With the popularization of cloud computing and web infrastructure, the demand for cloud data storage and processing has surged. However, data privacy issues (such as internal theft and data leakage caused by external attacks, accounting for 25% of cloud environment threats) have a significant impact on personal, enterprise, and national security. Efficiently compiling declarative privacy policies, such as access control and data usage restrictions, into executable policies is a key challenge. This study combines searchable encryption (SSE/PEKS), secret sharing, index optimization, and computation protocol design to construct a policy compilation framework: proposes a false positive free fuzzy keyword search scheme, and combines Shamir secret sharing to achieve multi-user, multi-keyword, dynamic update, and verification functions, which is more efficient than symmetric encryption schemes and ensures 100% accuracy; Design a low-level index structure, combined with point/range intersection determination technology, to construct a dynamic multi-dimensional range search system that improves search and update efficiency while ensuring single dimensional privacy; Improve the privacy data calculation function, design protocols for mean, variance, correlation, etc., and support complex analysis needs in policy implementation; In addition, extending the discrete logarithm problem to semigroup and proposing a solution algorithm to enhance the security of cryptographic primitives. Future research will focus on the Top-k problem of fuzzy search (returning the most relevant top k results), dynamically updated forward/backward privacy protection (preventing historical policies from leaking new data or subsequent queries from accessing deleted data), and exploring the discrete logarithm problem on the half loop to improve the efficiency and security of privacy policy enforcement.

Chenwei Chang. Compiling Declarative Privacy Policies into Runtime Enforcement for Cloud and Web Infrastructure. Machine Learning Theory and Practice (2025), Vol. 5, Issue 1: 76-86. https://doi.org/10.38007/ML.2025.050108.

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