地理位置推理攻击:从建模到隐私风险评估(短文)

Miguel Núñez del Prado Cortez, Jesus Frignal
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引用次数: 6

摘要

尽管基于位置的服务(LBS)在商业上取得了成功,但它们管理的数据的敏感性,特别是那些与用户位置有关的数据,使它们成为地理位置推断攻击的合适目标。这些攻击是传统推理攻击的一种新变体,旨在从用户的地理位置数据集中泄露用户生活的个人方面。由于这种威胁可能会极大地损害用户的隐私,从而损害LBS的信心,因此对地理位置推断攻击的深入了解对于保护LBS至关重要。为了实现这一目标,这篇短文向前迈出了一步,对众所周知的地理位置推断攻击类型进行建模,作为定量评估它们构成的隐私风险的前一步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Geo-Location Inference Attacks: From Modelling to Privacy Risk Assessment (Short Paper)
Despite the commercial success of Location-Based Services (LBS), the sensitivity of the data they manage, specially those concerning the user's location, makes them a suitable target for geo-location inference attacks. These attacks are a new variant of traditional inference attacks aiming at disclosing personal aspects of users' life from their geo-location datasets. Since this threat might dramatically compromise the privacy of users, and so the confidence of LBS, a deeper knowledge of geo-location inference attacks becomes essential to protect LBS. To contribute to this goal, this short paper makes a step forward to model well-known types of geo-location inference attacks as a previous step to quantitatively assess the privacy risk they pose.
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