Synthesizing Plausible Privacy-Preserving Location Traces

Explore a groundbreaking approach to synthesizing plausible privacy-preserving location traces in this 20-minute IEEE conference talk. Delve into the limitations of existing obfuscation techniques for protecting location privacy and discover a novel generative model that captures both geographic and semantic features of real location traces. Learn how this privacy-preserving framework creates synthetic traces that mimic consistent lifestyles and meaningful mobilities while significantly paralyzing location inference attacks. Examine the statistical similarities between synthetic and real traces, and understand how this method ensures plausible deniability without leaking individual data. Gain insights into the potential applications of this technique in geo-data analysis and location-based services.

Intro
Privacy Preserving Data Publishing
Data Synthesis
Why Synthetic Data?
Location-Based Services (LBS)
Existing Techniques?
Generative Framework
Modeling Human Mobility
Similarity Metrics
Generative Model
Privacy Tests
Utility: What is preserved?
Privacy in LBS Scenario
Conclusions