3 ms·

If I don't even know what data type my input is, what meaningful output can the system give? I wouldn't consider that cheating. Also, you can do feature engine

If I don't even know what data type my input is, what meaningful output can the system give? I wouldn't consider that cheating. Also, you can do feature engineering on a training set that you collect, as long as that is similar to the distribution of end user inputs. That's a pretty standard ML workflow.

personjerry 7y ago
The entire point of encrypted data is you don't know what's in there. What they're claiming is they've worked around this problem, but they haven't, because they're subtly incorporating outside information: specifically that they know what the data looks like already. From a true third-party looking at encrypted data, you would have no idea. What does your statement about feature engineering have to do with encrypted data? Maybe I can clarify with an example: You can't transform a waveform into more usable features with a fourier transform if the waveform is encrypted.
- karpierz 7y ago
  You seem to be misunderstanding the value proposition. Suppose that I have some of my personal medical data, and I'd like to use it to try and detect early symptoms of HIV. But because there's a stigma against HIV and I don't trust medical providers to handle my data correctly, I refuse to hand over my data to a third party service unencrypted. Without homomorphic encryption, I'd be out of luck; I couldn't use a third-party service to analyze my data and return a prediction. With homomorphic encryption: - I encrypt my data locally - Send the encrypted bits over to the third-party - The third-party uses their ML algorithm to compute a prediction - They send back an encrypted prediction - I decrypt the prediction locally and get my results. At no point in this process can anyone, aside from me, see either the input data or the output.

3 ms·

If I don't even know what data type my input is, what meaningful output can the system give? I wouldn't consider that cheating. Also, you can do feature engine

by sthatipamala 7y ago

personjerry 7y ago
The entire point of encrypted data is you don't know what's in there. What they're claiming is they've worked around this problem, but they haven't, because they're subtly incorporating outside information: specifically that they know what the data looks like already. From a true third-party looking at encrypted data, you would have no idea. What does your statement about feature engineering have to do with encrypted data? Maybe I can clarify with an example: You can't transform a waveform into more usable features with a fourier transform if the waveform is encrypted.
- karpierz 7y ago
  You seem to be misunderstanding the value proposition. Suppose that I have some of my personal medical data, and I'd like to use it to try and detect early symptoms of HIV. But because there's a stigma against HIV and I don't trust medical providers to handle my data correctly, I refuse to hand over my data to a third party service unencrypted. Without homomorphic encryption, I'd be out of luck; I couldn't use a third-party service to analyze my data and return a prediction. With homomorphic encryption: - I encrypt my data locally - Send the encrypted bits over to the third-party - The third-party uses their ML algorithm to compute a prediction - They send back an encrypted prediction - I decrypt the prediction locally and get my results. At no point in this process can anyone, aside from me, see either the input data or the output.