Community Defense in Depth: Teaching digital security and privacy practices for the public good

The talk opens with a news story about a woman who travelled out of state for an abortion, where a family's request for a wellness check showed how widely available license plate reader data can reveal a person's movements. The speaker presents threat modeling as harm reduction, using the STRIDE framework, and stresses that race, age, income and disability all change a person's threat model.

Two personas anchor the middle of the session. A freelance journalist returning through a border zone faces device searches, so the advice covers unique passwords for each app, backing up notes, powering off devices before inspection, uploading files to encrypted storage before travel, and collecting less sensitive data in the first place. A young activist distributing mutual aid at protests faces facial recognition unlocks, payment fraud and doxxing, so the suggestions include using a passcode rather than biometrics, moving sensitive chats to encrypted messaging, hiding location in photos, verifying callers before sending money, and asking trusted friends to manage accounts during harassment. Street cameras, cell site simulators and a military surveillance drone are described as examples of tools that can track people on the ground.

The talk closes by asking attendees to make one privacy change today, help a friend or neighbour with their own threat model, and consider who in their community would benefit most from the same exercise. The main lesson for practitioners is to start from the attacker's point of view and apply least privilege to every tool and dataset.