On web user tracking of browsing patterns for personalised advertising

Abstract On today’s Web, users trade access to their private data for content and services. App and service providers want to know everything they can about their users, in order to improve their product experience. Also, advertising sustains the business model of many websites and applications. Efficient and successful advertising relies on predicting users’ actions and tastes to suggest a range of products to buy. Both service providers and advertisers try to track users’ behaviour across their product network. For application providers this means tracking users’ actions within their platform. For third-party services following users, means being able to track them across different websites and applications. It is well known how, while surfing the Web, users leave traces regarding their identity in the form of activity patterns and unstructured data. These data constitute what is called the user’s online footprint. We analyse how advertising networks build and collect users footprints and how the suggested advertising reacts to changes in the user behaviour. Graphical Abstract

[1]  Hovav Shacham,et al.  Fingerprinting Information in JavaScript Implementations , 2011 .

[2]  Jose L. Muñoz,et al.  Optimal tag suppression for privacy protection in the semantic Web , 2012, Data Knowl. Eng..

[3]  Jordi Forné,et al.  An Information-Theoretic Privacy Criterion for Query Forgery in Information Retrieval , 2011, FGIT-SecTech.

[4]  Tiago P. Peixoto.  The entropy of stochastic blockmodel ensembles , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[5]  Jordi Forné,et al.  Measuring the privacy of user profiles in personalized information systems , 2014, Future Gener. Comput. Syst..

[6]  R. Tsien,et al.  Specificity and Stability in Topology of Protein Networks , 2022 .

[7]  R Pastor-Satorras,et al.  Dynamical and correlation properties of the internet. , 2001, Physical review letters.

[8]  Tiago P Peixoto,et al.  Parsimonious module inference in large networks. , 2012, Physical review letters.

[9]  Norman M. Sadeh,et al.  What do they know about me? Contents and Concerns of Online Behavioral Profiles , 2015, ArXiv.

[10]  Víctor Pàmies,et al.  Open Directory Project , 2003 .

[11]  Ashwin Machanavajjhala,et al.  Entity Resolution: Theory, Practice & Open Challenges , 2012, Proc. VLDB Endow..

[12]  Jérôme Kunegis,et al.  Tracking the Trackers: A Large-Scale Analysis of Embedded Web Trackers , 2016, ICWSM.

[13]  Tiago P. Peixoto.  Hierarchical block structures and high-resolution model selection in large networks , 2013, ArXiv.

[14]  S. Wasserman,et al.  Blockmodels: Interpretation and evaluation , 1992 .

[15]  Jordi Forné,et al.  On Web user tracking: How third-party http requests track users' browsing patterns for personalised advertising , 2016, 2016 Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net).

[16]  Nicola Zannone,et al.  Are On-Line Personae Really Unlinkable? , 2013, DPM/SETOP.

[17]  M E J Newman.  Assortative mixing in networks. , 2002, Physical review letters.

[18]  Kathryn B. Laskey,et al.  Stochastic blockmodels: First steps , 1983 .

[19]  Claude Castelluccia.  Behavioural Tracking on the Internet: A Technical Perspective , 2012, European Data Protection.

[20]  Nick Cramer,et al.  Automatic Keyword Extraction from Individual Documents , 2010 .

[21]  Konstantina Papagiannaki,et al.  Like a Pack of Wolves: Community Structure of Web Trackers , 2016, PAM.

[22]  Natasa Milic-Frayling,et al.  Network Analysis of Third Party Tracking: User Exposure to Tracking Cookies through Search , 2013, 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT).

[23]  Jordi Forné,et al.  You Never Surf Alone. Ubiquitous Tracking of Users' Browsing Habits , 2015, DPM/QASA@ESORICS.

[24]  K. Sneppen,et al.  Specificity and Stability in Topology of Protein Networks , 2002, Science.

[25]  Nora Cuppens-Boulahia,et al.  Data Privacy Management and Autonomous Spontaneous Security , 2014, Lecture Notes in Computer Science.

[26]  Piet Van Mieghem,et al.  Assortativity in complex networks , 2015, J. Complex Networks.

[27]  Thomas Herrmann,et al.  Your Interests According to Google - A Profile-Centered Analysis for Obfuscation of Online Tracking Profiles , 2016, ArXiv.

[28]  Katina Michael,et al.  Location and tracking of mobile devices: Überveillance stalks the streets , 2013, Comput. Law Secur. Rev..

[29]  Jordi Forné,et al.  Privacy-Preserving Enhanced Collaborative Tagging , 2014, IEEE Transactions on Knowledge and Data Engineering.

[30]  Tiago P. Peixoto.  Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[31]  Sándor Imre,et al.  User Tracking on the Web via Cross-Browser Fingerprinting , 2011, NordSec.

[32]  A. Vespignani,et al.  The architecture of complex weighted networks. , 2003, Proceedings of the National Academy of Sciences of the United States of America.