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\begin{document}

\title{A Privacy-Preserving Mobile LBS System for Small Businesses}



\author[1]{Ahmed Ahmed}%
\affil[1]{Prairie View A\&M University Roy G Perry College of Engineering}%


\vspace{-1em}



  
  \date{June 18, 2024}


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\begin{abstract}
Small-to-medium businesses are always seeking affordable ways to
advertise their products and services securely. With the emergence of
mobile technology, it is possible than ever to implement innovative
Location-based Advertising (LBS) systems using smartphones that preserve
the privacy of mobile users. In this paper, we present a prototype
implementation of such systems by developing a distributed
privacy-preserving system, which has parts executing on smartphones as a
mobile app, as well as a web-based application hosted on the cloud. The
mobile app leverages Google Maps libraries to enhance the user
experience in using the app. Mobile users can use the app to commute to
their daily destinations while viewing relevant ads such as job openings
in their neighborhood, discounts on favorite meals, etc. We developed a
client-server privacy architecture that anonymizes the mobile user
trajectories using a bounded perturbation strategy. A multi-modal
sensing approach is proposed for modeling the context switching of the
developed LBS system, which we represent as a Finite State Machine (FSM)
model. The multi-modal sensing approach can reduce the power consumed by
mobile devices by automatically detecting sensing mode changes to avoid
unnecessary sensing. The developed LBS system is organized into two
parts: the business side and the user side. First, the business side
allows business owners to create new ads by providing the ad details,
Geo-location, photos, and any other instructions. Second, the user side
allows mobile users to navigate through the map to see ads while
walking, driving, bicycling, or quietly sitting in their offices.
Experimental results are presented to demonstrate the scalability and
performance of the mobile side. Our experimental evaluation demonstrates
that the mobile app incurs low processing overhead and consequently has
a small energy footprint.%
\end{abstract}%



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