{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T15:19:56Z","timestamp":1777475996376,"version":"3.51.4"},"reference-count":30,"publisher":"Emerald","issue":"1","license":[{"start":{"date-parts":[[2021,9,17]],"date-time":"2021-09-17T00:00:00Z","timestamp":1631836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJICC"],"published-print":{"date-parts":[[2022,2,2]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>Artificial Intelligence (AI) has surpassed expectations in opening up different possibilities for machines from different walks of life. Cloud service providers are pushing. Edge computing reduces latency, improving availability and saving bandwidth.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>The exponential growth in tensor processing unit (TPU) and graphics processing unit (GPU) combined with different types of sensors has enabled the pairing of medical technology with deep learning in providing the best patient care. A significant role of pushing and pulling data from the cloud, big data comes into play as velocity, veracity and volume of data with IoT assisting doctors in predicting the abnormalities and providing customized treatment based on the patient electronic health record (EHR).<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The primary focus of edge computing is decentralizing and bringing intelligent IoT devices to provide real-time computing at the point of presence (PoP). The impact of the PoP in healthcare gains importance as wearable devices and mobile apps are entrusted with real-time monitoring and diagnosis of patients. The impact edge computing of the PoP in healthcare gains significance as wearable devices and mobile apps are entrusted with real-time monitoring and diagnosis of patients.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>The utility value of sensors data improves through the Laplacian mechanism of preserved PII response to each query from the ODL. The scalability is at 50% with respect to the sensitivity and preservation of the PII values in the local ODL.<\/jats:p><\/jats:sec>","DOI":"10.1108\/ijicc-05-2021-0099","type":"journal-article","created":{"date-parts":[[2021,9,15]],"date-time":"2021-09-15T10:33:54Z","timestamp":1631702034000},"page":"92-109","source":"Crossref","is-referenced-by-count":39,"title":["Emphasizing privacy and security of edge intelligence with machine learning for healthcare"],"prefix":"10.1108","volume":"15","author":[{"given":"Sukumar","family":"Rajendran","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8572-1197","authenticated-orcid":false,"given":"Sandeep Kumar","family":"Mathivanan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3335-6911","authenticated-orcid":false,"given":"Prabhu","family":"Jayagopal","sequence":"additional","affiliation":[]},{"given":"Kumar","family":"Purushothaman Janaki","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7632-371X","authenticated-orcid":false,"given":"Benjula Anbu Malar","family":"Manickam Bernard","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8552-3462","authenticated-orcid":false,"given":"Suganya","family":"Pandy","sequence":"additional","affiliation":[]},{"given":"Manivannan","family":"Sorakaya Somanathan","sequence":"additional","affiliation":[]}],"member":"140","published-online":{"date-parts":[[2021,9,17]]},"reference":[{"issue":"3","key":"key2022020205381464900_ref001","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1109\/MNET.2019.1800083","article-title":"Edge computing for smart health: context-aware approaches, opportunities, and challenges","volume":"33","year":"2019","journal-title":"IEEE Network"},{"key":"key2022020205381464900_ref002","doi-asserted-by":"crossref","first-page":"46474","DOI":"10.1109\/ACCESS.2019.2905597","article-title":"Automatic voice pathology monitoring using parallel deep models for smart healthcare","volume":"7","year":"2019","journal-title":"IEEE Access"},{"key":"key2022020205381464900_ref003","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.future.2019.02.005","article-title":"An energy efficient IoT data compression approach for edge machine learning","volume":"96","year":"2019","journal-title":"Future Generation Computer Systems"},{"key":"key2022020205381464900_ref004","first-page":"63","article-title":"Empowering healthcare IoT systems with hierarchical edge-based deep learning","year":"2018"},{"issue":"1","key":"key2022020205381464900_ref005","first-page":"1","article-title":"Decentralized computation of floading for multi-user mobile edge computing: a deep reinforcement learning approach","volume":"2020","year":"2020","journal-title":"EURASIP Journal on Wireless Communications and Networking"},{"key":"key2022020205381464900_ref006","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/j.future.2018.03.054","article-title":"Edge cognitive computing based smart healthcare system","volume":"86","year":"2018","journal-title":"Future Generation Computer Systems"},{"key":"key2022020205381464900_ref007","article-title":"Differential privacy-enabled federated learning for sensitive health data","year":"2019"},{"key":"key2022020205381464900_ref008","doi-asserted-by":"crossref","unstructured":"Dastjerdi, A.V., Gupta, H., Calheiros, R.N., Ghosh, S.K. and Buyya, R. 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