SESOS:云计算中有序结构化数据的可验证搜索外包方案

Javad Ghareh Chamani, Mohammad Sadeq Dousti, R. Jalili, D. Papadopoulos
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引用次数: 0

摘要

虽然云计算正以惊人的速度增长,但隐私问题远未得到解决。减少隐私问题的一种方法是以加密的形式将数据存储在云上。然而,加密通常会阻碍有用的计算云服务。一种理论上的方法是采用所谓的完全同态加密,但是开销如此之高,以至于它不被认为是实用的解决方案。其次最好的办法是制作特殊用途的密码系统,它支持云服务需要处理的一组操作。在本文中,我们提出了一个这样的密码系统,它支持对结构化数据类型(如时间戳或网络地址)的有效搜索,这些数据类型由几个已知值的段组成。新的密码系统称为SESOS,提供了执行LIKE查询的能力,以及搜索精确匹配和比较的能力。此外,扩展版本称为XSESOS,允许验证密文的完整性。SESOS的核心是将任何保序加密(OPE)方案与一种称为多映射完全安全加密系统(MuPS)的新型加密方案相结合。我们证明了MuPS是完全安全的,因此SESOS具有与底层OPE方案相同的安全特性。执行相等和比较操作的开销可以忽略不计。与只有100K条记录的数据库上的现有解决方案相比,LIKE查询的性能显著提高了1370X,结果解密的性能提高了520X(在较大的数据库中,改进更为显著)。
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SESOS: A Verifiable Searchable Outsourcing Scheme for Ordered Structured Data in Cloud Computing
While cloud computing is growing at a remarkable speed, privacy issues are far from being solved. One way to diminish privacy concerns is to store data on the cloud in encrypted form. However, encryption often hinders useful computation cloud services. A theoretical approach is to employ the so-called fully homomorphic encryption, yet the overhead is so high that it is not considered a viable solution for practical purposes. The next best thing is to craft special-purpose cryptosystems which support the set of operations required to be addressed by cloud services. In this paper, we put forward one such cryptosystem, which supports efficient search over structured data types, such as timestamps or network addresses, which are comprised of several segments with well-known values. The new cryptosystem, called SESOS, provides the ability to execute LIKE queries, along with the search for exact matches, as well as comparison. In addition, the extended version, called XSESOS, allows for verifying the integrity of ciphertexts. At its heart, SESOS combines any order-preserving encryption (OPE) scheme with a novel encryption scheme called Multi-map Perfectly Secure Cryptosystem(MuPS). We prove that MuPS is perfectly secure, and hence SESOS enjoys the same security properties of the underlying OPE scheme. The overhead of executing equality and comparison operations is negligible. The performance of LIKE queries is significantly improved by up to 1370X and the performance of result decryption improved by 520X compared to existing solutions on a database with merely 100K records (the improvement is even more significant in larger databases).
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