Adversarial mind-reading with compromised brain-computer interfaces

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Adversarial mind-reading with compromised brain-computer interfaces:
"On the Feasibility of Side-Channel Attacks with Brain-Computer Interfaces," a paper presented by UC Berkeley and U Geneva researchers at this summer's Usenix Security, explored the possibility of adversarial mind-reading attacks on gamers and other people using brain-computer interfaces, such as the Emotiv games controller.

The experimenters wanted to know if they could forcefully extract information from your brain by taking control of your system. In the experiment, they flashed images of random numbers and used the automatic brain-response to them to make guesses as which digits were in their subjects' ATM PINs. Another variant was watching the brain activity of subjects while flashing the logo of a bank and making a guess about whether the subject used that bank.

I suppose that over time, an attacker who was able to control the stimulus and measure the response could glean a large amount of private information from a victim, without the victim ever knowing it.

Brain computer interfaces (BCI) are becoming increasingly popular in the gaming and entertainment industries. Consumer-grade BCI devices are available for a few hundred dollars and are used in a variety of applications, such as video games, hands-free keyboards, or as an assistant in relaxation training. There are application stores similar to the ones used for smart phones, where application developers have access to an API to collect data from the BCI devices.

The security risks involved in using consumer-grade BCI devices have never been studied and the impact of malicious software with access to the device is unexplored. We take a first step in studying the security implications of such devices and demonstrate that this upcoming technology could be turned against users to reveal their private and secret information. We use inexpensive electroencephalography (EEG) based BCI devices to test the feasibility of simple, yet effective, attacks. The captured EEG signal could reveal the user’s private informa- tion about, e.g., bank cards, PIN numbers, area of living, the knowledge of the known persons. This is the first attempt to study the security implications of consumer-grade BCI devices. We show that the entropy of the private information is decreased on the average by approximately 15 % - 40 % compared to random guessing attacks.

On the Feasibility of Side-Channel Attacks with Brain-Computer Interfaces