---
title: "Privacy-Preserving Data Anonymization with privacyR"
author: "Vikrant Dev Rathore"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Privacy-Preserving Data Anonymization with privacyR}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 5
)
```

## Introduction

The `privacyR` package helps you anonymize sensitive data in healthcare and research datasets. It provides tools to protect patient privacy while keeping your data useful for analysis.

## Installation

```{r install, eval = FALSE}
# Install from CRAN
install.packages("privacyR")
```

## Basic Usage

### Anonymizing Patient Identifiers

Anonymize patient IDs while keeping referential integrity (same IDs get the same anonymized value):

```{r anonymize_id}
library(privacyR)

# Original patient IDs
patient_ids <- c("P001", "P002", "P003", "P001", "P002")
print(patient_ids)

# Anonymize IDs
anonymized_ids <- anonymize_id(patient_ids, seed = 123)
print(anonymized_ids)

# Note: Same original IDs map to same anonymized IDs
```

### Anonymizing Patient Names

```{r anonymize_names}
# Original names
names <- c("John Doe", "Jane Smith", "Bob Johnson", "John Doe")
print(names)

# Anonymize names
anonymized_names <- anonymize_names(names, seed = 123)
print(anonymized_names)
```

### Anonymizing Dates

Two methods are available: shifting or rounding.

#### Date Shifting

Shifting moves all dates by the same amount, preserving relative time differences:

```{r anonymize_dates_shift}
# Original dates
dates <- as.Date(c("2020-01-15", "2020-03-20", "2020-06-10"))
print(dates)

# Shift dates
shifted_dates <- anonymize_dates(dates, method = "shift", seed = 123)
print(shifted_dates)

# Relative differences are preserved
diff_original <- as.numeric(dates[2] - dates[1])
diff_shifted <- as.numeric(shifted_dates[2] - shifted_dates[1])
cat("Original difference:", diff_original, "days\n")
cat("Shifted difference:", diff_shifted, "days\n")
```

#### Date Rounding

Rounding reduces precision by grouping dates into buckets (day, week, month, year, etc.):

```{r anonymize_dates_round}
# Round to month
rounded_month <- anonymize_dates(dates, method = "round", 
                                 granularity = "month", seed = 123)
print(rounded_month)

# Round to year
rounded_year <- anonymize_dates(dates, method = "round", 
                                granularity = "year", seed = 123)
print(rounded_year)
```

### Anonymizing Locations

```{r anonymize_locations}
# Original locations
locations <- c("New York, NY", "Los Angeles, CA", "Chicago, IL", 
               "New York, NY")
print(locations)

# Generalize locations
generalized <- anonymize_locations(locations, method = "generalize", seed = 123)
print(generalized)

# Or remove locations entirely
removed <- anonymize_locations(locations, method = "remove", seed = 123)
print(removed)
```

## Working with Data Frames

The `anonymize_dataframe()` function provides a convenient way to anonymize entire data frames:

```{r anonymize_dataframe}
# Create sample patient data
patient_data <- data.frame(
  patient_id = c("P001", "P002", "P003", "P001"),
  name = c("John Doe", "Jane Smith", "Bob Johnson", "John Doe"),
  dob = as.Date(c("1980-01-15", "1975-03-20", "1990-06-10", "1980-01-15")),
  admission_date = as.Date(c("2020-01-10", "2020-02-15", "2020-03-20", "2020-01-10")),
  location = c("New York, NY", "Los Angeles, CA", "Chicago, IL", "New York, NY"),
  diagnosis = c("Hypertension", "Diabetes", "Hypertension", "Hypertension"),
  age = c(40, 45, 30, 40)
)

print("Original data:")
print(patient_data)

# Anonymize the entire data frame
anonymized_data <- anonymize_dataframe(patient_data, seed = 123)

print("\nAnonymized data:")
print(anonymized_data)
```

### Auto-detection

By default, `anonymize_dataframe()` automatically detects columns based on naming patterns and data types:

```{r auto_detect}
# The function automatically detects:
# - ID columns: patient_id, subject_id, etc.
# - Name columns: name, patient_name, etc.
# - Date columns: date, dob, admission_date, etc.
# - Location columns: location, address, city, etc.

# You can also manually specify columns
manual_anon <- anonymize_dataframe(
  patient_data,
  id_cols = "patient_id",
  name_cols = "name",
  date_cols = c("dob", "admission_date"),
  location_cols = "location",
  auto_detect = FALSE,
  seed = 123
)
```

## Best Practices

1. **Seeds and reproducibility**: 
   - The `seed` parameter is optional (default: `NULL`). When `seed = NULL`, the package still maintains referential integrity using a deterministic hash-based approach, so same inputs always produce same outputs.
   - For explicit reproducibility across sessions, provide a seed:
   ```{r best_practices, eval = FALSE}
   anonymized <- anonymize_dataframe(data, seed = 12345)
   ```
   - **Note**: The package always restores your R session's random number generator state after anonymization, so your random number generation is never affected.

2. **Referential integrity** is maintained automatically - same original values get the same anonymized values, which preserves relationships in your data. This works even when `seed = NULL`.

3. **Date anonymization**: 
   - Use "shift" to preserve relative time differences
   - Use "round" to reduce precision (e.g., month-year format)

4. **Location anonymization**: 
   - Use "generalize" to keep some location info
   - Use "remove" when location is too sensitive

5. **Validate your results** - make sure anonymized data still works for your analysis.

## Privacy Considerations

Keep in mind:

- Complete anonymization is difficult to achieve
- You may need additional privacy measures depending on your use case
- Consider consulting privacy experts for sensitive data
- Review relevant regulations (HIPAA, GDPR, etc.) for your jurisdiction

## Getting Help

For more information, see the package documentation:
```{r help, eval = FALSE}
?anonymize_dataframe
help(package = "privacyR")
```