@article{FabianBenderHesseldiecketal.2020,
  author    = {Fabian, Benjamin and Bender, Benedict and Hesseldieck, Ben and Haupt, Johannes and Lessmann, Stefan},
  title     = {Enterprise-grade protection against e-mail tracking},
  series = {Information Systems},
  journal   = {Information Systems},
  number    = {97},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0306-4379},
  doi       = {10.1016/j.is.2020.101702},
  year      = {2020},
  abstract  = {E-mail tracking provides companies with fine-grained behavioral data about e-mail recipients, which can be a threat for individual privacy and enterprise security. This problem is especially severe since e-mail tracking techniques often gather data without the informed consent of the recipients. So far e-mail recipients lack a reliable protection mechanism. This article presents a novel protection framework against e-mail tracking that closes an impor- tant gap in the field of enterprise security and privacy-enhancing technologies. We conceptualize, implement and evaluate an anti-tracking mail server that is capable of identifying tracking images in e-mails via machine learning with very high accuracy, and can selectively replace them with arbitrary images containing warning messages for the recipient. Our mail protection framework implements a selective prevention strategy as enterprise-grade software using the design science research paradigm. It is flexibly extensible, highly scalable, and ready to be applied under actual production conditions. Experimental evaluations show that these goals are achieved through solid software design, adoption of recent technologies and the creation of novel flexible software components.},
  language  = {en}
}