Learning across Data Owners with Joint Differential Privacy. (arXiv:2305.15723v1 [cs.LG])

In this paper, we study the setting in which data owners train machine
learning models collaboratively under a privacy notion called joint
differential privacy [Kearns et al., 2018]. In this setting, the model trained
for each data owner $j$ uses $j$'s data without privacy consideration and other
owners' data with differential privacy guarantees. This setting was initiated
in [Jain et al., 2021] with a focus on linear regressions. In this paper, we
study this setting for stochastic convex optimization (SCO). …

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