Data Science2024

Election Survey: Data Anonymization and Privacy Analysis

Applied privacy-preserving transformations to voter survey data while retaining analytical utility.

Methodology

Demographic recoding, age binning, and identifier removal, evaluated with K-anonymity, L-diversity, and Chi-square testing.

Findings

K-anonymity improved from 0 to 2 post-anonymization, while Chi-square tests confirmed key demographic-voting relationships remained statistically consistent.

Tools & Methods

K-AnonymityL-DiversityChi-Square Testing
Modular Data Science Pipeline with DVC & DaggerEnergy Consumption Analysis of Web Applications Using Bayesian Modeling