Entries Tagged "tracking"

Page 7 of 18

Tracking People Without GPS

Interesting research:

The trick in accurately tracking a person with this method is finding out what kind of activity they’re performing. Whether they’re walking, driving a car, or riding in a train or airplane, it’s pretty easy to figure out when you know what you’re looking for.

The sensors can determine how fast a person is traveling and what kind of movements they make. Moving at a slow pace in one direction indicates walking. Going a little bit quicker but turning at 90-degree angles means driving. Faster yet, we’re in train or airplane territory. Those are easy to figure out based on speed and air pressure.

After the app determines what you’re doing, it uses the information it collects from the sensors. The accelerometer relays your speed, the magnetometer tells your relation to true north, and the barometer offers up the air pressure around you and compares it to publicly available information. It checks in with The Weather Channel to compare air pressure data from the barometer to determine how far above sea level you are. Google Maps and data offered by the US Geological Survey Maps provide incredibly detailed elevation readings.

Once it has gathered all of this information and determined the mode of transportation you’re currently taking, it can then begin to narrow down where you are. For flights, four algorithms begin to estimate the target’s location and narrows down the possibilities until its error rate hits zero.

If you’re driving, it can be even easier. The app knows the time zone you’re in based on the information your phone has provided to it. It then accesses information from your barometer and magnetometer and compares it to information from publicly available maps and weather reports. After that, it keeps track of the turns you make. With each turn, the possible locations whittle down until it pinpoints exactly where you are.

To demonstrate how accurate it is, researchers did a test run in Philadelphia. It only took 12 turns before the app knew exactly where the car was.

This is a good example of how powerful synthesizing information from disparate data sources can be. We spend too much time worried about individual data collection systems, and not enough about analysis techniques of those systems.

Research paper.

Posted on December 15, 2017 at 6:18 AM • View Comments

E-Mail Tracking

Good article on the history and practice of e-mail tracking:

The tech is pretty simple. Tracking clients embed a line of code in the body of an email­—usually in a 1×1 pixel image, so tiny it’s invisible, but also in elements like hyperlinks and custom fonts. When a recipient opens the email, the tracking client recognizes that pixel has been downloaded, as well as where and on what device. Newsletter services, marketers, and advertisers have used the technique for years, to collect data about their open rates; major tech companies like Facebook and Twitter followed suit in their ongoing quest to profile and predict our behavior online.

But lately, a surprising­—and growing­—number of tracked emails are being sent not from corporations, but acquaintances. “We have been in touch with users that were tracked by their spouses, business partners, competitors,” says Florian Seroussi, the founder of OMC. “It’s the wild, wild west out there.”

According to OMC’s data, a full 19 percent of all “conversational” email is now tracked. That’s one in five of the emails you get from your friends. And you probably never noticed.

I admit it’s enticing. I would very much like the statistics that adding trackers to Crypto-Gram would give me. But I still don’t do it.

Posted on December 13, 2017 at 6:14 AM • View Comments

Websites Use Session-Replay Scripts to Eavesdrop on Every Keystroke and Mouse Movement

The security researchers at Princeton are postingYou may know that most websites have third-party analytics scripts that record which pages you visit and the searches you make. But lately, more and more sites use “session replay” scripts. These scripts record your keystrokes, mouse movements, and scrolling behavior, along with the entire contents of the pages you visit, and send them to third-party servers. Unlike typical analytics services that provide aggregate statistics, these scripts are intended for the recording and playback of individual browsing sessions, as if someone is looking over your shoulder.

The stated purpose of this data collection includes gathering insights into how users interact with websites and discovering broken or confusing pages. However the extent of data collected by these services far exceeds user expectations; text typed into forms is collected before the user submits the form, and precise mouse movements are saved, all without any visual indication to the user. This data can’t reasonably be expected to be kept anonymous. In fact, some companies allow publishers to explicitly link recordings to a user’s real identity.

The researchers will post more details on their blog; I’ll link to them when they’re published.

News article.

Posted on November 22, 2017 at 8:54 AM • View Comments

E-Mail Tracking

Interesting survey paper: on the privacy implications of e-mail tracking:

Abstract: We show that the simple act of viewing emails contains privacy pitfalls for the unwary. We assembled a corpus of commercial mailing-list emails, and find a network of hundreds of third parties that track email recipients via methods such as embedded pixels. About 30% of emails leak the recipient’s email address to one or more of these third parties when they are viewed. In the majority of cases, these leaks are intentional on the part of email senders, and further leaks occur if the recipient clicks links in emails. Mail servers and clients may employ a variety of defenses, but we analyze 16 servers and clients and find that they are far from comprehensive. We propose, prototype, and evaluate a new defense, namely stripping tracking tags from emails based on enhanced versions of existing web tracking protection lists.

Blog post on the research.

Posted on October 3, 2017 at 6:45 AM • View Comments

Using Ultrasonic Beacons to Track Users

I’ve previously written about ad networks using ultrasonic communications to jump from one device to another. The idea is for devices like televisions to play ultrasonic codes in advertisements and for nearby smartphones to detect them. This way the two devices can be linked.

Creepy, yes. And also increasingly common, as this research demonstrates:

Privacy Threats through Ultrasonic Side Channels on Mobile Devices

by Daniel Arp, Erwin Quiring, Christian Wressnegger and Konrad Rieck

Abstract: Device tracking is a serious threat to the privacy of users, as it enables spying on their habits and activities. A recent practice embeds ultrasonic beacons in audio and tracks them using the microphone of mobile devices. This side channel allows an adversary to identify a user’s current location, spy on her TV viewing habits or link together her different mobile devices. In this paper, we explore the capabilities, the current prevalence and technical limitations of this new tracking technique based on three commercial tracking solutions. To this end, we develop detection approaches for ultrasonic beacons and Android applications capable of processing these. Our findings confirm our privacy concerns: We spot ultrasonic beacons in various web media content and detect signals in 4 of 35 stores in two European cities that are used for location tracking. While we do not find ultrasonic beacons in TV streams from 7 countries, we spot 234 Android applications that are constantly listening for ultrasonic beacons in the background without the user’s knowledge.

News article. BoingBoing post.

Posted on May 8, 2017 at 9:16 AM • View Comments

Reading Analytics and Privacy

Interesting paper: “The rise of reading analytics and the emerging calculus of reading privacy in the digital world,” by Clifford Lynch:

Abstract: This paper studies emerging technologies for tracking reading behaviors (“reading analytics”) and their implications for reader privacy, attempting to place them in a historical context. It discusses what data is being collected, to whom it is available, and how it might be used by various interested parties (including authors). I explore means of tracking what’s being read, who is doing the reading, and how readers discover what they read. The paper includes two case studies: mass-market e-books (both directly acquired by readers and mediated by libraries) and scholarly journals (usually mediated by academic libraries); in the latter case I also provide examples of the implications of various authentication, authorization and access management practices on reader privacy. While legal issues are touched upon, the focus is generally pragmatic, emphasizing technology and marketplace practices. The article illustrates the way reader privacy concerns are shifting from government to commercial surveillance, and the interactions between government and the private sector in this area. The paper emphasizes U.S.-based developments.

Posted on April 27, 2017 at 6:20 AM • View Comments

De-Anonymizing Browser History Using Social-Network Data

Interesting research: “De-anonymizing Web Browsing Data with Social Networks“:

Abstract: Can online trackers and network adversaries de-anonymize web browsing data readily available to them? We show—theoretically, via simulation, and through experiments on real user data—that de-identified web browsing histories can be linked to social media profiles using only publicly available data. Our approach is based on a simple observation: each person has a distinctive social network, and thus the set of links appearing in one’s feed is unique. Assuming users visit links in their feed with higher probability than a random user, browsing histories contain tell-tale marks of identity. We formalize this intuition by specifying a model of web browsing behavior and then deriving the maximum likelihood estimate of a user’s social profile. We evaluate this strategy on simulated browsing histories, and show that given a history with 30 links originating from Twitter, we can deduce the corresponding Twitter profile more than 50% of the time. To gauge the real-world effectiveness of this approach, we recruited nearly 400 people to donate their web browsing histories, and we were able to correctly identify more than 70% of them. We further show that several online trackers are embedded on sufficiently many websites to carry out this attack with high accuracy. Our theoretical contribution applies to any type of transactional data and is robust to noisy observations, generalizing a wide range of previous de-anonymization attacks. Finally, since our attack attempts to find the correct Twitter profile out of over 300 million candidates, it is—to our knowledge—the largest scale demonstrated de-anonymization to date.

Posted on February 10, 2017 at 8:25 AM • View Comments

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Sidebar photo of Bruce Schneier by Joe MacInnis.