Speaker
Description
In many countries, perturbative methods are increasingly used as a privacy protection method for official statistics. The U.S. Census Bureau has applied the mechanism of differential privacy, specifically Zero-Concentrated Differential Privacy (zCDP) during the creation of statistical tables created based on data from the 2020 Census as well as Privacy-Protected Microdata Files (PPMFs) as a protection against “database reconstruction attacks”.
Several empirical studies on the effectiveness of perturbative methods (such as additive noise, data swapping and PRAM) for Japanese official microdata were conducted by Ito and Murata (2011), Ito and Hoshino (2012, 2013, 2014), and Ito et al. (2017, 2018). Other studies have investigated the possibility of adapting differential privacy for detailed geographical data and statistical data created based on individual data from the Japanese Population Census. Studies were also conducted on the potential of differential privacy as an anonymization method for Japanese statistical data (Ito and Terada (2019) and Ito et al. (2020), Ito et al. (2023)).
This paper conducts a comparative study into the potential of differential privacy for Japanese Population Census data, while reflecting the approaches towards the application of differential privacy to official statistics in other countries, particularly the United States. Specifically, this research conducts an analysis of data utility for statistical tables for which zCDP is applied at different geographical levels, which are created using individual data from the 2020 Japanese Population Census.