlo, in
2015
Later among the works it cites.
Xiao, Y. & Xiong, L. (2015), Protecting locations with differential privacy under temporal correlations, in
2015
Later among the works it cites.
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K. & Zhang, L. (2016), Deep learning with differential privacy, in
2016
Closest in time.
2016
Closest in time.
Fanti, G., Pihur, V. & Erlingsson, Ú. (2016), ‘Building a rappor with the unknown: Privacy-preserving learning of associations and data dictionaries’, Proceedings on Privacy Enhancing Technologies
2016
Closest in time.
Friedman, A., Berkovsky, S. & Kaafar, M. A. (2016), ‘A differential privacy framework for matrix factorization recommender systems’, User Modeling and User-Adapted Interaction
2016
Closest in time.
2016
Closest in time.
Hay, M., Machanavajjhala, A., Miklau, G., Chen, Y. & Zhang, D. (2016), Principled evaluation of differentially private algorithms using dpbench, in
2016
Closest in time.
Li, N., Lyu, M., Su, D. & Yang, W. (2016), ‘Differential privacy: From theory to practice’, Synthesis Lectures on Information Security, Privacy, & Trust
2016
Closest in time.
2016
Closest in time.
2016
Closest in time.
2016
Closest in time.
Wang, Q., Zhang, Y., Lu, X., Wang, Z., Qin, Z. & Ren, K. (2016), Rescuedp: Real-time spatio-temporal crowd-sourced data publishing with differential privacy, in
2016
Closest in time.
2017
Closest in time.
2017
Closest in time.
Charest, A.-S. & Hou, Y. (2017), ‘On the meaning and limits of empirical differential privacy’, Journal of Privacy and Confidentiality
2017
Closest in time.
Karwa, V., Krivitsky, P. N. & Slavković, A. B. (2017), ‘Sharing social network data: differentially private estimation of exponential family random-graph models’, JRSS-Series C (Applied Statisitics)
2017
Closest in time.
Kotsogiannis, I., Machanavajjhala, A., Hay, M. & Miklau, G. (2017), Pythia: Data dependent differentially private algorithm selection, in
2017
Closest in time.
https://pdfs.semanticscholar.org/5a40/c47078efdd3d6b6d15f323d6c3bc0d709ea8.pdf
Kowalczyk, L., Malkin, T., Ullman, J. & Wichs, D. (2017), ‘Hardness of non-interactive differential privacy from one-way functions’, Semantic Scholar · 2017
Closest in time.
Orr, A. (2017), ‘Google’s differential privacy may be better than apple’s’, https://www.macobserver.com/analysis/google-apple-differential-privacy/
2017
Closest in time.
Steinke, T. & Ullman, J. (2017), ‘Between pure and approximate differential privacy’, Journal of Privacy and Confidentiality
2017
Closest in time.
2017
Closest in time.
Vadhan, S. (2017), The complexity of differential privacy, in
2017
Closest in time.
Barrientos, A. F., Bolton, A., Balmat, T., Reiter, J. P., de Figueiredo, J. M., Machanavajjhala, A., Chen, Y., Kneifel, C., DeLong, M. et al. (2018), ‘Providing access to confidential research data through synthesis and verification: An application to data on employees of the us federal government’, The Annals of Applied Statistics
2018
Closest in time.
2018
Closest in time.
Lei, J., Charest, A., Slavkovic, A., Smith, A. & Fienberg, S. (2018), ‘Differentially private model selection with penalized and constrained likelihood’, JRSS-Series A
2018
Closest in time.
of Michigan, S. R. C. U. (2017 (accessed December 20, 2018)), ‘Iveware: Imputation and variance estimation software for srmi’, http://www.isr.umich.edu/src/smp/ive
2018
Closest in time.