# Privacy-Preserving Data Sharing in Telehealth Services
## 5 Conclusions and Future Work  
The proposed security algorithm for telemedicine systems offers a comprehensive protection suite, significantly improving existing methodologies for securing sensitive healthcare data. By integrating advanced cryptographic techniques and leveraging a multilayered security approach, the algorithm efficiently addresses various cybersecurity threats, from impersonation and insider attacks to data breaches and DoS attacks. This model ensures the confidentiality and integrity of data and upholds user anonymity and robust access control, which are critical for maintaining trust in digital healthcare environments. The algorithm's efficacy is further underscored by the comparative analysis presented in this study, which shows its superior performance in handling diverse security challenges compared to that of other contemporary systems. However, the algorithm has certain limitations. One major limitation is the computational overhead of advanced cryptographic techniques and multi-layered security measures, which can increase processing times and consume more system resources. This may limit the algorithm's efficiency, particularly in environments with limited computational power, such as mobile devices or IoT systems commonly used in telemedicine. Another limitation is the potential scalability challenges as the algorithm relies heavily on secure multi-party computation (SMPC) and blockchain technologies, which can become less efficient as the number of participants or the volume of data increases. This could result in higher latency and bandwidth consumption, especially in large-scale healthcare networks with numerous simultaneous users. Additionally, while the algorithm ensures a high level of security against existing threats, it may be vulnerable to emerging threats, such as quantum computing attacks, unless further developments in quantum-resistant cryptographic methods are incorporated. Future research could further enhance the model's scalability and adaptability to accommodate the rapidly evolving landscape of telemedicine and e-health services. One potential area for development is integrating AI-driven threat detection systems, which could provide dynamic and proactive security measures. Additionally, incorporating quantum-resistant cryptographic techniques could future-proof the model against emerging computational threats. As telemedicine expands its reach, the ongoing development and refinement of security measures will be crucial in ensuring the safety and privacy of patient data, fostering continued trust and reliance on these essential digital health services. Conceptualization, A.O. and E.A. ; data collection, A.O. ; analysis and interpretation of results, A.O., E.A. and W.S. ; draft manuscript preparation, E.A. and W.S. All authors have read and agreed to the published version of the manuscript. The authors received no specific funding for this study. The authors received no specific funding for this study. The author's feedback  
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