Abstract
Broadcasted mobile advertisements are increasingly being replaced by targeted mobile advertisements through consumer profiling. However privacy is a growing concern among consumers who may eventually prevent the advertising companies from profiling them. This paper proposes an agent-based targeting algorithm that is able to guarantee full consumer privacy while achieving mobile targeted advertising. We implemented a grocery discount-discovery application for iPhone that makes use of the new approach. We show that on modern hardware like on the iPhone, it’s feasible to run a client-based and privacy-preserving targeting algorithm with minimal additional computational overhead compared to a random advertising approach. We evaluated the targeting method by conducting a large-scale field-experiment with 903 participants. Results show that the computational overhead on user devices is well tolerated, compared to the control group with randomized advertising the targeting group showed a significantly increased application usage of 18%.
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© 2013 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Palme, E., Hess, B., Sutanto, J. (2013). Achieving Targeted Mobile Advertisements While Respecting Privacy. In: Uhler, D., Mehta, K., Wong, J.L. (eds) Mobile Computing, Applications, and Services. MobiCASE 2012. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 110. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36632-1_14
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DOI: https://doi.org/10.1007/978-3-642-36632-1_14
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