Collecting Geospatial Data with Local Differential Privacy for Personalized Services

被引：14

作者：

Hong, Daeyoung ^{[1
]}

Jung, Woohwan ^{[1
]}

Shim, Kyuseok ^{[1
]}

机构：

[1] Seoul Natl Univ, Seoul, South Korea

来源：

2021 IEEE 37TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2021) | 2021年

基金：

新加坡国家研究基金会;

关键词：

local differential privacy; geospatial data; RANGE QUERIES; RECOMMENDATION;

D O I：

10.1109/ICDE51399.2021.00230

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Geospatial data provides a lot of benefits for personalized services. However, since the geospatial data contains sensitive information about personal activities, collecting the raw data has a potential risk of leaking private information from the collectors. Recently, local differential privacy (LDP), which protects the privacy of users without trusting the collector, has been adopted to preserve privacy in many real applications. However, most of existing LDP algorithms focus on obtaining aggregated values such as mean and histogram from the collected data. In this paper, we investigate the problem of collecting the locations of individual users under LDP, and propose a perturbation mechanism designed carefully to reduce the error of each perturbed location according to the privacy budget and the domain size. In addition, we show the effectiveness of the proposed algorithm through experiments on various real datasets.

引用

页码：2237 / 2242

页数：6

共 23 条

[21] Spatial-Aware Hierarchical Collaborative Deep Learning for POI Recommendation [J].