If your usual YouTube viewing selection covers a wild and random variety of music, tech subjects, cooking, history, and anything in-between, you will sooner or later be baffled by some of the “Recommended for you” videos showing up. When it features a ten-hour mix of Soviet propaganda choir music, you might start wondering what a world taken over by an artificial intelligence might actually look like, and realize that your browser’s incognito / private mode really isn’t just for shopping birthday presents in secret. Things get a bit tricky if you actually enjoy or even rely on the whole subscribing-to-channels concept though, which is naturally difficult to bring in line with privacy in today’s world of user-data-driven business models.
Entering the conversation: the FreeTube project, a cross-platform application whose mission is to regain privacy and put the control of one’s data back into the user’s hands. Bypassing YouTube and its player, the watch history and subscriptions — which are still possible — are kept only locally on your own computer, and you can import either of them from YouTube and export them to use within FreeTube on another device (or back to YouTube). Even better, it won’t load a video’s comments without explicitly telling it to, and of course it keeps out the ads as well.
Originally, the Invidious API was used to get the content, and is still supported as fallback option, but FreeTube comes with its own extractor API nowadays. All source code is available from the project’s GitHub repository, along with pre-built packages for Linux (including ARM), Windows, and Mac. The application itself is created using Electron, which might raise a few eyebrows as it packs an entire browser rendering engine and essentially just disguises a website as standalone application. But as the FAQ addresses, this allows easy cross-platform support and helps the project, which would have otherwise been Linux-only, to reach as many people as possible. That’s a valid point in our book.
Keep in mind though, FreeTube is only a player, and more of a wrapper around YouTube itself, so YouTube will still see your IP and interaction with the service. If you want to be fully anonymous, this isn’t a silver bullet and will require additional steps like using a VPN. Unlike other services that you could replace with a local alternative to avoid tracking and profiling, content services are just a bit trickier if you want to actually have a useful selection. So this is a great compromise that also just works out of the box for everyone regardless of their technical background. Let’s just hope it won’t break too much next time some API changes.
The average person has become depressingly comfortable with the surveillance dystopia we live in. For better or for worse, they’ve come to accept the fact that data about their lives is constantly being collected and analyzed. We’re at the point where a sizable chunk of people believe their smartphone is listening in on their personal conversations and tailoring advertisements to overheard keywords, yet it’s unlikely they’re troubled enough by the idea that they’d actually turn off the phone.
But even the most privacy-conscious among us probably wouldn’t consider our water usage to be any great secret. After all, what could anyone possibly learn from studying how much water you use? Well, as [Jason Bowling] has proven with his fascinating water-meter data research, it turns out you can learn a whole hell of a lot by watching water use patterns. By polling a whole-house water flow meter every second and running the resulting data through various machine learning algorithms, [Jason] found there is a lot of personal information hidden in this seemingly innocuous data stream.
The key is that every water-consuming device in your home has a discernible “fingerprint” that, with enough time, can be identified and tracked. Appliances that always use the same amount of water, like an ice maker or dishwasher, are obvious spikes among the noise. But [Jason] was able to pick up even more subtle differences, such as which individual toilet in the home had been flushed and when.
Further, if you watch the data long enough, you can even start to identify information about individuals within the home. Want to know how many kids are in the family? Monitoring for frequent baths that don’t fill the tub all the way would be a good start. Want to know how restful somebody’s sleep was? A count of how many times the toilet was flushed overnight could give you an idea.
In terms of the privacy implications of what [Jason] has discovered, we’re mildly horrified. Especially since we’ve already seen how utility meters can be sniffed with nothing more exotic than an RTL-SDR. But on the other hand, his write-up is a fantastic look at how you can put machine learning to work in even the most unlikely of applications. The information he’s collected on using Python to classify time series data and create visualizations will undoubtedly be of interest to anyone who’s got a big data problem they’re looking to solve.
[Greg Shikhman] is at Octopart this summer as a software development intern. In between the time he’s spending getting coffee for the other devs, he came up with historical pricing for thousands of components available at Octopart
There’s a lot of cool data out there, like this bit of pricing info for a 555 timer. We’re guessing a few people were out of stock of 555s around the end of May, explaining why they were selling (well, available for) $1.68 a piece. If you’re trying to source components, it might be worth your while to check out Octopart’s historical price index. Buying a PIC microcontroller last August was a roll of the dice; in one day the price changed from $5 to just over $2.
With all this data, it’s even possible to data mine for real life events unrelated to shipping and stocking issues. Japanese manufacturer Renesas was hit pretty hard by last year’s earthquake, and this shows up in the historical prices for one of their microcontrollers. Not bad for an intern’s project.