Simulation-Based Inference for Differential Privacy

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This research project will deliver tools to obtain accurate and broad statistical conclusions from data that are subject to privacy constraints. Differential Privacy is an increasingly adopted technique to protect data within government and industry, such as in the US 2020 Decennial Census. However, privacy protection comes at a cost in terms of accuracy of the analysis run on these data, sometimes drastically affecting the decisions and conclusions that entail. While employing and training graduate students from diverse backgrounds, this project will use computer-simulation techniques to tackle a wide range of statistical tasks under these privacy settings. The increased accuracy from these new tools will allow for the wider adoption of Differential Privacy and increase the possibility of sharing data with reduced risks of privacy violations. This will guarantee broader access to essential and reliable information for decision-making bodies as well as for researchers in the social sciences and other fields of academic research. Results will be disseminated through a series of publications in journals and conference proceedings in the fields of statistics and computer science, as well as through presentations at national and international scientific conferences and workshops. Open-source software packages will be developed and made available to the broader community.This research project will deliver both theoretical and practical tools for the advancement of statistical approaches in complex parametric settings such as those entailed by the added noise of Differential Privacy mechanisms. Differential Privacy protects the private information of individuals included in the data by introducing calibrated noise (randomness) into the data. The idea behind this mechanism is that even a highly informed attacker/hacker will not be able to detect whether changes in outputs are due to a particular individual's response or are simply due to randomness. However, these noise-addition techniques also introduce additional bias and variance into the analyses made by researchers who will want to use these data for the advancement of knowledge in government, industry, and academia. This project will deliver more accurate analytical techniques by relying on simulation-based statistical methods, such as co-sufficient sampling and indirect inference. While preserving the same level of privacy, this approach will take into account the noise mechanisms used to privatize the data. The tools to be developed will improve estimation and statistical inference on noisy privatized data by correcting bias of estimators and delivering reliable confidence intervals and hypothesis tests for a wide range of statistical methods. The project will establish some of the first links between statistical privacy and simulation-based inference techniques and will expand the field of robust statistics.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.