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Cummings, R., Desfontaines, D., Evans, D., Geambasu, R., Huang, Y., Jagielski, M. et al. (2024). Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment. Harvard Data Science Review, 6, 1-123.
https://doi.org/10.1162/99608f92.d3197524

has been cited by the following article:

• TITLE: Differential Privacy Implementation for Anonymous Student Feedback on Campus Safety and Belonging

  AUTHORS: Emma Liu, Joyce Guo

  KEYWORDS: Differential Privacy, Data Anonymization, Local Differential Privacy, Student Feedback, Campus Safety, Privacy-Preserving Data Collection, Educational Data Ethics, Laplace Mechanism, Privacy Budget, Anonymous Surveys

  JOURNAL NAME: Open Journal of Social Sciences, Vol.13 No.12, December 26, 2025

  ABSTRACT: This paper is a review of differential privacy in data collection. Differential privacy is a mathematical framework that protects individual privacy when third parties collect and analyze sensitive information. The system works by adding carefully controlled mathematical noise to datasets to conceal any specific person’s data in the analysis. We will further explore its current applications in the fields of healthcare and public policy and detail our program developed upon this foundation. Utilizing differential privacy to maintain anonymity, the program is a survey that collects student feedback within a high school or college setting. The goal of this project is to help schools better understand student experiences and concerns while ensuring that personal information remains confidential and protected.