Paper Info
Title
Locally Private Frequency Estimation Of Physical Symptoms For Infectious Disease Analysis In Internet Of Medical Things
Abstract
Frequency estimation of physical symptoms for peoples is the most direct way to analyze and predict infectious diseases. In Internet of medical Things (IoMT), it is efficient and convenient for users to report their physical symptoms to hospitals or disease prevention departments by various mobile devices. Unfortunately, it usually brings leakage risk of these symptoms since data receivers may be untrusted. As a strong metric for health privacy, local differential privacy (LDP) requires that users should perturb their symptoms to prevent the risk. However, the widely-used data structure called sketch for frequency estimation does not satisfy the specified requirement. In this paper, we firstly define the problem of frequency estimation of physical symptoms under LDP. Then, we propose four different protocols, i.e., CMS-LDP, FCS-LDP, CS-LDP and FAS-LDP to solve the above problem. Next, we demonstrate that the designed protocols satisfy LDP and unbiased estimation. We also present two approaches to implement the key component (i.e., universal hash functions) of protocols. Finally, we conduct experiments to evaluate four protocols on two real-world datasets, representing two different distributions of physical symptoms. The results show that CMS-LDP and CS-LDP have relatively optimal utility for frequency estimation of physical symptoms in IoMT.
Year
DOI
Venue
2020
10.1016/j.comcom.2020.08.015
COMPUTER COMMUNICATIONS
Keywords
DocType
Volume
Health privacy, Frequency estimation, Local differential privacy, Infectious disease analysis
Journal
162
ISSN
Citations 
PageRank 
0140-3664
1
0.35
References 
Authors
25
6
Name
Order
Citations
PageRank
Xiaotong Wu1164.39
Mohammad Reza Khosravi251.12
Lianyong Qi356057.12
Genlin Ji443.10
Wanchun Dou587896.01
Xu Xiaolong642464.23