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DEKS: A Secure Cloud-Based Searchable Service Can Make Attackers Pay

Authors: Yubo Zheng; Peng Xu 0003; Wei Wang 0088; Tianyang Chen; Willy Susilo; Kaitai Liang; Hai Jin 0001;

DEKS: A Secure Cloud-Based Searchable Service Can Make Attackers Pay

Abstract

Many practical secure systems have been designed to prevent real-world attacks via maximizing the attacking cost so as to reduce attack intentions. Inspired by this philosophy, we propose a new concept named delay encryption with keyword search (DEKS) to resist the notorious keyword guessing attack (KGA), in the context of secure cloud-based searchable services. Avoiding the use of complex (and unreasonable) assumptions, as compared to existing works, DEKS optionally leverages a catalyst that enables one (e.g., a valid data user) to easily execute encryption; without the catalyst, any unauthenticated system insiders and outsiders take severe time consumption on encryption. By this, DEKS can overwhelm a KGA attacker in the encryption stage before it obtains any advantage. We leverage the repeated squaring function, which is the core building block of our design, to construct the first DEKS instance. The experimental results show that DEKS is practical in thwarting KGA for both small and large-scale datasets. For example, in the Wikipedia, a KGA attacker averagely takes 7.23 years to break DEKS when the delay parameter 𝑇=224. The parameter T can be flexibly adjusted based on practical needs, and theoretically, its upper bound is infinite.

https://doi.org/10.1007/978-3-031-17146-8_5

Countries
Australia, Netherlands
Keywords

Privacy, Delay encryption with keyword search, Keyword guessing attack, Security

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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