论文标题
在外包数据库中安全有效的查询处理
Secure and Efficient Query Processing in Outsourced Databases
论文作者
论文摘要
在外包数据库系统中使用了各种加密技术，以确保数据隐私，同时允许有效查询。这项工作提出了一个新的安全有效外包数据库系统的定义和组件，该系统回答各种类型的查询，并在不同的安全模型中保证了不同的隐私性。这项工作始于对五个订购订单的加密方案的调查，这些计划可以直接在许多数据库指数和五个范围查询协议中使用，并具有各种安全 /效率折衷。该调查在快照对手环境中对最新的范围查询解决方案进行了整理，并提供了一些关于构造效率的非明显观察。在$ \ mathcal {e} \ text {psolute} $中，安全范围查询引擎，在具有更强对手的设置中实现了安全性，她可以在其中连续观察服务器上的所有内容，甚至泄漏的结果尺寸也可以启用重建攻击。 $ \ Mathcal {E} \ Text {psolute} $提出了对系统的定义，构造，分析和实验评估，该系统可证明可以隐藏访问模式和通信量，同时保持效率。这项工作以$ k \ text {-a} n \ text {o} n $结束 - 快照对手模型中的安全相似性搜索引擎。这项工作构成了一种结构，其中$ k \ text {nn} $ Queries的安全性与OPE / ore解决方案相似 - 使用近似距离的比较来加密输入，以保留加密方案，以使超级空间中的输入点，但要触及，但要效果，但仍会产生准确的结果。我们使用TREC数据集和查询进行搜索，并跟踪等级质量指标，例如MRR和NDCG。对于攻击，我们构建了一个LSTM模型，该模型训练句子及其嵌入之间的相关性，然后从嵌入中预测单词。
Various cryptographic techniques are used in outsourced database systems to ensure data privacy while allowing for efficient querying. This work proposes a definition and components of a new secure and efficient outsourced database system, which answers various types of queries, with different privacy guarantees in different security models. This work starts with the survey of five order-revealing encryption schemes that can be used directly in many database indices and five range query protocols with various security / efficiency tradeoffs. The survey systematizes the state-of-the-art range query solutions in a snapshot adversary setting and offers some non-obvious observations regarding the efficiency of the constructions. In $\mathcal{E}\text{psolute}$, a secure range query engine, security is achieved in a setting with a much stronger adversary where she can continuously observe everything on the server, and leaking even the result size can enable a reconstruction attack. $\mathcal{E}\text{psolute}$ proposes a definition, construction, analysis, and experimental evaluation of a system that provably hides both access pattern and communication volume while remaining efficient. The work concludes with $k\text{-a}n\text{o}n$ -- a secure similarity search engine in a snapshot adversary model. The work presents a construction in which the security of $k\text{NN}$ queries is achieved similarly to OPE / ORE solutions -- encrypting the input with an approximate Distance Comparison Preserving Encryption scheme so that the inputs, the points in a hyperspace, are perturbed, but the query algorithm still produces accurate results. We use TREC datasets and queries for the search, and track the rank quality metrics such as MRR and nDCG. For the attacks, we build an LSTM model that trains on the correlation between a sentence and its embedding and then predicts words from the embedding.
