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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
González Cabañas, J., Cuevas, Á., Cuevas, R.: Unveiling and quantifying facebook exploitation of sensitive personal data for advertising purposes. In: 27th USENIX Security Symposium (USENIX Security 18) (2018)
Electronic Frontier Foundation. Privacy badger. https://www.eff.org/privacybadger
González Cabañas, J., Cuevas, Á., Cuevas, R.: FDVT: data valuation tool for facebook users. In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, CHI 2017. ACM (2017)
Guha, S., Cheng, B., Francis, P.: Privad: practical privacy in online advertising. In: USENIX 2011 (2011)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. CoRR, abs/1512.03385 (2015)
Hussain, Z., Zhang, M., Zhang, X., Ye, K., Thomas, C., Agha, Z., Ong, N., Kovashka, A.: Automatic understanding of image and video advertisements. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)
Privacy International. Your mental health for sale. Technical report (2019)
Jang, D., Jhala, R., Lerner, S., Shacham, H.: An empirical study of privacy-violating information flows in Javascript web applications. In: Proceedings of the 17th ACM conference on Computer and Communications Security (2010)
Kökciyan, N., Yolum, P.: PriGuard: a semantic approach to detect privacy violations in online social networks. IEEE Trans. Knowl. Data Eng. 28, 2724–2737 (2016)
Kontaxis, G., Polychronakis, M., Markatos, E.P.: Minimizing information disclosure to third parties in social login platforms. Int. J. Inf. Secur. 11, 321–332 (2012)
Mayer, J.R., Mitchell, J.C.: Third-party web tracking: policy and technology. In: IEEE Symposium on Security and Privacy (2012)
Pachilakis, M., Papadopoulos, P., Markatos, E.P., Kourtellis, N.: A measurement study of the header bidding ad-ecosystem, No more chasing waterfalls (2019)
Papadopoulos, P., Kourtellis, N., Markatos, E.P.: Cookie synchronization: everything you always wanted to know but were afraid to ask. CoRR, abs/1805.10505 (2018)
Papadopoulos, P., Rodríguez, P.R., Kourtellis, N., Laoutaris, N.: If you are not paying for it, you are the product: how much do advertisers pay to reach you? In: Proceedings of the 2017 Internet Measurement Conference, IMC (2017)
Parmar, A., Dedegikas, C., Toms, M., Dickert, C.: Adblock plus efficacy study. Accessed 24 Oct 2015
Signanini, J.M., McDermott, B.: Ghostery (2014). https://www.ghostery.com/
Solomos, K., Ilia, P., Ioannidis, S., Kourtellis, N.: Cross-device tracking: systematic method to detect and measure CDT. CoRR, abs/1812.11393 (2018)
The Economist. The world’s most valuable resource is no longer oil, but data (2017)
Toubiana, V., Narayanan, A., Boneh, D., Nissenbaum, H., Barocas, S.: Adnostic: privacy preserving targeted advertising. In: Proceedings Network and Distributed System Symposium (2010)
Yu, Z., Macbeth, S., Modi, K., Pujol, J.M.: Tracking the trackers. In: The 25th International Conference on World Wide Web (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-55180-3_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-55179-7
Online ISBN: 978-3-030-55180-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)