Abstract
Inference attacks and protection measures are two sides of the same coin. Although the former aims to reveal information while the latter aims to hide it, they both increase awareness regarding the risks and threats from social media apps. On the one hand, inference attack studies explore the types of personal information that can be revealed and the methods used to extract it. An additional risk is that geosocial media data are collected massively for research purposes, and the processing or publication of these data may further compromise individual privacy. On the other hand, consistent and increasing research on location protection measures promises solutions that mitigate disclosure risks. In this paper, we examine recent research efforts on the spectrum of privacy issues related to geosocial network data and identify the contributions and limitations of these research efforts. Furthermore, we provide protection recommendations to researchers that share, anonymise, and store social media data or publish scientific results.
Highlights
Background onInferences, Users, and PoliciesStudies on location inference attacks examine the types of personal information that can be revealed from individual-level spatial trajectories and the accuracy of inferred information
This paper addresses the use of geosocial network data, but it shall not be understood as a guideline relevant to building location-based social networks
An expert, who acts as a designated privacy manager and whose knowledge extends beyond location-related disclosure risks, should oversee data storing and processing tasks
Summary
Data from geosocial networks such as Twitter, Flickr, Instagram, Foursquare and others have become a comprehensively used basis for geospatial analysis in a number of application areas, including disaster management (Laituri and Kodrich 2008; Resch et al 2018), public health and epidemiology (Santillana et al 2015; Boulos et al 2011), urban planning (Foth et al 2011; Resch et al.2016), traffic management (Pan et al 2013; Steiger et al 2016a), crime analysis Types of re-identified information are, for example, the prediction of a social media user’s location in a georeferenced post (Preoţiuc-Pietro and Cohn 2013), the location of social media posts from a georeferenced dataset (Schulz et al 2013), or the home address and identity of individuals that carry GPS receivers (Krumm 2007) Other studies evaluated their inference results questionably because the validation data that were used had significant limitations. Data from public sources can be used as a ground truth information, there has been only one study in which they were used (Krumm 2007) (Table 1, category: inference approach) Some of these studies propose measures to protect subjects’ anonymity in location trajectories, such as perturbation, aggregation (e.g., areal, point, or temporal), considering the desired level of privacy defined by user preferences, shortening the time collection period, and removing sensitive areas (i.e., spatial cloaking) (Table 1, category: countermeasures). 1.5 M people (Schulz et al 2013), Global, 1 Million georeferenced tweets (Preoţiuc-Pietro and Cohn 2013), Globe, Foursquare check-ins of 9167 users (Li and Goodchild 2013), Los Angeles—USA, georeferenced tweets of 5 heavy users (Lampoltshammer et al 2014), London—UK, georeferenced tweets about crime events (Li et al 2016), China, GPS trajectories and social media data of 30 participants in five apps and Wi-Fi traffic records of 22,843 users
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