I can answer this. I'm a paying Dropbox customer and I didn't donate my data. The information is mine, and we can discuss using it, but my expectation of privacy includes Dropbox not using it for any purpose that isn't storing and serving it to me unless I've expressly stated otherwise.

While I'm not that interesting of a person, I do have an interest in things like my tax returns, financials, and other PII to not be in the hands of Dropbox employees.

Short answer: Dropbox is doing the right thing, and I really appreciate that.

Couldn't user data make it into the model, and be reverse-engineered from it, if you used it to train the model?

I mean, machine learning models aren't designed as one-way cryptographic hash functions, so you can't be sure that some of the training data can be inferred through the model's behavior.

Yes! there is an interesting paper on this subject: https://arxiv.org/abs/1609.02943

Counterpoint: https://arxiv.org/abs/1701.04082

Source: https://news.ycombinator.com/item?id=13824047

Preserving users' privacy first and foremost is absolutely the right thing to do here. I'm really glad Dropbox is willing to trade away a bit of model accuracy for better user privacy.

Because the ML models might start reproducing snippets of user data.

Perhaps they don't have the metadata for the data?