Secure Data Sharing With Flow Model

We consider a variant of this problem, where instead of requiring the data to be completely private so that no one gets In the classical multi-party computation setting, any information about it, we only require data to be partially multiple parties jointly compute a function without private. That is, no one can efficiently recover the original revealing their own input data. We consider a data, but users can extract other useful information from the variant of this problem, where the input data can encrypted data. Although being different, our requirement be shared for machine learning training purposes, has the flavor of differential privacy (Dwork et al., 2006), but the data are also encrypted so that they cannot e.g., users can obtain the average salary of all employees, be recovered by other parties. We present a but cannot figure out the salary of each individual.