Print Email Facebook Twitter Partial Device Fingerprints Title Partial Device Fingerprints Author Ciere, M. (TU Delft Organisation & Governance) Hernandez Ganan, C. (TU Delft Organisation & Governance) van Eeten, M.J.G. (TU Delft Organisation & Governance) Date 2017 Abstract In computing, remote devices may be identified by means of device fingerprinting, which works by collecting a myriad of clientside attributes such as the device’s browser and operating system version, installed plugins, screen resolution, hardware artifacts, Wi-Fi settings, and anything else available to the server, and then merging these attributes into uniquely identifying fingerprints. This technique is used in practice to present personalized content to repeat website visitors, detect fraudulent users, and stop masquerading attacks on local networks. However, device fingerprints are seldom uniquely identifying. They are better viewed as partial device fingerprints, which do have some discriminatory power but not enough to uniquely identify users. How can we infer from partial fingerprints whether different observations belong to the same device?We present a mathematical formulation of this problem that enables probabilistic inference of the correspondence of observations. We set out to estimate a correspondence probability for every pair of observations that reflects the plausibility that they are made by the same user. By extending probabilistic data association techniques previously used in object tracking, traffic surveillance and citation matching, we develop a general-purpose probabilistic method for estimating correspondence probabilities with partial fingerprints. Our approach exploits the natural variation in fingerprints and allows for use of situation-specific knowledge through the specification of a generative probability model. Experiments with a real-world dataset show that our approach gives calibrated correspondence probabilities. Moreover, we demonstrate that improved results can be obtained by combining device fingerprints with behavioral models To reference this document use: http://resolver.tudelft.nl/uuid:761fad4f-66b0-4f34-a875-c626eb2cde7c DOI https://doi.org/10.1007/978-3-319-71246-8_14 Publisher Springer ISBN 978-3-319-71245-1 Source proceedings of Joint European Conference on Machine Learning and Knowledge Discovery in Databases Event Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2017-09-18 → 2017-09-22, Skopje, Macedonia, The Former Yugoslav Republic of Series Lecture Notes in Computer Science, 0302-9743, 10535 Bibliographical note Accepted author manuscript Part of collection Institutional Repository Document type conference paper Rights © 2017 M. Ciere, C. Hernandez Ganan, M.J.G. van Eeten Files PDF paperID636.pdf 461.57 KB Close viewer /islandora/object/uuid:761fad4f-66b0-4f34-a875-c626eb2cde7c/datastream/OBJ/view