Implementation-ready guidance for privacy-preserving spatial analytics. A practitioner's reference for building geospatial pipelines that protect location data end to end — with federated learning, secure multi-party computation, and differential privacy applied directly to coordinates, trajectories, and spatial aggregates.

🌐 Live site: www.spatial-privacy-engineering.org

Location data defeats conventional anonymization. High-resolution coordinates, trajectory continuity, and contextual adjacency make a "de-identified" feed re-identifiable in hours — a single overnight point resolves to a home, and a continuous trace is unique even with every label stripped. Protecting it takes cryptographic, algorithmic, and policy controls working together, from ingestion through query execution.

This site turns that problem into working engineering. Every guide is opinionated and implementation-first: production-ready Python with type annotations and runnable validation harnesses, hand-authored architecture and data-flow diagrams, threat models specific to geospatial data, and compliance mappings that resolve each regulation to a concrete parameter — a grid-cell size, an epsilon ceiling, a retention window — rather than a vague note.

The knowledge base spans four in-depth areas:

• Core Fundamentals & Architecture — spatial sensitivity scoring, threat mapping, privacy-model selection, privacy-budget management, and compliance framework mapping (GDPR Article 25, HIPAA Safe Harbor, CCPA precise-geolocation).
• Federated Learning Workflows — client selection, gradient aggregation over non-IID geospatial data, model synchronization, secure aggregation (SecAgg), and DP-SGD for spatial models.
• Secure Multi-Party Computation — secret sharing for coordinates, homomorphic encryption (Paillier, CKKS), coordinate masking, private set intersection for spatial joins, and partition-tolerant share routing.
• Differential Privacy for Geospatial Data — spatial noise mechanisms, geo-indistinguishability, differentially private aggregation (heatmaps, quadtree histograms), and trajectory privacy with w-event budgets.

Privacy engineers, GIS data scientists, and healthcare, finance, and mobility technical teams shipping production spatial workloads — anyone who has to make location analytics both useful and defensible.

• Runnable code, not pseudocode. Every Python block is type-annotated and ships with an assertion-based validation harness.
• Original diagrams. Architecture, data-flow, and state diagrams are hand-authored inline SVGs that adapt to light and dark themes — no stock art.
• Regulation → parameter. GDPR, HIPAA, CCPA, and GLBA obligations are mapped to the exact cell size, epsilon, or retention window that satisfies them.
• Threat-model first. Each technique is framed against the adversary it defends against — trajectory reconstruction, map-matching, membership inference, gradient inversion.

A static site generated with Eleventy, with KaTeX for math and Prism for code highlighting, deployed on Cloudflare. Structured data (JSON-LD) ships on every page for rich search results.

npm install
npm run build      # build to _site/
npm run serve      # local dev server
npm run deploy     # build + deploy

Issues and suggestions are welcome — open an issue with corrections, gaps, or techniques you'd like to see covered.

Explore the full knowledge base at www.spatial-privacy-engineering.org.