A Predictive Model for User Motivation and Utility Implications of Privacy-Protection Mechanisms in Location Check-Ins

Abstract

Location check-ins contain both geographical and semantic information about the visited venues. Semantic information is usually represented by means of tags (e.g., "restaurant"). Such data can reveal some personal information about users beyond what they actually expect to disclose, hence their privacy is threatened. To mitigate such threats, several privacy protection techniques based on location generalization have been proposed. Although the privacy implications of such techniques have been extensively studied, the utility implications are mostly unknown. In this paper, we propose a predictive model for quantifying the effect of a privacy-preserving technique (i.e., generalization) on the perceived utility of check-ins. We first study the users' motivations behind their location check-ins, based on a study targeted at Foursquare users (N = 77). We propose a machine-learning method for determining the motivation behind each check-in, and we design a motivation-based predictive model for the utility implications of generalization. Based on the survey data, our results show that the model accurately predicts the fine-grained motivation behind a check-in in 43% of the cases and in 63% of the cases for the coarse-grained motivation. It also predicts, with a mean error of 0.52 (on a scale from 1 to 5), the loss of utility caused by semantic and geographical generalization. This model makes it possible to design of utility-aware, privacy-enhancing mechanisms in location-based online social networks. It also enables service providers to implement location-sharing mechanisms that preserve both the utility and privacy for their users.

The Long Road to Computational Location Privacy: A Survey

“…By leveraging the knowledge provided by the US Census Bureau and unsupervised machine learning methods, this personal location privacy auditing tool provides prediction on the home place of users as well as their age, income and ethnicity. Huguenin et al [55] leveraged machine learning to infer the motivation (e.g., "Inform about activity", "Share mood" or "Wish people to join me") behind check-ins of users on Foursquare. They achieved up to 63 % of accuracy when predicting a coarse-grained motivation.…”

“…They explore a notion of identifiability, quantifying the probability of an attacker to identify the user's location from the cloaking area, that help them to choose a value for the parameter. Huguenin et al [55] studied the effect of using generalization-based LPPMs while using LBSs such as Foursquare [4] to leave checkins. Towards this end, they used a predictive model to quantify the effect of generalization on perceived utility.…”

The Research of Mobile Location Privacy Protection Access Control Method Based on Game Theory

“…There is also someone in conjunction with other technologies for privacy protection research. Huguenin K. et al use machine learning methods to predict the user's motivation to check-in and quantify utility implications to protect location privacy [11]. Based on mobile cloud computing, Gong Y. et al proposed a framework to protect location privacy when assigning tasks to mobile devices, allowing mobile devices to contribute resources to the ad hoc mobile cloud without revealing location information [12].…”

Enabling Location Privacy Preservation in MANETs Based on Distance, Angle, and Spatial Cloaking

“…Location information makes wireless devices become location-aware data collection instruments, such as smartphones in Mobile Ad hoc Networks (MANETs) or vehicles in Vehicular Ad hoc Networks (VANETs) [1]. The applications of location information include Mobile Crowd Sensing (MCS) [2,3], geographic routing [4], Location-Based Service (LBS) [5], Online Social Networks (OSN) [6], Internet of Things (IoT) [7], mobile Wireless Sensor Networks (WSN) [8], etc. In these applications, mobile users report their locations to the server through the Access Point (AP) or the Base Station (BS) to acquire services.…”