UW Security Lab’s Yoshi Kohno profiled in Columns

CSE professor Yoshi Kohno is profiled in the March issue of Columns, UW’s alumni magazine.

“Kohno’s experiments are the stuff of science fiction movies: using a kid’s Erector Set to spy on its owner, tracking a runner using his mileage monitor or even hackers taking over a car while it’s driving and forcing it to brake to a stop. The only difference between Hollywood make-believe and reality is that this white hat hacker doesn’t need special effects to make them reality.”

Read the full article here.

Insecurity of the ORCA regional transit not-so-smart card

Since its inception, UW CSE researchers have raised concerns regarding the security and privacy aspects of Seattle’s ORCA (“One Regional Card for All”) regional transit smartcard.

Now “there’s an app for that” — FareBot, which enables any NFC-equipped Android phone to extract the data from ORCA (and similar transit smartcards in San Francisco, Singapore, and Japan).

FareBot, created by Seattle software developer Eric Butler, builds upon work by UW CSE’s Karl Koscher.

Crosscut reports on the app today in two articles.

“The Geeks Who Cracked the ORCA Card” ; “Smart card: What your ORCA never forgets” ; FareBot

Wenn Automation zum Risiko wird

Listen to Security Lab member Franzi Roesner discuss automotive computer security on a German radio station here (between 12:10 and 15:50). Franzi and colleagues at UW and UCSD experimentally discovered exploitable security vulnerabilities in a modern automobile.

SensorSift: Balancing Privacy and Utility in Sensor Data

The rapid growth of sensors and algorithmic reasoning are creating an important challenge to find balance between user privacy and functionality in smart applications. To address this problem Miro Enev and collaborators have developed a quantitative framework called SensorSift which we recently published and have now made available as open source!

http://homes.cs.washington.edu/~miro/sensorsift/.

At the heart of our contribution is an algorithm which transforms raw sensor data into a ‘sifted’ representation which minimizes exposure of user defined private attributes while maximally exposing application-requested public attributes. We envision multiple applications using the same platform, and requesting access to public attributes explicitly not known at the time of the platform creation. Support for future-defined public attributes, while still preserving the defined privacy of the private attributes, is a central challenge that we tackle.

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