A drop-in replacement for Google Play services location APIs for Android
Where you can find everything Android from Mapzen
Learning tool on location data privacy, that reflects to users, what conclusions can be drawn from their location histories. This is part of the SIMPORT project.
Geospatial privacy functions for Apache Spark
Geographical Masking methods. Applicable to discrete confidential-private-sensitive geodata.
Artifacts for paper "What is the Cost of Differential Privacy for Deep Learning-Based Trajectory Generation?".
This system focuses on providing advertisements to users based on their current location while preserving their location privacy. It also provides a platform for businesses to post offers about their products and get detailed statistics like the number of people liking their ads..
Privacy Preserving Localization for Smart Automotive Systems
Location Privacy Protection Mechanism (LPPM) : Heat-Map Confusion
Source Code for the Paper: "CSI Obfuscation: Single-Antenna Transmitters Can Not Hide from Adversarial Multi-Antenna Radio Localization Systems"
Digital privacy checklist before filing for divorce. Device security, account separation, spyware detection, and legal boundaries.
Chrome extension to keep your location private by adding noise
Geography-first retrieval and ranking engine on discrete hexagonal grids (H3), with first-class uncertainty. The open core of the HIVE model.
Privacy-preserving Location-based Task Allocation for mobile crowdsensing using approximate agreement and ILP matching.
This is the implementation of the LBS-owned entities that are employed to realize a privacy-enhaning scheme for LBS users as it is described in my master's degree thesis.
Production engineering reference for spatial data anonymization & privacy — k-anonymity, spatial masking, differential privacy, trajectory privacy, and GDPR/CCPA/HIPAA compliance.
A privacy-preserving location system exploring k-anonymity, adaptive differential privacy, and geofencing for location data. Implements DBSCAN and Voronoi-based spatial clustering, Laplace-noise differential privacy, and Fernet encryption to protect user location from inference attacks while preserving utility.
Implementation-ready guidance for privacy-preserving spatial analytics — federated learning, secure MPC, and differential privacy for GIS pipelines.
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