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, privacy–utility measurement, and compliance alignment for spatial privacy engineering.
Federated Learning
Federated learning workflows for geospatial models — client selection, gradient aggregation, sync strategies, async execution, and private evaluation.
Secure MPC
Secure computation for spatial analytics — secret sharing, homomorphic encryption, coordinate masking, private set intersection, async routing, and trusted execution.
Differential Privacy
Differential privacy for geospatial data — spatial noise mechanisms, geo-indistinguishability, private aggregation, trajectory privacy, and on-device local DP.
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.
The library now runs to 83 guides across four sections and twenty-three topics — every page carrying worked Python, a validation harness you can run, and diagrams drawn from the same numbers the text argues from. Use the cards above to jump into a section, or browse the topic-level overviews below.
New in this release
Four new topics close the gaps practitioners hit soonest: measuring what a privacy control actually costs, reporting from a device that trusts no server, operating a federated model you cannot look inside, and running spatial joins in an enclave.
Privacy–Utility Trade-off Measurement
Every privacy control on a spatial pipeline is a trade, and almost every team makes that trade without measuring either side of it. The privacy side at…
Local Differential Privacy for Mobile Clients
Central differential privacy assumes a collector that sees raw coordinates and is trusted to add noise honestly. Local differential privacy (LDP) removes…
Federated Evaluation and Monitoring
Training a model without seeing the data is the part everyone plans for. Operating it without seeing the data is the part that gets discovered in…
Trusted Execution for Spatial Workloads
Secure multi-party computation and homomorphic encryption buy privacy with arithmetic; a trusted execution environment buys it with hardware. Inside an…
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
- Choosing Epsilon from a Utility Target
- Per-User vs Per-Cell Budget Partitioning
- Simulating Trajectory Reconstruction Attacks
- Attestation Workflow for Spatial Enclaves
Browse the guides
Core Fundamentals & Architecture for Spatial Privacy
Architectural baselines, threat mapping, sensitivity scoring, privacy–utility measurement, 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, and private evaluation.
- Async Execution Patterns for Privacy-Preserving Spatial Analytics
- Client Selection Algorithms for Privacy-Preserving Spatial Analytics
- Federated Evaluation and Monitoring
- 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 computation for spatial analytics — secret sharing, homomorphic encryption, coordinate masking, private set intersection, async routing, and trusted execution.
Differential Privacy for Geospatial Data
Differential privacy for geospatial data — spatial noise mechanisms, geo-indistinguishability, private aggregation, trajectory privacy, and on-device local DP.