Title

Private Keyword-Search for Database Systems Against Insider Attacks

RIS ID

114185

Publication Details

Jiang, P., Mu, Y., Guo, F. & Wen, Q. (2017). Private Keyword-Search for Database Systems Against Insider Attacks. Journal of Computer Science and Technology, 32 (3), 599-617.

Abstract

The notion of searchable encrypted keywords introduced an elegant approach to retrieve encrypted data without the need of decryption. Since the introduction of this notion, there are two main searchable encrypted keywords techniques, symmetric searchable encryption (SSE) and public key encryption with keyword search (PEKS). Due to the complicated key management problem in SSE, a number of concrete PEKS constructions have been proposed to overcome it. However, the security of these PEKS schemes was only weakly defined in presence of outsider attacks; therefore they suffer from keyword guessing attacks from the database server as an insider. How to resist insider attacks remains a challenging problem. We propose the first searchable encrypted keywords against insider attacks (SEK-IA) framework to address this problem. The security model of SEK-IA under public key environment is rebuilt. We give a concrete SEK-IA construction featured with a constant-size trapdoor and the proposed scheme is formally proved to be secure against insider attacks. The performance evaluations show that the communication cost between the receiver and the server in our SEK-IA scheme remains constant, independent of the sender identity set size, and the receiver needs the minimized computational cost to generate a trapdoor to search the data from multiple senders.

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Link to publisher version (DOI)

http://dx.doi.org/10.1007/s11390-017-1745-8