Abstract
Each person generates a plethora of information just by browsing the Web, some of which is publicly available and some other that should remain private. In recent years, the line between public and private/sensitive information is becoming harder to distinguish and the information generated on the Web is being sold and used for advertising purposes, turning the personal lives of users into products and assets. It is extremely challenging for users and authorities to verify the behavior of trackers, advertisers and advertising websites in order to take action in case of misconduct. To address these issues, we present PrivLeAD, a domain independent system for the detection of sensitive data leakage in online advertisement. Specifically, PrivLeAD leverages Residual Networks to analyze the advertisements a user receives during normal internet browsing and thus detect in real time potential leakage of sensitive information. The system has been tested on real and synthetic data proving its feasibility and practical effectiveness.
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Pachilakis, M., Antonatos, S., Levacher, K., Braghin, S. (2021). PrivLeAD: Privacy Leakage Detection on the Web. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing, vol 1250. Springer, Cham. https://doi.org/10.1007/978-3-030-55180-3_32
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DOI: https://doi.org/10.1007/978-3-030-55180-3_32
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