Quantifying Interdependent Privacy Risks with Location Data

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Quantifying Interdependent Privacy Risks with Location Data

Quantifying Interdependent Privacy Risks with Location Data

Abstract

The emergence of location-based social networks offers an unprecedented opportunity to study the interaction between human mobility and social relations.This work is a step towards quantifying the suitability of a location for social activities,Quantifying Interdependent Privacy reports Risks with Location Data and the notion is called location sociality.Practical opportunities such as urban planning and location recommendation are created by being able to quantify location sociality

Introduction

Quantifying Interdependent Privacy Risks with Location Data Nowadays, sharing their geographical locations, i.e. check-ins, is quite common for OSN users. In addition, location-based social networks (LBSNs) are created as a special type of OSNs dedicated to location sharing. Two representative companies are Foursquare and Yelp. A large quantity of human mobility data becomes available with the emergence of LBSNs.

Disadvantages

  • Attacks exploiting both location and co-location information can be quite powerful.
  • projects on Quantifying Interdependent Privacy Co-location can improve the performance of a localization attack, thus degrading the location privacy of the users involved.

Advantages

  • Even in the case where a user does not disclose any location information, her privacy can decrease by up to 21% due to the information reported by other users.

Conclusion

Projects on Quantifying Interdependent Privacy have proposed a new notion in this paper, namely locationsociality, to describe whether a location is suitable for conducting social activities.Experimental results of millions of Instagram check-in data validate location sociality with some in-depth discoveries. Two case studies, including friendship prediction and location recommendation, show the usefulness of our quantification.