Privacy Aware Feature Engineering for Analytics

1. Overview

Feature engineering is often treated as a purely technical step, yet it is one of the most common places where privacy risk is introduced into analytics systems. Raw datasets frequently contain direct identifiers, quasi-identifiers, and unnecessary detail that can persist deep into analytical workflows.

This repository demonstrates a privacy aware feature engineering pipeline designed to transform raw customer data into analytics ready features without exposing personally identifiable information (PII). The pipeline shows how analysts can preserve behavioural signal while deliberately reducing identifiability and governance risk.

Rather than focusing on model accuracy, this project focuses on design decisions: what to remove, what to generalise, and what to retain. The aim is to show how responsible analytics can be built as a system, not as an afterthought.

Although implementations vary across organisations, these principles apply broadly to most data analytics environments.

2. Architecture Diagram

Location: diagrams/architecture.png

High-level flow:

Raw Customer Data (PII) ↓ Privacy-Aware Preprocessing ↓ Identifier Hashing & Redaction ↓ Binning & Generalisation ↓ Feature-Safe Dataset

The architecture ensures that unsafe data is handled only once and never propagated downstream.

3. Pipeline / System Design Step 1: Ingestion

Raw data is loaded from data/raw/sample_raw_data.csv. This dataset intentionally includes PII and quasi identifiers to reflect realistic operational extracts.

Step 2: Privacy Aware Preprocessing

Before any analytical features are created, the pipeline applies strict preprocessing rules:

direct identifiers are removed or replaced

identifiers are irreversibly hashed

sensitive attributes are generalised

unnecessary granularity is reduced

These steps are applied before feature engineering to minimise risk propagation.

Step 3: Feature Engineering

Only privacy safe features are generated, including:

binned age groups

activity recency bands

transaction frequency tiers

spend bands

grouped categorical attributes

All engineered features are designed to support aggregate analysis rather than individual profiling.

Step 4: Output & Validation

The resulting feature set is written to data/processed/sample_features.csv. A summary of feature distributions is generated to support validation and auditability.

4. Code Highlights Identifier hashing (irreversible) def _hash_identifier(self, value: str) -> str: salted_value = f"{value}{self.hash_salt}" return hashlib.sha256(salted_value.encode()).hexdigest()

This enables stable joins across datasets without retaining raw identifiers.

Explicit PII removal def _redact_email(self, email: str) -> str: return "[REDACTED]"

Partial masking is avoided to reduce leakage through inference.

Binning sensitive attributes def _bin_age(self, age: int) -> str: if age < 25: return "18–24" elif age < 35: return "25–34" elif age < 45: return "35–44" elif age < 55: return "45–54" elif age < 65: return "55–64" else: return "65+"

This preserves analytical signal while reducing identifiability.

5. Results / Outputs Privacy safe feature dataset

File: data/processed/sample_features.csv

The output dataset contains:

no emails

no postcodes

no raw customer identifiers

only hashed IDs and generalised features

This dataset is suitable for downstream analytics, reporting, or modelling.

Feature summary

File: outputs/feature_summary.csv

The summary provides a high level view of feature distributions, supporting:

sanity checks

bias detection

documentation

6. Why This Matters

Privacy risk is often introduced unintentionally during feature engineering rather than during modelling. By embedding privacy controls directly into the transformation layer, this pipeline demonstrates how analytics teams can:

reduce regulatory and reputational risk

avoid downstream rework

enable safer data reuse

design systems that scale responsibly

The approach shown here prioritises behavioural insight over personal detail, making analytics outputs more robust and defensible.

Although implementations vary across organisations, these principles apply broadly to most data analytics environments.

7. Reflection & Future Enhancements

Building this pipeline reinforced that privacy aware analytics is less about compliance checklists and more about design discipline.

Key learnings:

early removal of identifiers simplifies downstream governance

generalisation often preserves more value than expected

explicit documentation builds trust in analytical outputs

Future enhancements could include:

automated privacy risk scoring

configurable binning strategies

integration with predictive modelling pipelines

monitoring re identification risk over time

8. Limitations & Ethics

This pipeline reduces privacy risk but does not eliminate it entirely. Even generalised features can contribute to re identification when combined with external data.

Outputs from this system are intended for aggregate analysis and prioritisation, not for individual level decision making or automated profiling. Human oversight remains essential when interpreting results.

Responsible analytics requires ongoing evaluation of how data is collected, transformed, and used.

Although implementations vary across organisations, these principles apply broadly to most data analytics environments.

9. How to Reproduce

From the project root:

pip install -r requirements.txt python scripts/pipeline.py

This will:

load raw data

apply privacy aware preprocessing

generate feature safe outputs

save summaries for validation

Final Note

This repository reflects an analytics as a system mindset, where technical decisions, ethical considerations, and long-term governance are treated as first class concerns rather than afterthoughts.