Comparative Analysis of Differential Privacy Implementations on Synthetic Data

Document Type

Conference Proceeding

Source of Publication

2025 IEEE 15th Annual Computing and Communication Workshop and Conference, CCWC 2025

Publication Date

1-1-2025

Abstract

Differential privacy offers a promising solution to balance data utility and user privacy. This paper compares two prominent differential privacy tools-PyDP and IBM's diffprivlib-that are applied to a synthetic dataset with medical attributes. We evaluate these tools based on their effectiveness in maintaining data privacy while preserving the statistical integrity of the data. Our results reveal that PyDP provides synthetic data that closely matches real-world data, making it appropriate for tasks requiring accuracy while striking a better balance between data utility and privacy. However, IBM's diffprivlib is more suitable for privacy-critical applications due to its stronger privacy guarantees, but at the cost of increased noise and decreased data utility. This paper contributes to the practical understanding of implementing differential privacy in machine learning and software applications and enhances the tools available for developers in sensitive data environments.

ISBN

[9798331507695]

Publisher

IEEE

First Page

243

Last Page

249

Disciplines

Computer Sciences

Keywords

data confidentiality, Differential privacy, private data, synthetic data

Scopus ID

05001135741

Indexed in Scopus

yes

Open Access

no

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