PrivBV:Distance-Aware Encoding for Distributed Data with Local Differential Privacy  

作　　者：Lin Sun Guolou Ping Xiaojun Ye 

机构地区：[1]School of Software,Tsinghua University,Beijing 100084,China

出　　处：《Tsinghua Science and Technology》2022年第2期412-421,共10页清华大学学报（自然科学版（英文版）

基　　金：supported by National Key Research and Development Program of China(Nos.2019QY1402 and 2016YFB0800901)。

摘　　要：Recently,local differential privacy(LDP)has been used as the de facto standard for data sharing and analyzing with high-level privacy guarantees.Existing LDP-based mechanisms mainly focus on learning statistical information about the entire population from sensitive data.For the first time in the literature,we use LDP for distance estimation between distributed data to support more complicated data analysis.Specifically,we propose PrivBV—a locally differentially private bit vector mechanism with a distance-aware property in the anonymized space.We also present an optimization strategy for reducing privacy leakage in the high-dimensional space.The distance-aware property of PrivBV brings new insights into complicated data analysis in distributed environments.As study cases,we show the feasibility of applying PrivBV to privacy-preserving record linkage and non-interactive clustering.Theoretical analysis and experimental results demonstrate the effectiveness of the proposed scheme.

关 键 词：local differential privacy privacy-preserving data publishing non-interactive clustering 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论] TP309[自动化与计算机技术—计算机科学与技术]