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.
Posted on June 20, 2019 at 12:27 PM •
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.
Posted on March 12, 2019 at 6:38 AM •
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.
Posted on February 22, 2019 at 4:09 PM •
A year ago, the Norwegian Consumer Council published an excellent security analysis of children’s GPS-connected smart watches. The security was terrible. Not only could parents track the children, anyone else could also track the children.
A recent analysis checked if anything had improved after that torrent of bad press. Short answer: no.
Guess what: a train wreck. Anyone could access the entire database, including real time child location, name, parents details etc. Not just Gator watches either — the same back end covered multiple brands and tens of thousands of watches
The Gator web backend was passing the user level as a parameter. Changing that value to another number gave super admin access throughout the platform. The system failed to validate that the user had the appropriate permission to take admin control!
This means that an attacker could get full access to all account information and all watch information. They could view any user of the system and any device on the system, including its location. They could manipulate everything and even change users’ emails/passwords to lock them out of their watch.
In fairness, upon our reporting of the vulnerability to them, Gator got it fixed in 48 hours.
This is a lesson in the limits of naming and shaming: publishing vulnerabilities in an effort to get companies to improve their security. If a company is specifically named, it is likely to improve the specific vulnerability described. But that is unlikely to translate into improved security practices in the future. If an industry, or product category, is named generally, nothing is likely to happen. This is one of the reasons I am a proponent of regulation.
EDITED TO ADD (2/13): The EU has acted in a similar case.
Posted on January 31, 2019 at 10:30 AM •
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.
Posted on August 17, 2018 at 5:26 AM •
Google is tracking you, even if you turn off tracking:
Google says that will prevent the company from remembering where you’ve been. Google’s support page on the subject states: “You can turn off Location History at any time. With Location History off, the places you go are no longer stored.”
That isn’t true. Even with Location History paused, some Google apps automatically store time-stamped location data without asking.
For example, Google stores a snapshot of where you are when you merely open its Maps app. Automatic daily weather updates on Android phones pinpoint roughly where you are. And some searches that have nothing to do with location, like “chocolate chip cookies,” or “kids science kits,” pinpoint your precise latitude and longitude - accurate to the square foot - and save it to your Google account.
On the one hand, this isn’t surprising to technologists. Lots of applications use location data. On the other hand, it’s very surprising — and counterintuitive — to everyone else. And that’s why this is a problem.
I don’t think we should pick on Google too much, though. Google is a symptom of the bigger problem: surveillance capitalism in general. As long as surveillance is the business model of the Internet, things like this are inevitable.
Posted on August 14, 2018 at 6:22 AM •
The New York Times is reporting about a company called Securus Technologies that gives police the ability to track cell phone locations without a warrant:
The service can find the whereabouts of almost any cellphone in the country within seconds. It does this by going through a system typically used by marketers and other companies to get location data from major cellphone carriers, including AT&T, Sprint, T-Mobile and Verizon, documents show.
Boing Boing post.
EDITED TO ADD (6/12): Securus was hacked.
Posted on May 16, 2018 at 6:16 AM •
Interesting research: “‘Won’t Somebody Think of the Children?’ Examining COPPA Compliance at Scale“:
Abstract: We present a scalable dynamic analysis framework that allows for the automatic evaluation of the privacy behaviors of Android apps. We use our system to analyze mobile apps’ compliance with the Children’s Online Privacy Protection Act (COPPA), one of the few stringent privacy laws in the U.S. Based on our automated analysis of 5,855 of the most popular free children’s apps, we found that a majority are potentially in violation of COPPA, mainly due to their use of third-party SDKs. While many of these SDKs offer configuration options to respect COPPA by disabling tracking and behavioral advertising, our data suggest that a majority of apps either do not make use of these options or incorrectly propagate them across mediation SDKs. Worse, we observed that 19% of children’s apps collect identifiers or other personally identifiable information (PII) via SDKs whose terms of service outright prohibit their use in child-directed apps. Finally, we show that efforts by Google to limit tracking through the use of a resettable advertising ID have had little success: of the 3,454 apps that share the resettable ID with advertisers, 66% transmit other, non-resettable, persistent identifiers as well, negating any intended privacy-preserving properties of the advertising ID.
Posted on April 13, 2018 at 6:43 AM •
Ross Anderson has a really interesting paper on tracing stolen bitcoin. From a blog post:
Previous attempts to track tainted coins had used either the “poison” or the “haircut” method. Suppose I open a new address and pay into it three stolen bitcoin followed by seven freshly-mined ones. Then under poison, the output is ten stolen bitcoin, while under haircut it’s ten bitcoin that are marked 30% stolen. After thousands of blocks, poison tainting will blacklist millions of addresses, while with haircut the taint gets diffused, so neither is very effective at tracking stolen property. Bitcoin due-diligence services supplant haircut taint tracking with AI/ML, but the results are still not satisfactory.
We discovered that, back in 1816, the High Court had to tackle this problem in Clayton’s case, which involved the assets and liabilities of a bank that had gone bust. The court ruled that money must be tracked through accounts on the basis of first-in, first out (FIFO); the first penny into an account goes to satisfy the first withdrawal, and so on.
Ilia Shumailov has written software that applies FIFO tainting to the blockchain and the results are impressive, with a massive improvement in precision. What’s more, FIFO taint tracking is lossless, unlike haircut; so in addition to tracking a stolen coin forward to find where it’s gone, you can start with any UTXO and trace it backwards to see its entire ancestry. It’s not just good law; it’s good computer science too.
Posted on March 28, 2018 at 6:30 AM •
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.
Posted on December 15, 2017 at 6:18 AM •
Sidebar photo of Bruce Schneier by Joe MacInnis.