In this work we introduce an interactive variant of joint differential privacy towards handling online processes in which existing privacy definitions seem too restrictive. We study basic properties of this definition and demonstrate that it satisfies (suitable variants) of group privacy, composition, and post processing. We then study the cost of interactive joint privacy in the basic setting of online classification. We show that any (possibly non-private) learning rule can be effectively transformed to a private learning rule with only a polynomial overhead in the mistake bound. This demonstrates a stark difference with more restrictive notions of privacy such as the one studied by Golowich and Livni (2021), where only a double exponential overhead on the mistake bound is known (via an information theoretic upper bound).

翻译：本文提出了一种交互式联合差分隐私变体，以处理现有隐私定义在在线过程中显得过于严格的问题。我们研究了该定义的基本性质，并证明它满足（适当变体下的）群体隐私、组合性和后处理性。随后，我们分析了在线分类基础场景中交互式联合隐私的代价。我们证明，任何（可能非私有的）学习规则均可有效转化为私有学习规则，且仅在错误界上产生多项式开销。这一结果与Golowich和Livni（2021）研究的更严格隐私概念形成了显著差异——后者在错误界上已知存在双指数开销（基于信息论上界）。