ID 原文 译文
3293 尽管能够利用可搜索加密（SE）进行加密数据的文件检索，但不可信云服务器可能篡改、删除外包数据或利用已有搜索陷门来获取新插入文件相关信息。 Although searchable encryption (SE) enables cloud servers to provide retrieval services of the encrypted data for data owners, cloud servers who are untrusted, may tamper and delete data, or learn information of thenewly added encrypted files with previous trapdoors.
3294 此外，现有单关键词搜索由于限制条件较少，导致搜索精度差，造成带宽和计算资源的浪费。 Besides, single-keyword search inevitably incurs many unrelatedresults, resulting in a waste of bandwidth and computing resources.
3295 为了解决以上问题，提出一种高效的、可验证的多关键词搜索加密方案。 To address the problems above, an efficient and veri-fiable multi-keyword search encryption scheme was proposed,
3296 所提方案不仅能够支持多关键词搜索，也能实现搜索模式的隐私性和文件的前向安全性。 which could not only supported multiple-keyword search,but also realized the privacy of search pattern and forward security of the outsourced files.
3297 此外，还能实现外包数据的完整性验证。 In the meanwhile, it also en-sured the integrity check of the outsourced data.
3298 通过严格的安全证明，所提方案在标准模型下被证明是安全的，能够抵抗不可信云服务器的离线关键词猜测攻击（KGA）。 Through rigorous security verification, the proposed scheme was provedto be secure under the standard mode, and could resist offline keyword guesswork attack (KGA) on untrusted cloud servers.
3299 最后，通过与最近 3 种方案进行效率和性能比较，实验结果表明所提方案在功能和效率方面具有较好的综合性能。 Finally, by comparing the efficiency and performance with the recent three searchable encryption schemes, the expe-rimental results show that the proposed scheme has the best comprehensive performance in terms of function and effi-ciency among the four schemes.
3300 针对常见证据冲突度量方法适应性差、准确性低的问题，提出了一种基于 Pignistic 概率转换和奇异值分解的证据冲突度量方法。 In view of the problem of poor adaptability and low accuracy of common evidence conflict measurement me-thod, an evidence conflict measurement method based on Pignistic probability transformation and singular value decom-position was proposed.
3301 首先通过 Pignistic 概率转换将证据焦元差异映射到信度差异上，构建证据复合信任函数矩阵。 First, Pignistic probability transformation was used to map the evidence focal element differenceto the belief difference, and the evidence composite belief function matrix was constructed.
3302 然后采用奇异值分解的方法提取矩阵特征，根据奇异值特性将矩阵特征空间划分为相似子空间和冲突子空间， Then, the matrix featureswere extracted by singular value decomposition, and the matrix feature space was divided into similar subspace and con-flict subspace according to singular value characteristics.