What is it about?
Gross Domestic Product (GDP) is the total value of goods and services provided by a country for a period of time like one year. Various methodologies are used for GDP computation including three major approaches: (i) production, (ii) expenditure, and (iii) income. However, the practical application of these methodologies to actual country-level GDP assessment remains unsatisfied due to inherent limitations in accessing individual-level data. This paper studies the intricacies of GDP calculation, particularly focusing on the income approach. It explores innovative methods to ensure the privacy of participants in this computation, presenting techniques involving encryption and differential privacy. Experiment results show the proposed methods are promising and strongly protect individuals' privacy. This endeavor is groundbreaking, marking the first attempt to calculate GDP and related values while safeguarding contributors' privacy.
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Why is it important?
This research is the first-ever attempt to calculate Gross Domestic Product (GDP) while safeguarding privacy.
Perspectives
With this research, individuals can share their income details with the agency without fear of any punitive measures. This is the first time research has been conducted in this direction.
Sanjaikanth E Vadakkethil Somanathan Pillai
University of North Dakota
Read the Original
This page is a summary of: Privacy-Preserving Gross Domestic Product (GDP) Calculation Using Paillier Encryption and Differential Privacy, April 2024, ACM (Association for Computing Machinery),
DOI: 10.1145/3603287.3651188.
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