I am currently a Ph.D. candidate in the SEDAN (SErvices and Data mANagement) research group under the supervision of Dr. Radu State at the University of Luxembourg. Prior to my Ph.D, I worked as a researcher at the Fraunhofer Institute for Applied and Integrated Security (AISEC).
Online tracking of users is used for benign goals, such as detecting fraudulent logins, but also to invade user privacy. We posit that for non-oppressed users, tracking within one website does not have a substantial negative impact on privacy, while it enables legitimate benefits. In contrast, cross-domain tracking negatively impacts user privacy, while being of little benefit to the user. Existing methods to counter fingerprint-based tracking treat cross-domain tracking and regular tracking the same. This often results in hampering or disabling desired functionality, such as embedded videos. By distinguishing between regular and cross-domain tracking, more desired functionality can be preserved. We have developed a prototype tool, FP-Block, that counters cross-domain fingerprint-based tracking while still allowing regular tracking. FP-Block ensures that any embedded party will see a different, unrelatable fingerprint for each site on which it is embedded. Thus, the user’s fingerprint can no longer be tracked across the web, while desired functionality is better preserved compared to existing methods.
Mobile communication has grown quickly in the last two decades. Connections can be wirelessly established from almost any hab- itable place in the earth, leading to a plethora of connection-based track- ing mechanisms, such as GPS, GSM, RFID, etc. Trajectories representing the movement of people are consequently being gathered and analysed in a daily basis. However, a trajectory may contain sensitive and private information, which raises the problem of whether spatio-temporal data can be published in a private manner. In this article, we introduce a novel distance measure for trajectories that captures both aspect of the microaggregation process, namely clustering and obfuscation. Based on this distance measure we propose a trajectory anonymisation heuristic method ensuring that each trajectory is indistin- guishable from k − 1 other trajectories. The proposed distance measure is loosely based on the Fr ́echet distance, yet it can be computed efficiently in quadratic time complexity. Empirical studies on synthetic trajectories show that our anonymisation approach improves previous work in terms of utility without sacrificing privacy.
In molecular biology, the subject of protein structure prediction is of continued interest, not only to chart the molecular map of living cells, but also to design proteins with new functions. The Inverse Folding Problem (IFP) of finding sequences that fold into a defined structure is in itself an important research problem at the heart of rational protein design. In this work the Preference-Based Genetic Algorithm (PBGA) is employed to find many diversified solutions to the IFP. The PBGA algorithm incorporates a weighted sum model in order to combine fitness and diversity into a single objective function scoring a set of individuals as a whole. By adjusting the sum weights, a direct control of the fitness vs. diversity trade-off in the algorithm population is achieved by means of a selection scheme iteratively removing the least contributing individuals. Experimental results demonstrate the superior performance of the PBGA algorithm compared to other state-of-the-art algorithms both in terms of fitness and diversity.