## 5 The Proposed Scheme
### The Designed Fast Searchable Decryption Algorithm
Specifically, with 512 users in the system, revoking an attribute group covering two users takes only 629 ms, while revoking a keyword group covering two users among 1000 keywords requires approximately 1 s. Our scheme is well-suited for security-critical applications such as smart healthcare, video surveillance, urban public safety, intelligent transportation, and campus security, where rapid and secure access to sensitive content is essential. Our framework demonstrates several strengths: (1) it supports both attribute- and keyword-level user revocation in a unified manner, which is rarely addressed in the existing literature; (2) it integrates a user management tree and an inverted index to enable efficient ciphertext and index updates, ensuring searchability even after revocation; and (3) it exhibits strong scalability and low computational overhead for both attribute and keyword revocation, as confirmed through comparative experiments with the methods from [PERSON] et al. [30] and [PERSON] et al. [31]. Specifically, when the dictionary contains 1000 keywords, the time required to revoke all users associated with a single keyword group remains at the millisecond level. Nevertheless, the proposed approach has some limitations. These include the need for further optimization on resource-constrained devices (e.g., mobile or edge platforms), reliance on the random oracle model in the current security analysis, and the assumption of a single trusted authority. Additionally, the scheme has not yet been tested in real-world deployments. Future work will address these limitations through the following: (1) improving efficiency for low-power environments; (2) extending the security model to standard cryptographic assumptions; (3) adopting a multi-authority ABE framework to reduce trust dependency; and (4) validating the system through real-world application testing. Figure 7: Revocation performance of users covered by the keyword groups. **Author Contributions:** Conceptualization, X.L. ; data curation, W.X. and X.W. ; formal analysis, J.Y. ; funding acquisition, X.L. ; methodology, L.J. and X.W. ; project administration, X.L. ; resources, W.X. ; software, L.J. ; supervision, X.L. ; validation, J.Y. ; writing--original draft, L.J. ; writing--review and editing, W.X., X.W. and X.L. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the Natural Science Foundation of Chongqing (Innovation and Development Joint Fund) under grant CSTB2023 NSCQ-LZX0149, in part by the Fundamental Research Funds for the Central Universities under grant 2023 CDJKYJH019, in part by the General Program of the Natural Science Foundation of Chongqing under Grant CSTB2024 NSCQ-MSX0479, in part by the Chongqing Postdoctoral Foundation Special Support Program under Grant 2023 CQB-SHTB3119, in part by the China Postdoctoral Science Foundation under Grant 2024 MD754244, and in part by the Postdoctoral Fellowship Program of CPSF under Grant Number GZC20233322. Not applicable. Data Availability Statement: Data are contained within the article. The authors declare no conflicts of interest. ## References

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