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GPUaccelerated OP t1_iu4wl57 wrote

Reply to comment by suflaj in Do companies actually care about their model's training/inference speed? by GPUaccelerated

That's right but sometimes data sensitivity prevents the use of cloud.

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suflaj t1_iu4zaqo wrote

Well then it's a matter of trust - every serious cloud provider has a privacy policy that claims nothing is logged. Of course, you don't have to trust this, but this is a liability for the cloud provider, so you get to shift the blame if something goes wrong. And I'd argue that for most companies the word of a cloud peovider means more than your word, since they've got much to lose.

It's also standard practice to use end-to-end encryption, with some using end-to-end encrypted models. I don't really see a way how our company would handle personal data and retain samples in a GDPR compliant way without proprietary models in the cloud.

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GPUaccelerated OP t1_iuimm3t wrote

Right, but for example in the medical field, It's not a trust issue. It's a matter of laws that prevent patient data from leaving the physician's premise.

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suflaj t1_iuioq7c wrote

Which laws?

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GPUaccelerated OP t1_iuitwy2 wrote

not exactly sure, i'm not a lawyer. But it's something that gets taken very seriously by a lot of my medical field clients. Its definitely something for their side, not mine. I just help those specific clients go on-prem

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suflaj t1_iujhefz wrote

I asked for the specific law so I could show you that it cannot apply to end-to-end encrypted systems, which either have partly destroyed information, or the information that leaves the premises is not comprehensible to anything but the model and there is formal proof that it is infeasible to crack it.

These are all long solved problems, the only hard part is doing hashing without losing too much information, or encryption compact enough to both fit into the model and be comprehensible to it.

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