Privacy-Aware Relevant Data Access with Semantically Enriched Search Queries for Untrusted Cloud Storage Services

Area of Science:

• Computer Science
• Information Security
• Cloud Computing

Background:

• Public cloud services offer storage but pose privacy risks for sensitive data.
• Existing trapdoor-based search methods lack semantic understanding and flexible criteria.
• Securely accessing outsourced data with privacy is a significant challenge.

Purpose of the Study:

• To propose an index-based privacy-aware search methodology for semantic data retrieval.
• To enable flexible, conjunctive search queries without reliance on predefined trapdoors.
• To ensure oblivious query execution and protect data privacy against untrusted cloud providers.

Main Methods:

• Developed an index-based approach for privacy-aware data searching.
• Implemented oblivious query execution for secure search operations.
• Designed a system allowing conjunctive search queries with arbitrary criteria.

Main Results:

• The proposed methodology ensures semantic retrieval of data from untrusted cloud domains.
• Security analysis confirms no data privacy loss under conspired attacks.
• Computational analysis shows moderate resource requirements for privacy-aware search.

Conclusions:

• The index-based privacy-aware search method effectively addresses limitations of trapdoor-based approaches.
• It provides enhanced data access control and semantic search capabilities in cloud environments.
• The method offers a practical solution for secure and flexible data searching in untrusted domains.