Fuzzy Keyword Search over Encrypted Data in Cloud Computing
Fuzzy Keyword Search over Encrypted Data in Cloud Computing
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DOI：
10.1109/infcom.2010.5462196
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发表时间：
2010-03
期刊：
影响因子：
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通讯作者：
Jin Li;Qian Wang;Cong Wang;N. Cao;Kui Ren;Wenjing Lou
中科院分区：
文献类型：
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作者：
Jin Li;Qian Wang;Cong Wang;N. Cao;Kui Ren;Wenjing Lou
随着云计算的普及，越来越多的敏感信息被集中到云中。为了保护数据隐私，敏感数据通常必须在外包之前进行加密，这使得有效的数据利用成为一项非常具有挑战性的任务。虽然传统的可搜索加密方案允许用户通过关键字安全地搜索加密数据并选择性地检索感兴趣的文件，但这些技术仅支持精确的关键字搜索。也就是说，不容忍轻微的拼写错误和格式不一致，另一方面，这是典型的用户搜索行为，并且经常发生。这个显著的缺点使得现有技术不适合云计算，因为它极大地影响了系统的可用性，使得用户搜索体验非常令人沮丧，并且系统效率非常低。在本文中，我们第一次正式和解决的问题，有效的模糊关键字搜索加密云数据，同时保持关键字隐私。模糊关键字搜索通过在用户的搜索输入与预定义的关键字完全匹配时返回匹配文件或在完全匹配失败时基于关键字相似性语义返回最接近的可能匹配文件来极大地增强系统可用性。在我们的解决方案中，我们利用编辑距离来量化关键字相似性，并开发了一种先进的技术，构建模糊关键字集，这大大降低了存储和表示开销。通过严格的安全性分析，我们表明，我们提出的解决方案是安全和隐私保护，同时正确实现模糊关键字搜索的目标。
As Cloud Computing becomes prevalent, more and more sensitive information are being centralized into the cloud. For the protection of data privacy, sensitive data usually have to be encrypted before outsourcing, which makes effective data utilization a very challenging task. Although traditional searchable encryption schemes allow a user to securely search over encrypted data through keywords and selectively retrieve files of interest, these techniques support only exact keyword search. That is, there is no tolerance of minor typos and format inconsistencies which, on the other hand, are typical user searching behavior and happen very frequently. This significant drawback makes existing techniques unsuitable in Cloud Computing as it greatly affects system usability, rendering user searching experiences very frustrating and system efficacy very low. In this paper, for the first time we formalize and solve the problem of effective fuzzy keyword search over encrypted cloud data while maintaining keyword privacy. Fuzzy keyword search greatly enhances system usability by returning the matching files when users' searching inputs exactly match the predefined keywords or the closest possible matching files based on keyword similarity semantics, when exact match fails. In our solution, we exploit edit distance to quantify keywords similarity and develop an advanced technique on constructing fuzzy keyword sets, which greatly reduces the storage and representation overheads. Through rigorous security analysis, we show that our proposed solution is secure and privacy-preserving, while correctly realizing the goal of fuzzy keyword search.