PP-DDP: a privacy-preserving outsourcing framework for solving the double digest problem.

Area of Science:

• Bioinformatics
• Computational Biology
• Cryptography

Background:

• The double digest problem (DDP) is a fundamental bioinformatics challenge involving genetic fragment reordering.
• Existing DDP algorithms are time-consuming and do not address data privacy concerns, hindering cloud-based solutions.
• Sensitive DDP data, often from extensive experiments, poses privacy risks when outsourced.

Purpose of the Study:

• To design a secure outsourcing computation framework for the double digest problem (DDP).
• To enable cloud servers to solve DDP instances while preserving data privacy.
• To integrate a privacy-preserving mechanism with an efficient DDP solving algorithm.

Main Methods:

• Developed a privacy-preserving outsourcing framework utilizing a cloud server.
• Tailored an order-preserving homomorphic index (OPHI) scheme for ciphertext computation.
• Integrated a quantum-inspired genetic algorithm (QIGA) within the outsourcing framework.

Main Results:

• The proposed PP-DDP framework enables secure DDP solving over ciphertexts.
• Optimal solutions (mapping sequences) are transferred publicly without revealing original data.
• PP-DDP demonstrates high success rates, reasonable computational/communication costs, and outperforms existing methods like DDmap, SK05, and GM12.

Conclusions:

• The PP-DDP framework provides a secure and effective solution for the double digest problem.
• This approach addresses critical privacy concerns in bioinformatics data outsourcing.
• The integration of OPHI and QIGA ensures efficient and private computation.