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Practical approximate k-nearest neighbor queries with location and query privacy
Yi X., Paulet R., Bertino E., Varadharajan V. IEEE Transactions on Knowledge and Data Engineering28 (6):1546-1559,2016.Type:Article
Date Reviewed: Sep 20 2016

Location-based services (LBS) have been used as a Trojan horse to reveal users’ private data. Since they run on mobile applications, individuals can share real-time and historical location information online.

It is in this context that this paper provides a contribution, not solely via the technical solutions, but also by making readers aware of the privacy risks. Current algorithmic approaches and techniques such as information access control, mix zone, “dummy” locations, geographic data transformation, and the like fall short as security- and privacy-respecting protocols. It turns out that these approaches are vulnerable to potential attackers and untrusted providers.

In order to fill this gap, this paper provides an answer to the question of whether one could guarantee security and privacy for LBS-related queries. The answer comprises k-nearest neighbor (k-NN) queries and is based on private information retrieval with the Paillier public-key cryptosystem. The paper is well written and structured.

The paper is a promising starting point for those PhD students and researchers aspiring to conduct research in this field. From a pedagogical point of view, the surveyed related work could pave the way for setting a reading list for postgraduate students or business seminars in the field of big data analytics, mobile communication, and data privacy. The sections discussing the algorithms with the proof of their correctness may be challenging for those with no background knowledge in the theory of computer science algorithms.

Reviewer:  Epaminondas Kapetanios Review #: CR144779 (1612-0890)
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Wireless Communication (C.2.1 ... )
 
 
Access Controls (D.4.6 ... )
 
 
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Data Encryption (E.3 )
 
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