Secure User Privacy in Population Physique Clustering and Prediction Based on Sport Questionnaires
| Autor: | Hao Wang, Chunlan Tian, Chongmin Zhang, Wanli Huang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Information privacy
education.field_of_study General Computer Science sport questionnaires Process (engineering) privacy protection Population Hash function time efficiency General Engineering Data science hashing Variety (cybernetics) Information sensitivity Key (cryptography) Population physique General Materials Science lcsh:Electrical engineering. Electronics. Nuclear engineering education Cluster analysis lcsh:TK1-9971 clustering |
| Zdroj: | IEEE Access, Vol 8, Pp 171560-171567 (2020) IEEE Access |
| ISSN: | 2169-3536 |
| Popis: | Population physique is one of the key aspects for measuring and evaluating the healthy degree or living level of the population of a nation. Many population physique evaluation models or tools have been developed in the past decades, among which questionnaire is an essential and promising way to help achieve the above population physique measurement goal. Typically, through designing, distributing and collecting a variety of sport questionnaires associated with people's living conditions or sport preferences, we can quantify, observe and cluster the healthy degree of the whole nation's population objectively and scientifically. However, the above sport questionnaire-based population physique analysis methods are often time-consuming as a considerable amount of sport questionnaires needs to be compared. Moreover, the sport questionnaires filled out by people often contains some sensitive information that is not supposed to be disclosed to other people, which also call for appropriate privacy protection measures. Inspired by the above two challenges, we introduce hash techniques into population physique evaluation process and afterwards, we propose a hash-based population physique clustering and prediction method that is efficient in time cost and effective in terms of privacy protection. At last, we designed experiments based on a dataset to prove the effectiveness of our proposal in this research work. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ |
| Databáze: | OpenAIRE |
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