A novel location privacy framework without trusted third party based on location anonymity prediction

Abstract

Location-based services are becoming increasingly popular with the proliferation of location aware devices. It is not possible to access location-based services and preserve privacy at the same time when the user provides exact location information. Cloaking or obfuscating location data is the only way to protect location-privacy. To do that, most of the systems use third party location anonymizer. In this paper, we propose a novel location privacy framework without any trusted third party (TTP). Most of the existing solutions attempt to satisfy k-anonymity. However, there are several drawbacks of using fixed and user defined k. In order to solve the problems our proposed solution aims to meet probabilistic k-anonymity. Based on historic data expected number of users present in a place is predicted which is used as probabilistic anonymity level. Thus we eliminate the use of any TTP which results into improvement of query-processing time and provides fewer query results for the user to process eventually minimizing the overall response time. Users' exact location information is not revealed in either communication or computation process.

DP-LTOD: Differential Privacy Latent Trajectory Community Discovering Services over Location-Based Social Networks

“…The techniques in this category generalized the actual location into a cloaked spatial regions which were guaranteed to satisfy the k-anonymity. (3) Encryption [24]. The main idea was to provide effective location privacy preserving without the assistance of Trusted Third Party (TTP).…”

Sensitive Semantics-Aware Personality Cloaking on Road-Network Environment

“…In this framework, the trusted anonymizer located between the user and the LSP blurs the user's exact location and transforms the query candidates. (2) two-tier framework [13,21,27,[22][23][24][25][26]. In this framework, the user can directly contract with the LSP through using space transformation [13], Private Information Retrieval (PIR) [21], probabilistic anonymity [26] or peer-to-peer (P2P) model [22][23][24][25][26].…”

Practicalm-k-Anonymization for Collaborative Data Publishing without Trusted Third Party

“…Goryczka et al present a TTP-dependent CPPDP scheme that achieves --anonymity. However, TTP does not always exist in the real world [14,15]. This is especially true in aftermath Edward Snowden's revelation that the US Government has been conducting large-scale government surveillance (http://masssurveillance.info/).…”