PRIVE: anonymous location-based queries in distributed mobile systems

By Gabriel Ghinita, Panos Kalnis, Spiros Skiadopoulos. The Web Conference, 2007

460 citations recorded by the source corpus.

Nowadays, mobile users with global positioning devices canaccess Location Based Services (LBS) and query about pointsof interest in their proximity. For such applications to succeed,privacy and confidentiality are essential. Encryptionalone is not adequate; although it safeguards the systemagainst eavesdroppers, the queries themselves may disclosethe location and identity of the user. Recently, there havebeen proposed centralized architectures based on K-anonymity,which utilize an intermediate anonymizer between themobile users and the LBS. However, the anonymizer mustbe updated continuously with the current locations of allusers. Moreover, the complete knowledge of the entire systemposes a security threat, if the anonymizer is compromised.In this paper we address two issues: (i) We show thatexisting approaches may fail to provide spatial anonymityfor some distributions of user locations and describe a noveltechnique which solves this problem. (ii) We propose Prive,a decentralized architecture for preserving the anonymityof users issuing spatial queries to LBS. Mobile users self-organizeinto an overlay network with good fault toleranceand load balancing properties. Prive avoids the bottleneckcaused by centralized techniques both in terms of anonymizationand location updates. Moreover, the system state isdistributed in numerous users, rendering Prive resilient toattacks. Extensive experimental studies suggest that Priveis applicable to real-life scenarios with large populations ofmobile users.

Subfields: Artificial Intelligence, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/2048ddf30565f3f44544ca4ac741278d6cc2ae2d

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Citations in the corpus

The new Casper: query processing for location services without compromising privacy (2006, background): “, [Bamba et al. 2008; Beresford and Stajano 2003; Chow et al. 2006; Chow and Mokbel 2007; Cheng et al. 2006; Duckham and Kulik 2005; Gedik and Liu 2008; Gruteser and Grunwald 2003; Ghinita et al. 2007a; 2007b; Gruteser and Liu 2004; Hashem and Kulik 2007; Hengartner and Steenkiste 2003; Hong and Lan”
Privacy Preservation in the Publication of Trajectories (2008, background): “[7] showed how to apply this framework in a distributed environment, where users dynamically form and maintain the k-anonymized spatial regions.”
Mobile cloud computing: A survey (2013, unknown): “Examples exist in the p2p domain, such as [89–91].”
Preventing Location-Based Identity Inference in Anonymous Spatial Queries (2007, background): “Ghinita et al. [12], [13] and Chow et al. [10] study spatial cloaking in peer-to-peer systems.”
Dissent in Numbers: Making Strong Anonymity Scale (2012, unknown): “As everyday computing shifts to mobile devices, an ongoing challenge is to offer users the same privacy protections on phones as they would have on desktop computers [8,33,34].”
Mobile Edge Computing (2021, background): “…the needs in both privacy protection and service performance, e.g., establishing trusted local proxies for anonymous access to information and computation [21], and replacing deep packet inspection with locally-aware machine-learning techniques for traﬃc classiﬁcation and anomaly detection [30].”
Computing with Spatial Trajectories (2011, methodology): “tasks and thus can be extended for trajectory data reduction [19, 22, 20, 21].”