论文标题
高维空间中的LDP机制的公用事业分析和增强
Utility Analysis and Enhancement of LDP Mechanisms in High-Dimensional Space
论文作者
论文摘要
当地的差异隐私（LDP）是一种流行的保密数据收集机制，它在本地且仅将其信息的嘈杂版本发送给聚合器。在自然党中，数据收集器可以获得准确的统计信息，而无需访问原始数据，从而确保隐私。但是，最不发达国家的主要缺点是它在高维空间中令人失望的效用。尽管已经提出了各种减少扰动方案来减少扰动，但它们在收集器侧面具有相同和天真的聚集机制。在本文中，我们首先提出了一个分析框架，通常可以在高维空间中测量自由顾展机制的实用程序，该机制可以在不进行任何实验的情况下基准基准现有和将来的LDP机制。基于此，该框架进一步揭示了天真的聚集在高维空间中是最佳的，并且还有很大的改进空间。在此激励的情况下，我们提出了一种重新校准协议HDR4ME进行高维平均估计，这改善了现有的LDP机制的实用性，而无需对其进行任何更改。理论分析和广泛的实验都证实了我们框架和协议的一般性和有效性。
Local differential privacy (LDP), which perturbs the data of each user locally and only sends the noisy version of her information to the aggregator, is a popular privacy-preserving data collection mechanism. In LDP, the data collector could obtain accurate statistics without access to original data, thus guaranteeing privacy. However, a primary drawback of LDP is its disappointing utility in high-dimensional space. Although various LDP schemes have been proposed to reduce perturbation, they share the same and naive aggregation mechanism at the side of the collector. In this paper, we first bring forward an analytical framework to generally measure the utilities of LDP mechanisms in high-dimensional space, which can benchmark existing and future LDP mechanisms without conducting any experiment. Based on this, the framework further reveals that the naive aggregation is sub-optimal in high-dimensional space, and there is much room for improvement. Motivated by this, we present a re-calibration protocol HDR4ME for high-dimensional mean estimation, which improves the utilities of existing LDP mechanisms without making any change to them. Both theoretical analysis and extensive experiments confirm the generality and effectiveness of our framework and protocol.
