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Wi-Fi Hotspot Tracking

Free Wi-Fi hotspots can track your location, even if you don’t connect to them. This is because your phone or computer broadcasts a unique MAC address.

What distinguishes location-based marketing hotspot providers like Zenreach and Euclid is that the personal information you enter in the captive portal­ — like your email address, phone number, or social media profile­ — can be linked to your laptop or smartphone’s Media Access Control (MAC) address. That’s the unique alphanumeric ID that devices broadcast when Wi-Fi is switched on.

As Euclid explains in its privacy policy, “…if you bring your mobile device to your favorite clothing store today that is a Location — ­and then a popular local restaurant a few days later that is also a Location­ — we may know that a mobile device was in both locations based on seeing the same MAC Address.”

MAC addresses alone don’t contain identifying information besides the make of a device, such as whether a smartphone is an iPhone or a Samsung Galaxy. But as long as a device’s MAC address is linked to someone’s profile, and the device’s Wi-Fi is turned on, the movements of its owner can be followed by any hotspot from the same provider.

“After a user signs up, we associate their email address and other personal information with their device’s MAC address and with any location history we may previously have gathered (or later gather) for that device’s MAC address,” according to Zenreach’s privacy policy.

The defense is to turn Wi-Fi off on your phone when you’re not using it.

EDITED TO ADD: Note that the article is from 2018. Not that I think anything is different today….

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Russians Hack FBI Comms System

Yahoo News reported that the Russians have successfully targeted an FBI communications system:

American officials discovered that the Russians had dramatically improved their ability to decrypt certain types of secure communications and had successfully tracked devices used by elite FBI surveillance teams. Officials also feared that the Russians may have devised other ways to monitor U.S. intelligence communications, including hacking into computers not connected to the internet. Senior FBI and CIA officials briefed congressional leaders on these issues as part of a wide-ranging examination on Capitol Hill of U.S. counterintelligence vulnerabilities.

These compromises, the full gravity of which became clear to U.S. officials in 2012, gave Russian spies in American cities including Washington, New York and San Francisco key insights into the location of undercover FBI surveillance teams, and likely the actual substance of FBI communications, according to former officials. They provided the Russians opportunities to potentially shake off FBI surveillance and communicate with sensitive human sources, check on remote recording devices and even gather intelligence on their FBI pursuers, the former officials said.

It’s unclear whether the Russians were able to recover encrypted data or just perform traffic analysis. The Yahoo story implies the former; the NBC News story says otherwise. It’s hard to tell if the reporters truly understand the difference. We do know, from research Matt Blaze and others did almost ten years ago, that at least one FBI radio system was horribly insecure in practice — but not in a way that breaks the encryption. Its poor design just encourages users to turn off the encryption.

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Modifying a Tesla to Become a Surveillance Platform

From DefCon:

At the Defcon hacker conference today, security researcher Truman Kain debuted what he calls the Surveillance Detection Scout. The DIY computer fits into the middle console of a Tesla Model S or Model 3, plugs into its dashboard USB port, and turns the car’s built-in cameras­ — the same dash and rearview cameras providing a 360-degree view used for Tesla’s Autopilot and Sentry features­ — into a system that spots, tracks, and stores license plates and faces over time. The tool uses open source image recognition software to automatically put an alert on the Tesla’s display and the user’s phone if it repeatedly sees the same license plate. When the car is parked, it can track nearby faces to see which ones repeatedly appear. Kain says the intent is to offer a warning that someone might be preparing to steal the car, tamper with it, or break into the driver’s nearby home.

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How Apple’s “Find My” Feature Works

Matthew Green intelligently speculates about how Apple’s new “Find My” feature works.

If you haven’t already been inspired by the description above, let me phrase the question you ought to be asking: how is this system going to avoid being a massive privacy nightmare?

Let me count the concerns:

  • If your device is constantly emitting a BLE signal that uniquely identifies it, the whole world is going to have (yet another) way to track you. Marketers already use WiFi and Bluetooth MAC addresses to do this: Find My could create yet another tracking channel.

  • It also exposes the phones who are doing the tracking. These people are now going to be sending their current location to Apple (which they may or may not already be doing). Now they’ll also be potentially sharing this information with strangers who “lose” their devices. That could go badly.

  • Scammers might also run active attacks in which they fake the location of your device. While this seems unlikely, people will always surprise you.

The good news is that Apple claims that their system actually does provide strong privacy, and that it accomplishes this using clever cryptography. But as is typical, they’ve declined to give out the details how they’re going to do it. Andy Greenberg talked me through an incomplete technical description that Apple provided to Wired, so that provides many hints. Unfortunately, what Apple provided still leaves huge gaps. It’s into those gaps that I’m going to fill in my best guess for what Apple is actually doing.

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On Surveillance in the Workplace

Data & Society just published a report entitled “Workplace Monitoring & Surveillance“:

This explainer highlights four broad trends in employee monitoring and surveillance technologies:

  • Prediction and flagging tools that aim to predict characteristics or behaviors of employees or that are designed to identify or deter perceived rule-breaking or fraud. Touted as useful management tools, they can augment biased and discriminatory practices in workplace evaluations and segment workforces into risk categories based on patterns of behavior.

  • Biometric and health data of workers collected through tools like wearables, fitness tracking apps, and biometric timekeeping systems as a part of employer- provided health care programs, workplace wellness, and digital tracking work shifts tools. Tracking non-work-related activities and information, such as health data, may challenge the boundaries of worker privacy, open avenues for discrimination, and raise questions about consent and workers’ ability to opt out of tracking.

  • Remote monitoring and time-tracking used to manage workers and measure performance remotely. Companies may use these tools to decentralize and lower costs by hiring independent contractors, while still being able to exert control over them like traditional employees with the aid of remote monitoring tools. More advanced time-tracking can generate itemized records of on-the-job activities, which can be used to facilitate wage theft or allow employers to trim what counts as paid work time.

  • Gamification and algorithmic management of work activities through continuous data collection. Technology can take on management functions, such as sending workers automated “nudges” or adjusting performance benchmarks based on a worker’s real-time progress, while gamification renders work activities into competitive, game-like dynamics driven by performance metrics. However, these practices can create punitive work environments that place pressures on workers to meet demanding and shifting efficiency benchmarks.

In a blog post about this report, Cory Doctorow mentioned “the adoption curve for oppressive technology, which goes, ‘refugee, immigrant, prisoner, mental patient, children, welfare recipient, blue collar worker, white collar worker.'” I don’t agree with the ordering, but the sentiment is correct. These technologies are generally used first against people with diminished rights: prisoners, children, the mentally ill, and soldiers.

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Friday Squid Blogging: A Tracking Device for Squid

Really:

After years of “making do” with the available technology for his squid studies, Mooney created a versatile tag that allows him to research squid behavior. With the help of Kakani Katija, an engineer adapting the tag for jellyfish at California’s Monterey Bay Aquarium Research Institute (MBARI), Mooney’s team is creating a replicable system flexible enough to work across a range of soft-bodied marine animals. As Mooney and Katija refine the tags, they plan to produce an adaptable, open-source package that scientists researching other marine invertebrates can also use.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Read my blog posting guidelines here.

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New Ways to Track Internet Browsing

Interesting research on web tracking: “Who Left Open the Cookie Jar? A Comprehensive Evaluation of Third-Party Cookie Policies:

Abstract: Nowadays, cookies are the most prominent mechanism to identify and authenticate users on the Internet. Although protected by the Same Origin Policy, popular browsers include cookies in all requests, even when these are cross-site. Unfortunately, these third-party cookies enable both cross-site attacks and third-party tracking. As a response to these nefarious consequences, various countermeasures have been developed in the form of browser extensions or even protection mechanisms that are built directly into the browser.

In this paper, we evaluate the effectiveness of these defense mechanisms by leveraging a framework that automatically evaluates the enforcement of the policies imposed to third-party requests. By applying our framework, which generates a comprehensive set of test cases covering various web mechanisms, we identify several flaws in the policy implementations of the 7 browsers and 46 browser extensions that were evaluated. We find that even built-in protection mechanisms can be circumvented by multiple novel techniques we discover. Based on these results, we argue that our proposed framework is a much-needed tool to detect bypasses and evaluate solutions to the exposed leaks. Finally, we analyze the origin of the identified bypass techniques, and find that these are due to a variety of implementation, configuration and design flaws.

The researchers discovered many new tracking techniques that work despite all existing anonymous browsing tools. These have not yet been seen in the wild, but that will change soon.

Three news articles. BoingBoing post.

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