Predicting Users' Motivations behind Location Check-Ins and Utility Implications of Privacy Protection Mechanisms

By Igor Bilogrevic, Kévin Huguenin, Stefan Mihaila, Reza Shokri, Jean-Pierre Hubaux. 2015

48 citations recorded by the source corpus.

Location check-ins contain both geographical and semantic information about the visited venues, in the form of tags (e.g., “restaurant”). Such data might reveal some personal information about users beyond what they actually want to disclose, hence their privacy is threatened. In this paper, we study users’ motivations behind location check-ins, and we quantify the effect of a privacy-preserving technique (i.e., generalization) on the perceived utility of check-ins. By means of a targeted user study on Foursquare (N = 77), we show that the motivation behind Foursquare check-ins is a mediator of the loss of utility caused by generalization. Using these findings, we propose a machine learning method for determining the motivation behind each check-in, and we design a motivation-based predictive model for utility. Our results show that the model accurately predicts the loss of utility caused by semantic and geographical generalization; this model enables the design of utility-aware, privacy-enhancing mechanisms in location-based social networks.

Subfields: Artificial Intelligence, Computer Science Applications, Sociology and Political Science

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Supriyo Chakraborty
2. Changchang Liu
3. Prateek Mittal
4. Peter Kairouz
5. Lalitha Sankar
6. Chong Huang
7. Neil Zhenqiang Gong

In Computer Science Applications

1. Prateek Mittal
2. Supriyo Chakraborty
3. Changchang Liu
4. Peter Kairouz
5. Lalitha Sankar
6. Chong Huang

In Sociology and Political Science

1. Prateek Mittal
2. Supriyo Chakraborty
3. Changchang Liu
4. Lalitha Sankar
5. Chong Huang
6. Peter Kairouz

Citations in the corpus

• Dependence Makes You Vulnberable: Differential Privacy Under Dependent Tuples (2016, background): “Sharing of location information is often associated with serious privacy threats [3], [35], [36].”
• Quantifying and protecting location privacy (2015, unknown): “The follow-ups of these solutions are used to design optimal obfuscationmechanisms in more advanced settings [2, 21, 26].”