A Secure Recommendation System for Providing Context-Aware Physical Activity Classification for Users

Advances in Wireless Body Area Networks, where embedded accelerometers, gyroscopes, and other sensors empower users to track real-time health data continuously, have made it easier for users to follow a healthier lifestyle. Various other apps have been intended to choose suitable physical exercise,...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Sudhakar Sengan, Subramaniyaswamy V, Rutvij H. Jhaveri, Vijayakumar Varadarajan, Roy Setiawan, Logesh Ravi
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Technology (General) T1-995 Science (General) Q1-390
Acceso en línea:	https://doaj.org/article/2b91b2018189448aa9d3ce6f891ce6ca
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:2b91b2018189448aa9d3ce6f891ce6ca
record_format	dspace
spelling	oai:doaj.org-article:2b91b2018189448aa9d3ce6f891ce6ca2021-11-22T01:10:15ZA Secure Recommendation System for Providing Context-Aware Physical Activity Classification for Users1939-012210.1155/2021/4136909https://doaj.org/article/2b91b2018189448aa9d3ce6f891ce6ca2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/4136909https://doaj.org/toc/1939-0122Advances in Wireless Body Area Networks, where embedded accelerometers, gyroscopes, and other sensors empower users to track real-time health data continuously, have made it easier for users to follow a healthier lifestyle. Various other apps have been intended to choose suitable physical exercise, depending on the current healthcare environment. A Mobile Application (Mobile App) based recommendation system is a technology that allows users to select an apt activity that might suit their preferences. However, most of the current applications require constant input from end-users and struggle to include those who have hectic schedules or are not dedicated and self-motivated. This research introduces a methodology that uses a “Selective Cluster Cube” recommender system to intelligently monitor and classify user behavior by collecting accelerometer data and synchronizing with its calendar. We suggest customized daily workouts based on historical user and related user habits, interests, physical status, and accessibility. Simultaneously, the exposure of customer requirements to the server is also a significant concern. Developing privacy-preserving protocols with basic cryptographic techniques (e.g., protected multi-party computing or HE) is a standard solution to address privacy issues, but in combination with state-of-the-art advising frameworks, it frequently provides far-reaching solutions. This paper proposes a novel framework, a Privacy Protected Recommendation System (PRIPRO), that employs HE for securing private user data. The PRIPRO model is compared for accuracy and robustness using standard evaluation parameters against three datasets.Sudhakar SenganSubramaniyaswamy VRutvij H. JhaveriVijayakumar VaradarajanRoy SetiawanLogesh RaviHindawi-WileyarticleTechnology (General)T1-995Science (General)Q1-390ENSecurity and Communication Networks, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology (General) T1-995 Science (General) Q1-390
spellingShingle	Technology (General) T1-995 Science (General) Q1-390 Sudhakar Sengan Subramaniyaswamy V Rutvij H. Jhaveri Vijayakumar Varadarajan Roy Setiawan Logesh Ravi A Secure Recommendation System for Providing Context-Aware Physical Activity Classification for Users
description	Advances in Wireless Body Area Networks, where embedded accelerometers, gyroscopes, and other sensors empower users to track real-time health data continuously, have made it easier for users to follow a healthier lifestyle. Various other apps have been intended to choose suitable physical exercise, depending on the current healthcare environment. A Mobile Application (Mobile App) based recommendation system is a technology that allows users to select an apt activity that might suit their preferences. However, most of the current applications require constant input from end-users and struggle to include those who have hectic schedules or are not dedicated and self-motivated. This research introduces a methodology that uses a “Selective Cluster Cube” recommender system to intelligently monitor and classify user behavior by collecting accelerometer data and synchronizing with its calendar. We suggest customized daily workouts based on historical user and related user habits, interests, physical status, and accessibility. Simultaneously, the exposure of customer requirements to the server is also a significant concern. Developing privacy-preserving protocols with basic cryptographic techniques (e.g., protected multi-party computing or HE) is a standard solution to address privacy issues, but in combination with state-of-the-art advising frameworks, it frequently provides far-reaching solutions. This paper proposes a novel framework, a Privacy Protected Recommendation System (PRIPRO), that employs HE for securing private user data. The PRIPRO model is compared for accuracy and robustness using standard evaluation parameters against three datasets.
format	article
author	Sudhakar Sengan Subramaniyaswamy V Rutvij H. Jhaveri Vijayakumar Varadarajan Roy Setiawan Logesh Ravi
author_facet	Sudhakar Sengan Subramaniyaswamy V Rutvij H. Jhaveri Vijayakumar Varadarajan Roy Setiawan Logesh Ravi
author_sort	Sudhakar Sengan
title	A Secure Recommendation System for Providing Context-Aware Physical Activity Classification for Users
title_short	A Secure Recommendation System for Providing Context-Aware Physical Activity Classification for Users
title_full	A Secure Recommendation System for Providing Context-Aware Physical Activity Classification for Users
title_fullStr	A Secure Recommendation System for Providing Context-Aware Physical Activity Classification for Users
title_full_unstemmed	A Secure Recommendation System for Providing Context-Aware Physical Activity Classification for Users
title_sort	secure recommendation system for providing context-aware physical activity classification for users
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/2b91b2018189448aa9d3ce6f891ce6ca
work_keys_str_mv	AT sudhakarsengan asecurerecommendationsystemforprovidingcontextawarephysicalactivityclassificationforusers AT subramaniyaswamyv asecurerecommendationsystemforprovidingcontextawarephysicalactivityclassificationforusers AT rutvijhjhaveri asecurerecommendationsystemforprovidingcontextawarephysicalactivityclassificationforusers AT vijayakumarvaradarajan asecurerecommendationsystemforprovidingcontextawarephysicalactivityclassificationforusers AT roysetiawan asecurerecommendationsystemforprovidingcontextawarephysicalactivityclassificationforusers AT logeshravi asecurerecommendationsystemforprovidingcontextawarephysicalactivityclassificationforusers AT sudhakarsengan securerecommendationsystemforprovidingcontextawarephysicalactivityclassificationforusers AT subramaniyaswamyv securerecommendationsystemforprovidingcontextawarephysicalactivityclassificationforusers AT rutvijhjhaveri securerecommendationsystemforprovidingcontextawarephysicalactivityclassificationforusers AT vijayakumarvaradarajan securerecommendationsystemforprovidingcontextawarephysicalactivityclassificationforusers AT roysetiawan securerecommendationsystemforprovidingcontextawarephysicalactivityclassificationforusers AT logeshravi securerecommendationsystemforprovidingcontextawarephysicalactivityclassificationforusers
_version_	1718418330191134720

A Secure Recommendation System for Providing Context-Aware Physical Activity Classification for Users

Ejemplares similares