Efficient Forward and Backward Private Conjunctive Searchable Encryption With Comprehensive Verification

IF 7.5 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Yue Ge;Ying Gao;Lin Qi;Jiankai Qiu
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引用次数: 0

Abstract

Dynamic searchable symmetric encryption (DSSE) enables the retrieval and update of massive encrypted data and is thus widely applied in cloud storage. Malicious cloud servers may tamper with outsourced data or return incorrect search results, making verification indispensable. However, current verifiable conjunctive DSSE with forward and backward privacy cannot simultaneously achieve accurate search and empty result verification. Given the above problems, we propose a novel forward and backward private conjunctive DSSE with comprehensive verification called VCFB. VCFB introduces the notion of random blinding factors and secure dynamic cross-tags to achieve accurate conjunctive search with sublinear overhead. We design the search state and construct chain structures to ensure forward security. The new verification algorithm based on bilinear-map dynamic accumulators can guarantee the verifiability of search results even if an empty result is returned. We use a sample check method to verify the new dynamic cross-tags, reducing computation costs. We precisely define the leakage function of VCFB and give detailed security proof, demonstrating that VCFB is forward and backward private under the malicious server model. Experimental results show that VCFB ensures efficient and accurate conjunctive search, outperforming the similar verifiable conjunctive DSSE scheme regarding indexing storage and update performance.
具有全面验证的高效前向和后向私有合取可搜索加密
动态可搜索对称加密(DSSE)实现了海量加密数据的检索和更新,在云存储中得到了广泛的应用。恶意的云服务器可能会篡改外包数据或返回错误的搜索结果,这使得验证变得必不可少。然而,目前具有前向和后向隐私的可验证联合DSSE无法同时实现精确搜索和空结果验证。针对上述问题,我们提出了一种新颖的具有全面验证的前向和后向私有连接DSSE,称为VCFB。VCFB引入了随机盲因素和安全动态交叉标签的概念,以实现精确的亚线性开销的联合搜索。通过设计搜索状态和构造链结构来保证前向安全性。基于双线性映射动态累加器的验证算法即使返回空结果也能保证搜索结果的可验证性。我们使用样本检查方法来验证新的动态交叉标签,减少了计算成本。我们精确定义了VCFB的泄漏函数,并给出了详细的安全性证明,证明了VCFB在恶意服务器模型下是前向和后向私有的。实验结果表明,VCFB保证了高效准确的联合搜索,在索引存储和更新性能方面优于类似的可验证联合DSSE方案。
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来源期刊
CiteScore
11.80
自引率
2.80%
发文量
114
期刊介绍: The IEEE Transactions on Big Data publishes peer-reviewed articles focusing on big data. These articles present innovative research ideas and application results across disciplines, including novel theories, algorithms, and applications. Research areas cover a wide range, such as big data analytics, visualization, curation, management, semantics, infrastructure, standards, performance analysis, intelligence extraction, scientific discovery, security, privacy, and legal issues specific to big data. The journal also prioritizes applications of big data in fields generating massive datasets.
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