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Government agencies typically need to take potential risks of disclosure into account whenever they publish statistics based on their data or give external researchers access to collected data.
1940 bibliography
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Trust and understanding, two psychological aspects of randomized response
Landsheer, J. A., Van Der Heijden, P., and Van Gils, G. (1999) · 1999
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Revealing information while preserving privacy
Dinur, I. and Nissim, K. (2003) · 2003
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Model assisted survey sampling
Särndal, C.-E., Swensson, B., and Wretman, J. (2003) · 2003
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Calibrating noise to sensitivity in private data analysis
Dwork, C., Mcsherry, F., Nissim, K., and Smith, A. (2006) · 2006
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Transparent privacy is principled privacy
Gong, R. (2020) · 2006
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Privacy, accuracy, and consistency too: A holistic solution to contingency table release
Barak, B., Chaudhuri, K., Dwork, C., Kale, S., McSherry, F., and Talwar, K. (2007) · 2007
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Controlling privacy loss in survey sampling
Bun, M., Drechsler, J., Gaboardi, M., and McMillan, A. (2020) · 2007
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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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Universally utility-maximizing privacy mechanisms
Ghosh, A., Roughgarden, T., and Sundararajan, M. (2009) · 2009
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Differential privacy and the risk-utility tradeoff for multi-dimensional contingency tables
Fienberg, S. E., Rinaldo, A., and Yang, X. (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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The Census Bureau’s simulated reconstruction-abetted re-identification attack on the 2010 Census
U.S. Census Bureau (2021c) · 2010
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Disclosure avoidance in the Census Bureau’s 2010 demonstration data product
Van Riper, D., Kugler, T., and Ruggles, S. (2020) · 2010
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A statistical framework for differential privacy
Wasserman, L. and Zhou, S. (2010) · 2010
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Sensitive questions in online surveys: Experimental results for the randomized response technique (RRT) and the unmatched count technique (UCT)
Coutts, E. and Jann, B. (2011) · 2011
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Differentially private data cubes: Optimizing noise sources and consistency
Ding, B., Winslett, M., Han, J., and Li, Z. (2011) · 2011
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The limits of differential privacy (and its misuse in data release and machine learning)
Domingo-Ferrer, J., Sánchez, D., and Blanco-Justicia, A. (2020) · 2011
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Assessing the representativeness of public opinion surveys
Kohut, A., Keeter, S., Doherty, C., Dimock, M., and Christian, L. (2012) · 2012
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Differential privacy and statistical disclosure risk measures: An investigation with binary synthetic data
McClure, D. and Reiter, J. P. (2012) · 2012
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Fool’s gold: An illustrated critique of differential privacy
Bambauer, J., Muralidhar, K., and Sarathy, R. (2013) · 2013
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Evaluating the potential of differential privacy mechanisms for census data
Soria-Comas, J. and Drechsler, J. (2013) · 2013
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The algorithmic foundations of differential privacy
Dwork, C. and Roth, A. (2014) · 2014
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Rappor: Randomized aggregatable privacy-preserving ordinal response
Erlingsson, Ú., Pihur, V., and Korolova, A. (2014) · 2014
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The ‘privacy paradox’ in the social web: The impact of privacy concerns, individual characteristics, and the perceived social relevance on different forms of self-disclosure
Taddicken, M. (2014) · 2014
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Validating sensitive questions: A comparison of survey and register data
Kirchner, A. (2015) · 2015
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Maximum likelihood postprocessing for differential privacy under consistency constraints
Understanding database reconstruction attacks on public data
Garfinkel, S., Abowd, J. M., and Martindale, C. (2019) · 2019
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The social survey statistician’s perspective
Kreuter, F. (2019) · 2019
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Researchers object to census privacy measure
Mervis, J. (2019) · 2019
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Giving the international scientific community access to German labor market data: A success story
Müller, D. and Möller, J. (2019) · 2019
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Differential privacy and federal data releases
Reiter, J. P. (2019) · 2019
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Differential privacy and census data: Implications for social and economic research
Ruggles, S., Fitch, C., Magnuson, D., and Schroeder, J. (2019) · 2019
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Lee, J., Wang, Y., and Kifer, D. (2015) · 2015
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Deep learning with differential privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., and Zhang, L. (2016) · 2016
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The economics of privacy
Acquisti, A., Taylor, C., and Wagman, L. (2016) · 2016
Cited alongside, same era.
Inference using noisy degrees: Differentially private β \beta -model and synthetic graphs
Karwa, V. and Slavković, A. (2016) · 2016
Cited alongside, same era.
Learning with privacy at scale
Apple’s Differential Privacy Team (2017) · 2017
Cited alongside, same era.
Collecting telemetry data privately
Ding, B., Kulkarni, J., and Yekhanin, S. (2017) · 2017
Cited alongside, same era.
Bridging the gap between computer science and legal approaches to privacy
Nissim, K., Bembenek, A., Wood, A., Bun, M., Gaboardi, M., Gasser, U., O’Brien, D. R., Steinke, T., and Vadhan, S. (2017) · 2017
Cited alongside, same era.
Comparative study of differentially private data synthesis methods
Bowen, C. M. and Liu, F. (2020) · 2020
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Differential privacy in the 2020 Decennial Census and the implications for available data products
boyd, D. (2019) · 2020
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Differentially private k-nearest neighbor missing data imputation
Clifton, C., Hanson, Eric, J., Merrill, K., and Merrill, S. (2020) · 2020
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Congenial differential privacy under mandated disclosure
Gong, R. and Meng, X.-L. (2020) · 2020
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Maine state economist letter to census on differential privacy
Hallowell, A. and Rector, A. (2020) · 2020
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Implementing differential privacy: Seven lessons from the 2020 United States Census
Hawes, M. B. (2020) · 2020
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Total error and variability measures for the quarterly workforce indicators and LEHD origin-destination employment statistics in OnTheMap
McKinney, K. L., Green, A. S., Vilhuber, L., and Abowd, J. M. (2020) · 2020
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Facebook Privacy-Protected Full URLs Data Set
Messing, S., DeGregorio, C., Hillenbrand, B., King, G., Mahanti, S., Mukerjee, Z., Nayak, C., Persily, N., State, B., and Wilkins, A. (2020) · 2020
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Differential privacy and social science: An urgent puzzle
Oberski, D. L. and Kreuter, F. (2020) · 2020
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How differential privacy will affect our understanding of health disparities in the United States
Santos-Lozada, A. R., Howard, J. T., and Verdery, A. M. (2020) · 2020
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Why your answers matter
U.S. Census Bureau (2020) · 2020
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About the 2020 Census
U.S. Census Bureau (2021a) · 2020
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Census bureau sets key parameters to protect privacy in 2020 census results
U.S. Census Bureau (2021b) · 2020
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Changes to the census could make small towns disappear
Wezerek, X. and Van Riper, D. (2020) · 2020
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Structure and sensitivity in differential privacy: Comparing k-norm mechanisms
Awan, J. and Slavković, A. (2021) · 2021
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Differential privacy and the accuracy of county-level net migration estimates
Winkler, R. L., Butler, J. L., Curtis, K. J., and Egan-Robertson, D. (2021) · 2021
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Bias and variance of post-processing in differential privacy
Zhu, K., Van Hentenryck, P., and Fioretto, F. (2021) · 2021
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Towards a modern approach to privacy-aware government data releases
Altman, M., Wood, A., O’Brien, D. R., Vadhan, S., and Gasser, U. (2015) · 2072
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