Privacy-Preserving Opt-Out from Homomorphically Encrypted Clinical Trials.

Area of Science:

• Computer Science
• Cryptography
• Bioinformatics

Background:

• Data protection regulations like GDPR grant individuals the 'right to be forgotten,' requiring personal data deletion.
• Homomorphic encryption allows computations on encrypted data, preserving privacy during analysis.

Purpose of the Study:

• To implement the 'right to be forgotten' for clinical data using homomorphic encryption.
• To develop privacy-preserving methods for data opt-out and verifiable deletion in international research collaborations.

Main Methods:

• Structuring clinical data as collections of encrypted vectors.
• Developing and implementing algorithms for privacy-preserving opt-out and verifiable data deletion.
• Testing algorithms in a software prototype and analyzing computational performance.

Main Results:

• A functional software prototype demonstrating privacy-preserving data deletion.
• Validation of algorithms for opt-out and verifiable deletion in encrypted vector collections.
• Performance analysis indicating computational efficiency within practical constraints.

Conclusions:

• The proposed framework enables patient withdrawal from clinical trials at any stage.
• Balancing data privacy with computational efficiency is achievable using homomorphic encryption for clinical datasets.
• This approach supports secure and compliant data management in international clinical research.