Privacy-Preserving Spatial Analytics
Implementation-ready guidance for federated learning, secure multi-party computation, and differential privacy in GIS pipelines. Built for privacy engineers, GIS data scientists, and healthcare/finance tech teams shipping production spatial workloads.
Core Fundamentals
Architectural baselines, threat mapping, sensitivity scoring, and compliance alignment for spatial privacy engineering.
Federated Learning
Federated learning workflows for geospatial models — client selection, gradient aggregation, sync strategies, async execution.
Secure MPC
Secure multi-party computation patterns for spatial analytics — secret sharing, homomorphic encryption, coordinate masking, private set intersection, async routing.
Differential Privacy
Differential privacy for geospatial data — spatial noise mechanisms, geo-indistinguishability, private aggregation, and trajectory privacy.
About this site
Spatial data carries elevated re-identification risk: high-resolution coordinates, trajectory continuity, and contextual adjacency routinely defeat conventional anonymization. This site documents how to integrate federated learning (FL), secure multi-party computation (MPC), and differential privacy (DP) into geospatial pipelines, with Python reference implementations, threat models, and compliance mappings for GDPR, HIPAA, and CCPA.
Each guide is opinionated and implementation-first. You'll find production-ready code, validation harnesses, threat-vector matrices, and operational checklists that translate cryptographic primitives into deterministic spatial controls. The focus is on the cross-section where coordinate systems meet privacy budgets, secure aggregation, and audit-ready engineering.
Use the cards above to jump into a section, or browse the topic-level overviews below.
Start here
New to the material? These hands-on implementation walk-throughs are the fastest way to see the patterns in working Python.
- Choosing Between FL, MPC, and DP for Spatial Pipelines
- Implementing Geo-Indistinguishability in Python
- Building Differentially Private Heatmaps
- Implementing FedAvg for Spatial Time-Series
- Implementing SecAgg Masking for Spatial Gradients
- Shamir Secret Sharing for GPS Coordinate Protection
- Spatial PSI for Proximity Queries
- Privacy Budget Exhaustion — Detection and Recovery
Browse the guides
Core Fundamentals & Architecture for Spatial Privacy
Architectural baselines, threat mapping, sensitivity scoring, and compliance alignment for spatial privacy engineering.
Federated Learning Workflows for Geospatial Data
Federated learning workflows for geospatial models — client selection, gradient aggregation, sync strategies, async execution.
- Async Execution Patterns for Privacy-Preserving Spatial Analytics
- Client Selection Algorithms for Privacy-Preserving Spatial Analytics
- Gradient Aggregation Techniques for Privacy-Preserving Spatial Analytics
- Model Synchronization Strategies for Privacy-Preserving Spatial Analytics
- Secure Aggregation Protocols
Secure Multi-Party Computation in Spatial Analytics
Secure multi-party computation patterns for spatial analytics — secret sharing, homomorphic encryption, coordinate masking, private set intersection, async routing.
Differential Privacy for Geospatial Data
Differential privacy for geospatial data — spatial noise mechanisms, geo-indistinguishability, private aggregation, and trajectory privacy.