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Locally private frequency estimation of physical symptoms for infectious disease analysis in Internet of Medical Things.
Wu, Xiaotong; Khosravi, Mohammad Reza; Qi, Lianyong; Ji, Genlin; Dou, Wanchun; Xu, Xiaolong.
Afiliação
  • Wu X; School of Computer Science and Technology, Nanjing Normal University, PR China.
  • Khosravi MR; Department of Computer Engineering, Persian Gulf University, Bushehr, Iran.
  • Qi L; School of Information, Science and Engineering, Qufu Normal University, PR China.
  • Ji G; School of Computer Science and Technology, Nanjing Normal University, PR China.
  • Dou W; the State Key Lab for Novel Software Technology, Nanjing University, PR China.
  • Xu X; School of Computer and Software, Nanjing University of Information Science & Technology, PR China.
Comput Commun ; 162: 139-151, 2020 Oct 01.
Artigo em Inglês | MEDLINE | ID: mdl-32873996
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.
Texto completo: Disponível Coleções: Bases de dados internacionais Contexto em Saúde: ODS3 - Saúde e Bem-Estar Tema em saúde: Meta 3.3: Acabar com as doenças tropicais negligenciadas e combater as doenças transmissíveis Base de dados: MEDLINE Tipo de estudo: Estudo diagnóstico / Patient_preference / Estudo prognóstico Aspecto: Preferência do paciente Idioma: Inglês Revista: Comput Commun Ano de publicação: 2020 Tipo de documento: Artigo

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Texto completo: Disponível Coleções: Bases de dados internacionais Contexto em Saúde: ODS3 - Saúde e Bem-Estar Tema em saúde: Meta 3.3: Acabar com as doenças tropicais negligenciadas e combater as doenças transmissíveis Base de dados: MEDLINE Tipo de estudo: Estudo diagnóstico / Patient_preference / Estudo prognóstico Aspecto: Preferência do paciente Idioma: Inglês Revista: Comput Commun Ano de publicação: 2020 Tipo de documento: Artigo