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 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.
This content will become publicly available on June 26, 2024
Exploring Spatial Transformation-Based Privacy in a Small Town
As mobile devices become increasingly prevalent in society, the expected utility of such devices rises; arguably,
the most impact comes from location-based services as they provide tremendous benefits to mobile users. These users also value privacy, i.e., keeping their locations and search queries private, but that is not easy to achieve. It has been previously proposed that user location privacy can be secured through the use of space filling curves due to their ability to preserve spatial proximity while hiding the actual physical locations. With a
space filling curve, such as the Hilbert curve, an application that provides location-based services can allow the user to take advantage of those services without transmitting a physical location. Earlier research has uncovered vulnerabilities of such systems and proposed remedies. But those countermeasures were clearly aimed at reasonably large metropolitan areas. It was not clear if they were appropriate for small towns, which display
sparsity of Points of Interest (POIs) and limited diversity in their categories. This paper studies the issue focusing on a small university town.
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- Award ID(s):
- 2150145
- NSF-PAR ID:
- 10488584
- Publisher / Repository:
- IARIA
- Date Published:
- Journal Name:
- MOBILITY 2023 : The Thirteenth International Conference on Mobile Services, Resources, and Users
- Subject(s) / Keyword(s):
- ["Mobile environments","Location-dependent and sensitive","Privacy","Query Processing."]
- Format(s):
- Medium: X
- Location:
- Nice France
- Sponsoring Org:
- National Science Foundation
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