## 4 Experimental Evaluation
### Performance Analysis  
**Implementation and settings. ** In order to provide a more realistic evaluation of the scheme's performance in practical applications, we conducted tests using the Enron email dataset, which reflects real-world scenarios. This dataset contains email information from approximately 150 users across different folders. We selected 30,109 files from the Enron email dataset to form the file collection. Then, we utilized the method described in refs. [39, 40] to extract 77,000 keywords from these files. We implemented VSSE in Python. Specifically, we used AES-256 and SHA-256 to implement the pseudo-random function and hash function, respectively. For the verification module, we used Solidity to build Ethereum smart contracts and utilize Ganache to evaluate the smart contracts. We have conducted a comparative analysis between VSSE and the existing schemes presented in refs. [26, 32] to demonstrate the advantages of our proposed scheme. In particular, the scheme in refs. [32] is a blockchain-based solution and is also the primary comparison object in our study. **Index Generation Performance. ** Constructing a secure index is the foundation for performing effective search operations. Figure 5 illustrates the correlation between the index generations time and the amount of keywords. Illustrated in Figure 5, r index generation time across all schemes exhibits a proportional relationship with the number of keywords. Specifically, to balance search efficiency, the scheme in ref. [26] constructs a dual-index structure, which requires more time. On the other hand, in scheme [32], the index is constructed with less time cost by using key-value pairs, but lacks consideration for forward security. Compared to the schemes in refs. [26, 32], VSSE outperforms [32] and takes more time than [26]. However, the index generated by VSSE fully considers the forward privacy and contributes to the search efficiency. Subsequent experiments will demonstrate this. **Search Performance. ** Figure 6 illustrates the correlation between search time and factors such as the quantity of keywords, the number of users, and the count of matching files. In Figure 5(a), we demonstrate the influence of the quantity of keywords and the count of matching files on the search time. In Figure 5(a), distinct colors of bars are employed to indicate varying quantities of matching files. It is evident that the search time exhibits a direct proportionality with the number of matching files and remains unaffected by the number of keywords. This observation is attributed to the construction of the inverted index. Moreover, Figure 5(b) illustrates the search efficiency of each scheme under different numbers of matching documents. VSSE outperforms the scheme in refs. [26, 32]. The scheme in ref. [32] is worse than [26] and VSSE, which is because this scheme requires additional interaction with the blockchain during search operations. The search performance of [26] matches VSSE, but it lacks a verification feature. VSSE does not require additional interaction with the blockchain during the search and achieves search result verification functionality. Figure 5(c) depicts the impact of varying numbers of users and matching documents on search time. It can be observed that, with a fixed number of matching files, search time increases with the quantity of users grows. Similarly, with a constant number of users, search time also increases with the scale of files. The increase in search time due to the growth in the number of users is foreseeable. Figure 5(d) further illustrates the influence of varying numbers of users on the search time for each scheme.