@article {660400,
	title = {Differentially Private Survey Research},
	journal = {American Journal of Political Science},
	year = {Forthcoming},
	abstract = {Survey researchers have long sought to protect the privacy of their respondents via de-identification (removing names and other directly identifying information) before sharing data. Although these procedures can help, recent research demonstrates that they fail to protect respondents from intentional re-identification attacks, a problem that threatens to undermine vast survey enterprises in academia, government, and industry. This is especially a problem in political science because political beliefs are not merely the subject of our scholarship; they represent some of the most important information respondents want to keep private. We confirm the problem in practice by re-identifying individuals from a survey about a controversial referendum declaring life beginning at conception. We build on the concept of "differential privacy" to offer new data sharing procedures with mathematical guarantees for protecting respondent privacy and statistical validity guarantees for social scientists analyzing differentially private data. \&nbsp;The cost of these new procedures is larger standard errors, which can be overcome with somewhat larger sample sizes.},
	author = {Georgina Evans and Gary King and Adam D. Smith and Abhradeep Thakurta}
}
@article {677821,
	title = {The Essential Role of Statistical Inference in Evaluating Electoral Systems: A Response to DeFord et al.},
	journal = {Political Analysis},
	year = {Forthcoming},
	month = {2023},
	abstract = {Katz, King, and Rosenblatt (2020) introduces a theoretical framework for understanding redistricting and electoral systems, built on basic statistical and social science principles of inference. DeFord et al. (Forthcoming, 2021) instead focuses solely on descriptive measures, which lead to the problems identified in our arti- cle. In this paper, we illustrate the essential role of these basic principles and then offer statistical, mathematical, and substantive corrections required to apply DeFord et al.{\textquoteright}s calculations to social science questions of interest, while also showing how to easily resolve all claimed paradoxes and problems. We are grateful to the authors for their interest in our work and for this opportunity to clarify these principles and our theoretical framework.\&nbsp;},
	author = {Jonathan Katz and Gary King and Elizabeth Rosenblatt}
}
@article {643747,
	title = {Statistically Valid Inferences from Privacy Protected Data},
	journal = {American Political Science Review},
	year = {Forthcoming},
	abstract = {Unprecedented quantities of data that could help social scientists understand and ameliorate the challenges of human society are presently locked away inside companies, governments, and other organizations, in part because of privacy concerns. We address this problem with a general-purpose data access and analysis system with mathematical guarantees of privacy for research subjects, and statistical validity guarantees for researchers seeking social science insights. We build on the standard of {\textquoteleft}{\textquoteleft}differential privacy,{\textquoteright}{\textquoteright} correct for biases induced by the privacy-preserving procedures, provide a proper accounting of uncertainty, and impose minimal constraints on the choice of statistical methods and quantities estimated. We also replicate two recent published articles and show how we can obtain approximately the same substantive results while simultaneously protecting the privacy. Our approach is simple to use and computationally efficient; we also offer open source software that implements all our methods.},
	url = {https://www.doi.org/10.1017/S0003055422001411},
	author = {Georgina Evans and Gary King and Margaret Schwenzfeier and Abhradeep Thakurta}
}
