arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2403.09080cs.CR

针对云上安全$k$-NN计算的唯密文攻击

Ciphertext-Only Attack on a Secure $k$-NN Computation on Cloud

Shyam Murthy, Santosh Kumar Upadhyaya, Srinivas Vivek

AI总结:

本研究发现Sanyashi等人2023年提出的基于ASPE的云端隐私保护$k$-NN加密方案存在重大漏洞,提出高效算法并通过实验验证该方案易受唯密文攻击。

摘要:

云计算的兴起催生了将数据存储与计算任务迁移至云端的趋势。为保护客户数据、业务详情等机密信息,在将敏感数据存储到云端前对其进行加密至关重要。实施加密可防范未授权访问、数据泄露,以及由此产生的经济损失、声誉损害与法律问题。此外,为便于在云端存储的数据上运行数据挖掘算法,加密方案需与域计算兼容。针对特定查询向量的$k$近邻($k$-NN)计算被广泛应用于基于位置的服务等领域。Sanyashi等人(ICISS 2023)提出了一种加密方案,利用非对称标量积保留加密(Asymmetric Scalar-Product-Preserving Encryption, ASPE)实现云端隐私保护的$k$-NN计算。\n在本研究中,我们发现上述Sanyashi等人的加密方案存在重大漏洞。具体而言,我们提出了一种高效算法,并通过实验证明其加密方案易受唯密文攻击(ciphertext-only attack, COA)。

英文摘要:

The rise of cloud computing has spurred a trend of transferring data storage and computational tasks to the cloud. To protect confidential information such as customer data and business details, it is essential to encrypt this sensitive data before cloud storage. Implementing encryption can prevent unauthorized access, data breaches, and the resultant financial loss, reputation damage, and legal issues. Moreover, to facilitate the execution of data mining algorithms on the cloud-stored data, the encryption needs to be compatible with domain computation. The $k$-nearest neighbor ($k$-NN) computation for a specific query vector is widely used in fields like location-based services. Sanyashi et al. (ICISS 2023) proposed an encryption scheme to facilitate privacy-preserving $k$-NN computation on the cloud by utilizing Asymmetric Scalar-Product-Preserving Encryption (ASPE). In this work, we identify a significant vulnerability in the aforementioned encryption scheme of Sanyashi et al. Specifically, we give an efficient algorithm and also empirically demonstrate that their encryption scheme is vulnerable to the ciphertext-only attack (COA).

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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