SESOS: A Verifiable Searchable Outsourcing Scheme for Ordered Structured Data in Cloud Computing

Document Type: ORIGINAL RESEARCH PAPER

Authors

1 Sharif University of Technology, Tehran, Iran & Hong Kong University of Science and Technology, Hong Kong

2 Sharif University of Technology, Tehran, Iran

3 Associate Professor, Department of Computer Engineering, Sharif University of Technology, Tehran, IRAN.

4 Hong Kong University of Science and Technology, Hong Kong

Abstract

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).

Keywords


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