Mireshghallah, Fatemehsadat

Journals note that prediction services can still make accurate predictions using a fraction of the data collected from a user device. They propose Cloak, which suppresses non-pertinent features (i.e. those features which can consistently tolerate addition of noise without degrading utility) to the prediction task. Cloak has a provable degree of privacy, and unlike cryptographic techniques, does not degrade prediction latency. Using the training data, labels, a pre-trained model and a privacy-utility knob, they (1) find the pertinent features through perturbation training, and (2) learn utility-preserving constant values for suppressing the non-pertinent data....