Next-generation security for big data analytics in healthcare IoT using hybrid cryptographic techniques.

Area of Science:

• Cybersecurity
• Health Informatics
• Big Data Analytics

Background:

• Internet of Healthcare Things (IoHT) environments generate vast amounts of sensitive Big Data.
• Existing data security measures, like Hadoop Distributed File System (HDFS) encryption, have limitations for large-scale medical datasets.
• Traditional encryption algorithms struggle with the performance and security demands of IoHT Big Data.

Purpose of the Study:

• To propose a novel hybrid encryption framework to enhance security for Big Data in IoHT environments.
• To address the performance and security limitations of existing encryption techniques for large-scale medical data.
• To improve the computational handling and security of heterogeneous medical device data within the IoHT infrastructure.

Main Methods:

• Developed a hybrid encryption framework combining Attribute-Based Encryption (ABE) with the Blowfish cipher.
• Secured data generated by heterogeneous medical devices across the IoHT infrastructure.
• Benchmarked the proposed framework against established hybrid schemes (CP-ABE + HE, HE + BF, CP-ABE + AES).

Main Results:

• The proposed hybrid scheme demonstrated superior performance compared to existing approaches.
• Achieved a peak system efficiency of 98.5%.
• Recorded encryption and decryption times of 6.8 minutes and 5.7 minutes, respectively.

Conclusions:

• The hybrid encryption framework effectively enhances security for Big Data in IoHT environments.
• The proposed approach offers improved computational performance for handling large-scale medical datasets.
• This method provides a robust solution for securing sensitive health information in connected healthcare systems.