I’m going insane. I cannot for the life of me find a suitable way to listen to music privately. I’m on iOS, and I don’t know whether to just stick to Apple Music or give up on music in general (I tried, TRIED to go local, but all the apps are shitty). Any way to listen to music and not have your data compromised? Should I just stick to Apple Music and hope that laws change (maybe something like EU’s DMA?)
Edit: Hey all! First of all, thank you so much for all the recommendations! I’ve discovered so many great apps and tools I didn’t even know existed (and it has also brought my hopes up for privacy in general). Even though it’s still not perfect, I’ve been using foobar2000 on iOS, downloading music I find (I’m still using Apple Music for discovery, but will probably stop when my subscription ends this month). For desktop I’m using HyperPipe, which although a little buggy at times is so awesome! One thing I do miss about this system is the lack of lyrics. Apple Music has such a beautiful UI when it comes with lyrics, but you can’t have it all when it comes to privacy it seems. Thanks for the amazing discussion! I’m so far loving Lemmy ;)
Guess I’ll be the one to ask. Why do you want music privacy?
You assume to know what kind of information is leaking when you use these apps.
How did you come to have these proprietary information?
Unless you have proof otherwise - I’m going to assume that they have access to: My location, my ip, typing speed and common spelling mistakes, IMEI identifiers , installed social media apps…
Now all it takes to make an online profile about you is just one more app or website that leaks the same kind of information
Yea of all the things to keep private, my music listening habits isn’t one of them. Tbh the algorithms give me good recommendations
The companies that aggregate data and find patterns in them can probably predict a lot about you from your music listening habits, when they correlate it with data about other people, or even about yourself. The power of profiling isn’t in any specific data but in the patterns that emerge when you gather a lot of diverse data about a lot of people.
Listening habits will tell them about your routine, including where you are, when, and when you have time to listen to music (so, therefore, when you don’t). If you don’t ever listen to music between 8pm and 10pm, for example, it may indicate that you have children to put to bed. If you listen mostly between 12am and 5am it may indicate that you work a nightshift. If you listen between 8 and 9 and again between 5 and 6, you’re probably a commuter. When you listen on a computer and when on mobile will tell them something too. And these are only the obvious patterns that I can think of off the top of my head. AI systems running on big data are designed to find patterns humans don’t notice.
And of course the styles of music you listen to will be readily correlated with demographic profiles. When you feed data into AI systems designed to find patterns people can’t spot, you’ll find the most unlikely data reveals things about people that they’d never imagine you could know.
Given this, it’s entirely possible that your music listening telemetry could eventually influence your credit score, your insurance premiums, your qualification for security clearances or your employability. You don’t know where the data ends up, or with what other data it’s correlated. This is why it’s desirable in general to keep data private if it’s not needed to provide the service.