Mapping Privacy Properties of Searchable Encryption to Leakage Profiles
Manuela Horduna · Procedia Computer Science · 2025
Searchable Encryption enables efficient queries over outsourced data without disclosing plaintext to untrusted entities. However, balancing robust privacy, dynamic operations, and performance remains challenging. Privacy threats underscore the need for systems that uphold user rights. By adopting a suitable privacy model, searchable encryption can address these threats and advance toward a more secure, privacy-conscious future and respond to cybersecurity threats more quickly and accurately. This paper focuses on enhancing privacy models in searchable encryption systems by refining privacy properties and introduces a comprehensive perspective on privacy, highlighting the differences between security and privacy. Also it formalizes various types of privacy and examines searchable encryption schemes, providing an overview of how privacy is maintained across different systems. Furthermore, we introduce a systematic mapping between privacy classes and corresponding leakage profiles, clarifying how a single searchable encryption system can exhibit different “modes” of privacy depending on the adversarial context. By providing this comprehensive framework, we aim to guide the design and analysis of more robust and privacy-centric searchable encryption solutions.