Attribute-based multi-user collaborative searchable encryption in COVID-19.

Area of Science:

• Cybersecurity
• Cryptography
• Data Privacy

Background:

• The COVID-19 pandemic necessitated extensive collection of sensitive patient data.
• Searchable encryption is crucial for secure data retrieval in cloud environments, often using attribute-based access control.
• Existing attribute-based searchable encryption (ABSE) schemes lack support for collaborative search, limiting user access flexibility.

Purpose of the Study:

• To propose a novel attribute-based collaborative searchable encryption scheme for multi-user environments (ABCSE-MU).
• To address the limitations of existing ABSE schemes by enabling collaborative data searching.
• To enhance data privacy and flexibility in accessing encrypted data within a multi-user cloud setting.

Main Methods:

• Developed a new attribute-based collaborative searchable encryption scheme (ABCSE-MU) utilizing an access tree structure.
• Introduced translation nodes within the access policy to facilitate secure collaborative search.
• Employed random blinding techniques to ensure secret key confidentiality and security.
• Proved the scheme's security under the Decisional Bilinear Diffie-Hellman (DBDH) assumption.

Main Results:

• The proposed ABCSE-MU scheme successfully implements collaborative search capabilities through translation nodes.
• Achieved enhanced flexibility in search permissions while maintaining robust data security.
• Demonstrated the scheme's security against chosen-keyword attacks and collusion.
• Validated the scheme's security under the DBDH assumption.

Conclusions:

• The ABCSE-MU scheme offers a more flexible and secure approach to searchable encryption in multi-user scenarios.
• It effectively overcomes the limitations of single-user ABSE schemes by enabling secure collaboration.
• The scheme provides strong data confidentiality and resistance to various security threats, crucial for sensitive data management.