Secure Social Healthcare System Over Cloud

The increasing reliance on e-healthcare systems, especially within cloud environments, has prompted concerns regarding the susceptibility of patient health records to insider attacks. These attacks pose a substantial risk, potentially resulting in the manipulation of health records, unauthorized data usage, and significant financial losses stemming from data breaches. Furthermore, healthcare institutions face legal and reputational repercussions, underscoring the crucial need for effective detection methods. Here it aims to address the pressing requirement for securing e-healthcare systems by introducing an innovative framework for identifying insider attacks. The proposed approach integrates watermarking extraction and logging detection techniques to distinguish between lawful and unlawful intrusions into the system. An audit trail is implemented to monitor user activities, creating a comprehensive record for subsequent analysis. The evaluation of the framework demonstrates outstanding precision, recall, and accuracy, establishing it as a robust solution for mitigating insider threats in Cloud-based Healthcare systems. The implementation employs practical software tools such as HeidSQL and NetBeans IDE 7.4, showcasing its feasibility and accessibility for adoption in real-world scenarios. The suggested framework signifies a notable advancement in securing e-healthcare systems, addressing the potential ramifications of misinformation in patient health records and its subsequent impact on individual treatments and outcomes. By bolstering detection capabilities, this research contributes to the overall resilience of healthcare systems operating in cloud environments.

Authors

Publication Details

Published
2024-08-08
DOI
https://doi.org/10.1109/iccpct61902.2024.10673258
Citations
1
Primary Topic
Cloud Data Security Solutions
Type
article
Field-Weighted Citation Impact
0.78
Controls
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article

Secure Social Healthcare System Over Cloud

S. Mahalakshmi, K. Valarmathi, P. A. Mathina, M Muneeshwar et al.
1 citations
Cloud Data Security Solutions
0.78
article

Secure Social Healthcare System Over Cloud

S. Mahalakshmi, K. Valarmathi, P. A. Mathina, M Muneeshwar, R Vinoth
article en
1 citations

Abstract

The increasing reliance on e-healthcare systems, especially within cloud environments, has prompted concerns regarding the susceptibility of patient health records to insider attacks. These attacks pose a substantial risk, potentially resulting in the manipulation of health records, unauthorized data usage, and significant financial losses stemming from data breaches. Furthermore, healthcare institutions face legal and reputational repercussions, underscoring the crucial need for effective detection methods. Here it aims to address the pressing requirement for securing e-healthcare systems by introducing an innovative framework for identifying insider attacks. The proposed approach integrates watermarking extraction and logging detection techniques to distinguish between lawful and unlawful intrusions into the system. An audit trail is implemented to monitor user activities, creating a comprehensive record for subsequent analysis. The evaluation of the framework demonstrates outstanding precision, recall, and accuracy, establishing it as a robust solution for mitigating insider threats in Cloud-based Healthcare systems. The implementation employs practical software tools such as HeidSQL and NetBeans IDE 7.4, showcasing its feasibility and accessibility for adoption in real-world scenarios. The suggested framework signifies a notable advancement in securing e-healthcare systems, addressing the potential ramifications of misinformation in patient health records and its subsequent impact on individual treatments and outcomes. By bolstering detection capabilities, this research contributes to the overall resilience of healthcare systems operating in cloud environments.

Openalex Percentile: Top 21%
Cloud Data Security Solutions
0.78
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Secure Social Healthcare System Over Cloud — S. Mahalakshmi, K. Valarmathi, et al. · (2024) | TGRS Research Map | TGRS