Entries Tagged "geolocation"

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Determining Physical Location on the Internet

Interesting research: “CPV: Delay-based Location Verification for the Internet“:

Abstract: The number of location-aware services over the Internet continues growing. Some of these require the client’s geographic location for security-sensitive applications. Examples include location-aware authentication, location-aware access policies, fraud prevention, complying with media licensing, and regulating online gambling/voting. An adversary can evade existing geolocation techniques, e.g., by faking GPS coordinates or employing a non-local IP address through proxy and virtual private networks. We devise Client Presence Verification (CPV), a delay-based verification technique designed to verify an assertion about a device’s presence inside a prescribed geographic region. CPV does not identify devices by their IP addresses. Rather, the device’s location is corroborated in a novel way by leveraging geometric properties of triangles, which prevents an adversary from manipulating measured delays. To achieve high accuracy, CPV mitigates Internet path asymmetry using a novel method to deduce one-way application-layer delays to/from the client’s participating device, and mines these delays for evidence supporting/refuting the asserted location. We evaluate CPV through detailed experiments on PlanetLab, exploring various factors that affect its efficacy, including the granularity of the verified location, and the verification time. Results highlight the potential of CPV for practical adoption.

News articles.

Posted on February 12, 2016 at 6:19 AMView Comments

Fugitive Located by Spotify

The latest in identification by data:

Webber said a tipster had spotted recent activity from Nunn on the Spotify streaming service and alerted law enforcement. He scoured the Internet for other evidence of Nunn and Barr’s movements, eventually filling out 12 search warrants for records at different technology companies. Those searches led him to an IP address that traced Nunn to Cabo San Lucas, Webber said.

Nunn, he said, had been avidly streaming television shows and children’s programs on various online services, giving the sheriff’s department a hint to the couple’s location.

Posted on July 29, 2015 at 1:43 PMView Comments

Geotagging Twitter Users by Mining Their Social Graphs

New research: Geotagging One Hundred Million Twitter Accounts with Total Variation Minimization,” by Ryan Compton, David Jurgens, and David Allen.

Abstract: Geographically annotated social media is extremely valuable for modern information retrieval. However, when researchers can only access publicly-visible data, one quickly finds that social media users rarely publish location information. In this work, we provide a method which can geolocate the overwhelming majority of active Twitter users, independent of their location sharing preferences, using only publicly-visible Twitter data.

Our method infers an unknown user’s location by examining their friend’s locations. We frame the geotagging problem as an optimization over a social network with a total variation-based objective and provide a scalable and distributed algorithm for its solution. Furthermore, we show how a robust estimate of the geographic dispersion of each user’s ego network can be used as a per-user accuracy measure which is effective at removing outlying errors.

Leave-many-out evaluation shows that our method is able to infer location for 101,846,236 Twitter users at a median error of 6.38 km, allowing us to geotag over 80% of public tweets.

Posted on March 10, 2015 at 6:50 AMView Comments

How Did the Feds Identity Dread Pirate Roberts?

Last month, I wrote that the FBI identified Ross W. Ulbricht as the Silk Road’s Dread Pirate Roberts through a leaky CAPTCHA. Seems that story doesn’t hold water:

The FBI claims that it found the Silk Road server by examining plain text Internet traffic to and from the Silk Road CAPTCHA, and that it visited the address using a regular browser and received the CAPTCHA page. But [Nicholas] Weaver says the traffic logs from the Silk Road server (PDF) that also were released by the government this week tell a different story.

“The server logs which the FBI provides as evidence show that, no, what happened is the FBI didn’t see a leakage coming from that IP,” he said. “What happened is they contacted that IP directly and got a PHPMyAdmin configuration page.” See this PDF file for a look at that PHPMyAdmin page. Here is the PHPMyAdmin server configuration.

But this is hardly a satisfying answer to how the FBI investigators located the Silk Road servers. After all, if the FBI investigators contacted the PHPMyAdmin page directly, how did they know to do that in the first place?

“That’s still the $64,000 question,” Weaver said. “So both the CAPTCHA couldn’t leak in that configuration, and the IP the government visited wasn’t providing the CAPTCHA, but instead a PHPMyAdmin interface. Thus, the leaky CAPTCHA story is full of holes.”

My guess is that the NSA provided the FBI with this information. We know that the NSA provides surveillance data to the FBI and the DEA, under the condition that they lie about where it came from in court.

NSA whistleblower William Binney explained how it’s done:

…when you can’t use the data, you have to go out and do a parallel construction, [which] means you use what you would normally consider to be investigative techniques, [and] go find the data. You have a little hint, though. NSA is telling you where the data is…

Posted on October 20, 2014 at 6:19 AMView Comments

Geolocating Twitter Users

Interesting research into figuring out where Twitter users are located, based on similar tweets from other users:

While geotags are the most definitive location information a tweet can have, tweets can also have plenty more salient information: hashtags, FourSquare check-ins, or text references to certain cities or states, to name a few. The authors of the paper created their algorithm by analyzing the content of tweets that did have geotags and then searching for similarities in content in tweets without geotags to assess where they might have originated from. Of a body of 1.5 million tweets, 90 percent were used to train the algorithm, and 10 percent were used to test it.

The paper.

Posted on March 26, 2014 at 1:10 PMView Comments

Sidebar photo of Bruce Schneier by Joe MacInnis.