Wireless devices are everywhere, at home, at the office, and on the street. Devices are bombarding us with transmissions across a wide range of RF frequencies. Many of these invisible transmissions reflect off our bodies, carrying off information about ou location, movement, and other physiological properties. While a boon to professionals with carefully calibrated instruments, they may also be revealing private data about us to potential attackers nearby.
In this paper, we examine the problem of adversarial WiFi sensing, and consider whether ambient WiFi signals around us pose real risks to our personal privacy. We identify a passive adversarial sensing attack, where bad actors using a single smartphone can silently localize and track individuals in their home or office from outside walls, by just listening to ambient WiFi signals. We experimentally validate this attack in 11 real-world locations, and show user tracking with high accuracy. Finally, we propose and evaluate defenses including geo-fencing, rate limiting, and signal obfuscation by WiFi access points.

Artificial Intelligence gives us a uniquely fascinating and clear perspective at the nature of our minds and our relationship to reality. We will discuss perception, mental representation, agency, consciousness, selfhood, and how they can arise in a computational system, like our brain.

This is a clean-room implementation of rsync with a BSD (ISC) license. It's compatible with a modern rsync (3.1.3 is used for testing), but accepts only a subset of rsync's command-line arguments.