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Top-k Query Processing over Distributed Sensitive Data

Authors: Mahboubi, Sakina; Akbarinia, Reza; Valduriez, Patrick;

Top-k Query Processing over Distributed Sensitive Data

Abstract

Distributed systems provide users with powerful capabilities to store and process their data in third-party machines. However, the privacy of the outsourced data is not guaranteed. One solution for protecting the user data against privacy attacks is to encrypt the sensitive data before sending to the nodes of the distributed system. Then, the main problem is to evaluate user queries over the encrypted data.In this paper, we propose a complete solution for processing top-k queries over encrypted databases stored across the nodes of a distributed system. The problem of distributed top-k query processing has been well addressed over plaintext (non encrypted) data. However, the proposed approaches cannot be used in the case of encrypted data.

Country
France
Keywords

Top-k Query, Distributed System, Privacy, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], Sensitive Data

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selected citations
These citations are derived from selected sources.
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).
BIP!Citations provided by BIP!
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.
BIP!Impulse provided by BIP!
3
Average
Average
Average
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