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Effective Analysis of Human Exposure Conditions with Body-worn Dosimeters in the 2.4 GHz Band

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Published on: May 2, 2018

Effects of differential privacy techniques: Considerations for end users.

Quentin Brummet¹, Patrick Coyle¹, Brandon Sepulvado¹

¹NORC at the University of Chicago, 55 E Monroe St., Chicago, IL, 60603, USA.

Research in Social & Administrative Pharmacy : RSAP

|September 5, 2020

Summary

Differential privacy (DP) techniques can effectively protect survey data, but their performance depends on how the privacy budget is allocated. Careful consideration of data aspects is crucial for optimal results.

Keywords:

Data utility Differential privacy Disclosure avoidance Early care and education Noise infusion Survey research

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Area of Science:

Computer Science
Statistics
Social Science Research

Background:

Differential privacy (DP) is essential for protecting sensitive survey data.
The National Survey of Early Care and Education (NSECE) provides a practical context for evaluating DP methods.
Understanding the impact of DP noise on statistical estimates is critical for data utility.

Purpose of the Study:

To analyze the effects of DP noise injection on survey data estimates.
To compare the performance of different DP techniques for releasing means, medians, and regression coefficients.
To identify factors influencing the effectiveness of DP in statistical analysis.

Main Methods:

Applied differentially private (DP) noise injection techniques to survey data.
Evaluated DP performance using estimates of means, medians, and regression coefficients.
Examined the impact of parameter choices (e.g., histogram bins, variable scaling) on DP results.

Main Results:

Basic DP techniques demonstrate good performance when the privacy budget is not overly fragmented.
The choice of parameters, such as histogram bin count or variable scaling, can significantly alter outcomes.
DP noise injection impacts statistical estimates, with varying effects across different measures.

Conclusions:

DP noise injection is a viable method for protecting survey data, but requires careful implementation.
Small implementation details can have substantial effects on the accuracy of released statistics.
Future DP technique development should prioritize end-user data needs and key statistical measures.