Leveraging Distributed Data Over Big Data Analytics Platform for Healthcare Services

Mande, Ramesh; JayaLakshmi, G.; Yelavarti, Kalyan Chakravarti

doi:10.1109/icoei.2018.8553827

Cited by 9 publications

(1 citation statement)

References 5 publications

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“…Examples include a study undertaken by Greenbone Networks from the middle of July to the beginning of September that examined thousands of online medical care systems from all over the world [4,5]. Thus, over 24 million patient data records from various countries may now be seen and downloaded with reasonable ease using the Internet [6][7][8]. Patient data records that have been leaked contain specific personal and medical information, such as your name, date of birth, examination date, survey items, attending physician's name, and image information of test results [9].…”

Section: Introductionmentioning

confidence: 99%

Federated Learning Approach to Protect Healthcare Data over Big Data Scenario

Juneja¹,

Mohafez

El-Bayoumy³

et al. 2022

Sustainability

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The benefits and drawbacks of various technologies, as well as the scope of their application, are thoroughly discussed. The use of anonymity technology and differential privacy in data collection can aid in the prevention of attacks based on background knowledge gleaned from data integration and fusion. The majority of medical big data are stored on a cloud computing platform during the storage stage. To ensure the confidentiality and integrity of the information stored, encryption and auditing procedures are frequently used. Access control mechanisms are mostly used during the data sharing stage to regulate the objects that have access to the data. The privacy protection of medical and health big data is carried out under the supervision of machine learning during the data analysis stage. Finally, acceptable ideas are put forward from the management level as a result of the general privacy protection concerns that exist throughout the life cycle of medical big data throughout the industry.

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