论文标题
多中央差异隐私
Multi-Central Differential Privacy
论文作者
论文摘要
通常在中央模型中研究差异隐私，其中受信任的“聚合器”持有所有个人的敏感数据，并负责保护其隐私。一个流行的替代方法是当地模型，其中聚合者是不信任的，而每个人都对自己的隐私负责。本地模型的分散隐私保证在统计公用事业或计算复杂性方面以高价。因此，研究了中间模型，例如改组模型和PAN隐私，以尝试达到两全其美。 在本说明中，我们为差异隐私提出了一个中间信任模型，我们称之为多中央模型。这里有多个聚合器，我们只是假设他们不会偶然地串通。该模型在避免本地模型的价格的同时放松了中央模型的信任要求。我们激励该模型，并为其提供一些简单有效的算法。我们认为该模型是进一步研究的有希望的方向。
Differential privacy is typically studied in the central model where a trusted "aggregator" holds the sensitive data of all the individuals and is responsible for protecting their privacy. A popular alternative is the local model in which the aggregator is untrusted and instead each individual is responsible for their own privacy. The decentralized privacy guarantee of the local model comes at a high price in statistical utility or computational complexity. Thus intermediate models such as the shuffled model and pan privacy have been studied in an attempt to attain the best of both worlds. In this note, we propose an intermediate trust model for differential privacy, which we call the multi-central model. Here there are multiple aggregators and we only assume that they do not collude nefariously. This model relaxes the trust requirements of the central model while avoiding the price of the local model. We motivate this model and provide some simple and efficient algorithms for it. We argue that this model is a promising direction for further research.
