DP-HORUS
Congcong Fu, Hui Li, Jian Lou, Jiangtao Cui · Proceedings of the 31st ACM International Conference on Information & Knowledge Management · 2022
Hierarchical count histograms is the task of publishing count statistics at different granularity as per hierarchy defined on a dimension table in a data warehouse, which has wide applications in On-line Analytical Processing (OLAP) scenarios. In this paper, we systematically investigate this task subjected to the rigorous privacy-preserving constraint under the untrusted server setting. Our study first reveals that the straightforward baseline approach of the local differential privacy fails to achieve a satisfactory privacy and utility tradeoff. We are thus motivated to propose DP-HORUS, a novel crypto-assisted Differentially Private framework for Hierarchical cOunt histogRams under Untrusted Server. DP-HORUS consists of a series of novel designs, including 1) Encrypted Hierarchical Tree (EHT) structure, which maintains the concept hierarchy in the input data; 2) Random Matrix (RM), which reduces communication and computational cost; 3) To further boosted the utility, we propose DP-HORUS+ encompassing two additional modules of Histograms Structure (HS) and Hierarchical Consistency (HC), which are respectively introduced to reduce the noise caused by data sparsity and to ensure the hierarchy consistency. We provide both theoretical analysis and extensive empirical study on both real-world and synthetic datasets, which demonstrates the superior utility of the proposed methods over the state-of-the-art solutions while ensuring strict privacy guarantee.