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An improved dummy generation approach for infeasible regions

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Abstract

Location-based services (LBS), which provide personalized and timely information, entail privacy concerns such as unwanted leaks of current user locations to potential stalkers. In this regard, existing works have proposed dummy generation techniques by creating a cloaking region (CR) such that the user’s location is at a fixed distance from the centre of CR. Hence, if the adversary somehow knows the location of the centre of CR, the user’s location would be vulnerable to attacks. Moreover, in case of the existing approaches, infeasible regions are assumed to have no relationship with time. However, this assumption is typically not valid in real-world scenarios. For example, a supermarket can be considered to be an infeasible region from 9 pm to 9 am since it would be closed at that time. Thus, if a dummy is placed at this location at that particular time, the attacker would know that it is a dummy, thereby reducing the user’s location privacy. In this regard, our key contributions are three-fold. First, we propose an improved dummy generation approach, which we designate as Annulus-based Gaussian Dummy Generation (AGDG), for facilitating improved location privacy for mobile users. Second, we introduce the notion of time-dependent infeasible regions to further improve the dummy generation approach by considering infeasible regions that change with time. Third, we conducted experiments to demonstrate that the AGDG effectively provides improved location privacy, including for regions with time-dependent infeasible regions w.r.t. existing approaches.

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    Authors and Affiliations

    1. IIIT Hyderabad, Gachibowli, Hyderabad, 500032, Telangana, India

      Shadaab Siddiqie & P Krishna Reddy

    2. Ashoka University, Sonepat, Rajiv Gandhi Education City, 131029, Haryana, India

      Anirban Mondal

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    1. Shadaab Siddiqie
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    2. Anirban Mondal
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    Correspondence to Shadaab Siddiqie.

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    Anirban Mondal and P Krishna Reddy are contributed equally to this work.

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    Siddiqie, S., Mondal, A. & Reddy, P.K. An improved dummy generation approach for infeasible regions. Appl Intell 53, 18700–18714 (2023). https://doi.org/10.1007/s10489-022-04379-2

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