Tidy Differential Privacy
Add Gaussian Noise
Add Laplace Noise
Check Privacy Budget
Add Differentially Private Noise to Data Frame Columns
Differentially Private Count
Differentially Private Mean
Differentially Private Sum
Create a New Privacy Budget
Pipe operator
Print Privacy Budget
Calculate L1 Sensitivity for Count Queries
Calculate L2 Sensitivity for Mean Queries
Calculate L1 Sensitivity for Sum Queries
Spend Privacy Budget
tidydp: Tidy Differential Privacy
A tidy-style interface for applying differential privacy to data frames. Provides pipe-friendly functions to add calibrated noise, compute private statistics, and track privacy budgets using the epsilon-delta differential privacy framework. Implements the Laplace mechanism (Dwork et al. 2006 <doi:10.1007/11681878_14>) and the Gaussian mechanism for achieving differential privacy as described in Dwork and Roth (2014) <doi:10.1561/0400000042>.