Abstract
Targeted advertising is an inherent part of the modern Web as we know it. For this purpose, personal data is collected at large scale to optimize and personalize displayed advertisements to increase the probability that we click them. Anonymity and privacy are also important aspects of the World Wide Web since its beginning. Activists and developers relentlessly release tools that promise to protect us from Web tracking. Besides extensive blacklists to block Web trackers, researchers used machine learning techniques in the past years to automatically detect Web trackers. However, for this purpose often artificial data is used, which lacks in quality.
Due to its sensitivity and the manual effort to collect it, real user data is avoided. Therefore, we present T.EX - The Transparency EXtension, which aims to record a browsing session in a secure and privacy-preserving manner. We define requirements and objectives, which are used for the design of the tool. An implementation is presented, which is evaluated for its performance. The evaluation shows that our implementation can be used for the collection of data to feed machine learning algorithms.
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
Bau, J., Mayer, J., Paskov, H., Mitchell, J.: A promising direction for web tracking countermeasures. In: Workshop on Web 2.0 Security and Privacy (2013)
Bujlow, T., Carela-Espanol, V., Lee, B.R., Barlet-Ros, P.: A survey on web tracking: mechanisms, implications, and defenses. Proc. IEEE 105(8), 1476–1510 (2017)
Cliqz - Der sichere Browser mit integrierter Schnell-Suche. https://cliqz.com/. Accessed 4 Feb 2019
Crumble - Online Privacy, Stop Tracking. https://chrome.google.com/webstore/detail/crumble-online-privacy/icpfjjckgkocbkkdaodapelofhgjncoh. Accessed 4 Feb 2019
Disconnect. https://disconnect.me/. Accessed 4 Feb 2019
Englehardt, S., Narayanan, A.: Online tracking. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security - CCS 2016, no. 1, pp. 1388–1401 (2016)
Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation), OJ L 119, pp. 1–88, 4 May 2016
Kontaxis, G., Chew, M.: Tracking protection in Firefox for privacy and performance. In: IEEE Web 2.0 Security & Privacy, June 2015
Firefox Lightbeam - Add-ons for Firefox. https://addons.mozilla.org/de/firefox/addon/lightbeam/. Accessed 4 Feb 2019
Ghostery Makes the Web Cleaner, Faster and Safer! https://www.ghostery.com. Accessed 4 Feb 2019
Lerner, A., Simpson, A. K., Kohno, T., Roesner, F.: Internet Jones and the raiders of the lost trackers: an archaeological study of web tracking from 1996 to 2016. In: Usenix Security (2016)
Metwalley, H., Traverso, S., Mellia, M.: Unsupervised detection of web trackers. In: IEEE Global Communications Conference (GLOBECOM), pp. 1–6 (2015)
Privacy Badger - Electronic Frontier Foundation. https://www.eff.org/privacybadger. Accessed 4 Feb 2019
Thode, W., Griesbaum, J., Mandl, T.: I would have never allowed it: user perception of third-party tracking and implications for display advertising. Re:inventing information science in the networked society. In: Proceedings of the 14th International Symposium on Information Science (ISI 2015), Zadar, Croatia, 19th–21st May 2015, vol. 66, pp. 445–456 (2015)
BO-Scope: a tool to measure over time your own exposure to third parties on the web. https://github.com/gorhill/uBO-Scope. Accessed 4 Feb 2019
UltraBlock - Block Ads, Trackers and Third Party Cookies. https://ultrablock.org/. Accessed 4 Feb 2019
Matrix: point and click matrix to filter net requests according to source, destination and type. https://github.com/gorhill/uMatrix. Accessed 4 Feb 2019
Wu, Q., Liu, Q., Zhang, Y., Liu, P., Wen, G.: A machine learning approach for detecting third-party trackers on the web. In: Askoxylakis, I., Ioannidis, S., Katsikas, S., Meadows, C. (eds.) ESORICS 2016. LNCS, vol. 9878, pp. 238–258. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45744-4_12
Yu, Z., Macbeth, S., Modi, K., Pujol, J. M.: Tracking the trackers. In: Proceedings of the 25th International Conference on World Wide Web - WWW 2016, pp. 121–132 (2016)
Acknowledgments
Supported by the European Union’s Horizon 2020 research and innovation programme under grant 731601.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Raschke, P., Zickau, S., Kröger, J.L., Küpper, A. (2019). Towards Real-Time Web Tracking Detection with T.EX - The Transparency EXtension. In: Naldi, M., Italiano, G., Rannenberg, K., Medina, M., Bourka, A. (eds) Privacy Technologies and Policy. APF 2019. Lecture Notes in Computer Science(), vol 11498. Springer, Cham. https://doi.org/10.1007/978-3-030-21752-5_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-21752-5_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-21751-8
Online ISBN: 978-3-030-21752-5
eBook Packages: Computer ScienceComputer Science (R0)