Statistically Valid Inferences from Privacy-Protected Data
Bibliographic Data
| ID | 3227600 |
|---|---|
| Authors | Georgina Evans (0000-0001-6159-9844, Harvard University Press, corresponding author), Gary King (0000-0002-5327-7631, Harvard University Press, corresponding author), Margaret Schwenzfeier (Harvard University Press, corresponding author), Abhradeep Thakurta (Google (United States), corresponding author) |
| Year | 2023 |
| Volume | 117 |
| Issue | 4 |
| Pages | 1275-1290 |
| Publication date | 2023-11-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | American Political Science Review (JOURNAL) |
| Journal identifiers | ISSN: 0003-0554 • E-ISSN: 1537-5943 |
| Publisher | Cambridge University Press (CUP) (PUBLISHER) |
| DOI | 10.1017/s0003055422001411 |
| OpenAlex | W4319598802 |
| Language | EN |
| Citations received | 5 |
| References cited | 40 |
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 "differential privacy," 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 illustrate by replicating key analyses from two recent published articles and show how we can obtain approximately the same substantive results while simultaneously protecting privacy. Our approach is simple to use and computationally efficient; we also offer open-source software that implements all our methods
Computer security · Data mining · Data science · Differential privacy · Information privacy · Internet privacy · Key (lock · Privacy Protection · Simple (philosophy · Advanced Causal Inference Techniques · Computer Science · Privacy-Preserving Technologies in Data · Statistical Methods and Bayesian Inference
Research Preregistration in Political Science
Statistical Approaches To Protecting Confidentiality For Microdata And Their Effects On The Quality Of Statistical Inferences
Statistically Valid Inferences from Differentially Private Data Releases, with Application to the Facebook URLs Dataset
Making the Most of Statistical Analyses
Does Affirmative Action Worsen Bureaucratic Performance? Evidence from the Indian Administrative Service
A New Model for Industry–Academic Partnerships
Does Property Ownership Lead to Participation in Local Politics? Evidence from Property Records and Meeting Minutes
A Unified Approach to Measurement Error and Missing Data
| Unique citing works | 5 |
|---|---|
| Citations per year | 1,67 |
| Citation span | 2023 - 2025 (3) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 5 |