论文标题
在线查询回答的多分析师差异隐私
Multi-Analyst Differential Privacy for Online Query Answering
论文作者
论文摘要
大多数差异化的机制都是为使用单个分析师而设计的。但是，实际上，通常有多个利益相关者具有不同的优先级，可能需要共享相同的隐私损失预算。这激发了用于多分析师差异隐私的公平预算共享问题。我们以前的工作定义了Desiderata，即在此空间中的任何机制都应满足并引入预算共享的方法，以提前知道查询。 我们将以前的工作扩展到多分析师的差异私有查询，以回答在线查询答案的情况下，查询一次是一个，并且必须在不了解以下查询的情况下回答。我们证明，在线案例中查询的未知顺序会导致在满足Desiderata时可以回答的查询数量的基本限制。作为回应，我们开发了两种机制，一种在所有情况下都满足了Desiderata，但受到基本限制，另一种将输入顺序随机的限制限制，以确保现有的在线查询答录机制可以满足Desiderata。
Most differentially private mechanisms are designed for the use of a single analyst. In reality, however, there are often multiple stakeholders with different and possibly conflicting priorities that must share the same privacy loss budget. This motivates the problem of equitable budget-sharing for multi-analyst differential privacy. Our previous work defined desiderata that any mechanism in this space should satisfy and introduced methods for budget-sharing in the offline case where queries are known in advance. We extend our previous work on multi-analyst differentially private query answering to the case of online query answering, where queries come in one at a time and must be answered without knowledge of the following queries. We demonstrate that the unknown ordering of queries in the online case results in a fundamental limit in the number of queries that can be answered while satisfying the desiderata. In response, we develop two mechanisms, one which satisfies the desiderata in all cases but is subject to the fundamental limitations, and another that randomizes the input order ensuring that existing online query answering mechanisms can satisfy the desiderata.
