Differential Privacy Unveiled – the Case of the 2020 Census for Redistricting and Data Privacy
Census statistics play a pivotal role in making public policy decisions such as redrawing legislative districts and allocating federal funds as well as supporting social science research. However, given the risk of revealing individual information, many statistical agencies are considering disclosure control methods based on differential privacy, by adding noise to tabulated data and subsequently conducting postprocessing. The U.S. Census Bureau in particular has implemented a Disclosure Avoidance System (DAS) based on differential privacy technology to protect individual Census responses. This system adds random noise, guided by a privacy loss budget (denoted by ϵ), to Census tabulations, aiming to prevent the disclosure of personal information as mandated by law. The privacy loss budget value ϵ determines the level of privacy protection, with higher ϵ values allowing more noise. While the adoption of differential privacy has been controversial, this approach is crucial for maintaining data confidentiality. Other countries and organizations are also considering this technology as well.
Improving Nature’s Randomized Control Trial
Does a higher body mass index (BMI) increase the severity of COVID-19 symptoms? Mendelian randomization is one method that can be used to study this question without worrying about unmeasured variables (e.g., weight, height, or sex) that could affect the results. A recent paper published in the Annals of Statistics developed a new technique for Mendelian randomization which improves the ability to measure cause-and-effect relationships.