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Email tracking allows email senders to collect fine-grained behavior and location data on email recipients, who are uniquely identifiable via their email address. Such tracking invades user privacy in that email tracking techniques gather data without user consent or awareness. Striving to increase privacy in email communication, this paper develops a detection engine to be the core of a selective tracking blocking mechanism in the form of three contributions. First, a large collection of email newsletters is analyzed to show the wide usage of tracking over different countries, industries and time. Second, we propose a set of features geared towards the identification of tracking images under real-world conditions. Novel features are devised to be computationally feasible and efficient, generalizable and resilient towards changes in tracking infrastructure. Third, we test the predictive power of these features in a benchmarking experiment using a selection of state-of-the-art classifiers to clarify the effectiveness of model-based tracking identification. We evaluate the expected accuracy of the approach on out-of-sample data, over increasing periods of time, and when faced with unknown senders. (C) 2018 Elsevier B.V. All rights reserved.
E-Mail Tracking
(2016)
E-mail advertisement, as one instrument in the marketing mix, allows companies to collect fine-grained behavioural data about individual users’ e-mail reading habits realised through sophisticated tracking mechanisms. Such tracking can be harmful for user privacy and security. This problem is especially severe since e-mail tracking techniques gather data without user consent. Striving to increase privacy and security in e-mail communication, the paper makes three contributions. First, a large database of newsletter e-mails is developed. This data facilitates investigating the prevalence of e- mail tracking among 300 global enterprises from Germany, the United Kingdom and the United States. Second, countermeasures are developed for automatically identifying and blocking e-mail tracking mechanisms without impeding the user experience. The approach consists of identifying important tracking descriptors and creating a neural network-based detection model. Last, the effectiveness of the proposed approach is established by means of empirical experimentation. The results suggest a classification accuracy of 99.99%.
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.
Myasthenia gravis is an autoimmune disease affecting neuromuscular transmission and causing skeletal muscle weakness. Additionally, systemic inflammation, cognitive deficits and autonomic dysfunction have been described.
However, little is known about myasthenia gravis-related reorganization of the brain. In this study, we thus investigated the structural and functional brain changes in myasthenia gravis patients.
Eleven myasthenia gravis patients (age: 70.64 +/- 9.27; 11 males) were compared to age-, sex- and education-matched healthy controls (age: 70.18 +/- 8.98; 11 males). Most of the patients (n = 10, 0.91%) received cholinesterase inhibitors.
Structural brain changes were determined by applying voxel-based morphometry using high-resolution T-1-weighted sequences. Functional brain changes were assessed with a neuropsychological test battery (including attention, memory and executive functions), a spatial orientation task and brain-derived neurotrophic factor blood levels.
Myasthenia gravis patients showed significant grey matter volume reductions in the cingulate gyrus, in the inferior parietal lobe and in the fusiform gyrus. Furthermore, myasthenia gravis patients showed significantly lower performance in executive functions, working memory (Spatial Span, P = 0.034, d = 1.466), verbal episodic memory (P = 0.003, d = 1.468) and somatosensory-related spatial orientation (Triangle Completion Test, P = 0.003, d = 1.200).
Additionally, serum brain-derived neurotrophic factor levels were significantly higher in myasthenia gravis patients (P = 0.001, d = 2.040). Our results indicate that myasthenia gravis is associated with structural and functional brain alterations. Especially the grey matter volume changes in the cingulate gyrus and the inferior parietal lobe could be associated with cognitive deficits in memory and executive functions.
Furthermore, deficits in somatosensory-related spatial orientation could be associated with the lower volumes in the inferior parietal lobe. Future research is needed to replicate these findings independently in a larger sample and to investigate the underlying mechanisms in more detail.
Klaus et al. compared myasthenia gravis patients to matched healthy control subjects and identified functional alterations in memory functions as well as structural alterations in the cingulate gyrus, in the inferior parietal lobe and in the fusiform gyrus.