Algebraic secret sharing using privacy homomorphisms for IoT-based healthcare systems

Area of Science:

• Computer Science
• Information Security
• Healthcare Technology

Background:

• The healthcare industry increasingly uses Internet of Things (IoT) for data acquisition.
• Privacy and security are critical concerns for sensitive patient data in IoT healthcare applications.
• Existing solutions may not adequately address secure data sharing among multiple participants.

Purpose of the Study:

• To develop and analyze secret sharing mechanisms for secure data sharing in IoT-based healthcare.
• To enable authorized subsets of participants to reconstruct shared health data.
• To ensure data confidentiality even when facing collusion among participants.

Main Methods:

• Investigated secret sharing in the encrypted domain for healthcare data.
• Proposed a novel scheme where encrypted data from sensor nodes is shared among cloud servers.
• Developed practical secret sharing schemes tailored for IoT healthcare systems.

Main Results:

• Demonstrated a method for secure data sharing without revealing data to unauthorized parties or the dealer.
• Showcased that colluding participants have no advantage in guessing private data.
• The proposed retrieval procedure avoids decryption, making it suitable for time-sensitive scenarios.

Conclusions:

• The developed secret sharing schemes effectively address privacy and security challenges in IoT healthcare.
• The novel approach allows for secure data aggregation and controlled access in medical applications.
• The schemes offer a practical solution for protecting patient health information in an increasingly connected environment.