NEWS

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tidydp 0.1.0 (2025-11-27)

Initial CRAN Release

This is the first release of tidydp, a tidy-style interface for applying differential privacy to data frames in R.

Core Features

• Differential Privacy Mechanisms

  • Laplace mechanism for pure epsilon-differential privacy
  • Gaussian mechanism for (epsilon, delta)-differential privacy
  • Automatic sensitivity calculations based on data bounds

• Tidy-Style API Functions

  • dp_add_noise(): Add calibrated noise to numeric columns with pipe support
  • dp_count(): Compute differentially private counts with optional grouping
  • dp_mean(): Compute differentially private means with optional grouping
  • dp_sum(): Compute differentially private sums with optional grouping

• Privacy Budget Management

  • new_privacy_budget(): Create and initialize privacy budgets
  • check_privacy_budget(): Verify sufficient budget before operations
  • Automatic budget tracking with basic composition
  • Print method for budget status visualization

Technical Details

• All functions support the magrittr pipe operator (%>%)
• Flexible mechanism selection (Laplace or Gaussian)
• Support for grouped operations using group_by parameter
• Comprehensive error handling and input validation
• Built from scratch without external differential privacy dependencies

Documentation

• Complete function documentation with examples
• Comprehensive README with usage examples
• Full test suite with >95% code coverage
• Example script demonstrating common workflows

Notes

• This package implements differential privacy mechanisms from first principles
• Suitable for statistical analysis with formal privacy guarantees
• Compatible with the tidyverse ecosystem
• Designed for CRAN submission standards