@misc{11959, author = {{Wegener, Moritz}}, title = {{{It's a match! - Entwicklung eines Ansatzes zur Identifikation von Cross-Listings auf Airbnb und TripAdvisor}}}, year = {{2019}}, } @misc{13449, author = {{Gerzen, Gabriele}}, title = {{{Do Online Ratings Matter in the Sharing Economy? Analyzing the Impact of Online Ratings on Demand on Airbnb}}}, year = {{2019}}, } @misc{14987, author = {{Resch, Tim}}, title = {{{Möglichkeiten und Grenzen crowdbasierter Ideenbewertung - Ein systematischer Literaturüberblick}}}, year = {{2019}}, } @misc{13748, author = {{Milder, Christoph}}, title = {{{Externe Stimuli zur Geschäftsmodell-Ideengenerierung: Ein Experimenteller Ansatz}}}, year = {{2019}}, } @phdthesis{34167, author = {{Riebler, Heinrich}}, title = {{{Efficient parallel branch-and-bound search on FPGAs using work stealing and instance-specific designs}}}, doi = {{10.17619/UNIPB/1-830}}, year = {{2019}}, } @inproceedings{13259, author = {{Chen, Wei-Fan and Al-Khatib, Khalid and Hagen, Matthias and Wachsmuth, Henning and Stein, Benno}}, booktitle = {{Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom}}, pages = {{76--82}}, title = {{{Unraveling the Search Space of Abusive Language in Wikipedia with Dynamic Lexicon Acquisition}}}, year = {{2019}}, } @inproceedings{13904, abstract = {{In this paper, we introduce updatable anonymous credential systems (UACS) and use them to construct a new privacy-preserving incentive system. In a UACS, a user holding a credential certifying some attributes can interact with the corresponding issuer to update his attributes. During this, the issuer knows which update function is run, but does not learn the user's previous attributes. Hence the update process preserves anonymity of the user. One example for a class of update functions are additive updates of integer attributes, where the issuer increments an unknown integer attribute value v by some known value k. This kind of update is motivated by an application of UACS to incentive systems. Users in an incentive system can anonymously accumulate points, e.g. in a shop at checkout, and spend them later, e.g. for a discount.}}, author = {{Blömer, Johannes and Bobolz, Jan and Diemert, Denis Pascal and Eidens, Fabian}}, booktitle = {{Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security - CCS '19}}, location = {{London}}, title = {{{Updatable Anonymous Credentials and Applications to Incentive Systems}}}, doi = {{10.1145/3319535.3354223}}, year = {{2019}}, } @inproceedings{10108, abstract = {{Recent years have seen the development of numerous tools for the analysis of taint flows in Android apps. Taint analyses aim at detecting data leaks, accidentally or by purpose programmed into apps. Often, such tools specialize in the treatment of specific features impeding precise taint analysis (like reflection or inter-app communication). This multitude of tools, their specific applicability and their various combination options complicate the selection of a tool (or multiple tools) when faced with an analysis instance, even for knowledgeable users, and hence hinders the successful adoption of taint analyses. In this work, we thus present CoDiDroid, a framework for cooperative Android app analysis. CoDiDroid (1) allows users to ask questions about flows in apps in varying degrees of detail, (2) automatically generates subtasks for answering such questions, (3) distributes tasks onto analysis tools (currently DroidRA, FlowDroid, HornDroid, IC3 and two novel tools) and (4) at the end merges tool answers on subtasks into an overall answer. Thereby, users are freed from having to learn about the use and functionality of all these tools while still being able to leverage their capabilities. Moreover, we experimentally show that cooperation among tools pays off with respect to effectiveness, precision and scalability.}}, author = {{Pauck, Felix and Wehrheim, Heike}}, booktitle = {{Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering}}, isbn = {{978-1-4503-5572-8}}, keywords = {{Android Taint Analysis, Cooperation, Precision, Tools}}, pages = {{374--384}}, title = {{{Together Strong: Cooperative Android App Analysis}}}, doi = {{10.1145/3338906.3338915}}, year = {{2019}}, } @inproceedings{13874, author = {{Isenberg, Tobias and Jakobs, Marie-Christine and Pauck, Felix and Wehrheim, Heike}}, booktitle = {{Tests and Proofs - 13th International Conference, {TAP} 2019, Held as Part of the Third World Congress on Formal Methods 2019, Porto, Portugal, October 9-11, 2019, Proceedings}}, pages = {{3--20}}, title = {{{When Are Software Verification Results Valid for Approximate Hardware?}}}, doi = {{10.1007/978-3-030-31157-5_1}}, year = {{2019}}, } @misc{39056, author = {{Lütkevedder, Dennis}}, title = {{{"Data-Driven Mergers" in digitalen Märkten - eine wettbewerbspolitische Analyse}}}, year = {{2019}}, } @misc{39059, author = {{Memon, Uzair Ahmed}}, title = {{{On the Implications of Big Data for Competition Policy - Big data, Market power, Competition law}}}, year = {{2019}}, } @misc{39067, author = {{Milczarek, André}}, title = {{{Risiken und Gefahren von Marktmacht in digitalen Märkten - eine wettbewerbspolitische Analyse}}}, year = {{2019}}, } @misc{37684, author = {{Heinrichs, Fabian}}, title = {{{Digitale Märkte - Zu den Auswirkungen von Big Data auf Marktmacht und die Bildung von Kartellen}}}, year = {{2019}}, } @misc{38042, author = {{Fortmeier, Julia}}, title = {{{Anreizwirkungen der Bonusregelung - Eine wettbewerbspolitische Analyse}}}, year = {{2019}}, } @misc{38045, author = {{Hagedorn, Carolin}}, title = {{{The intersection of privacy and competition law - Lessons from data-driven mergers}}}, year = {{2019}}, } @misc{38097, author = {{Ayyildiz, Berfin}}, title = {{{Die Akquisition von Double Click durch Google - eine wettbewerbspolitische Analyse}}}, year = {{2019}}, } @misc{38093, author = {{Shelepova, Ekaterina}}, title = {{{Merger Analysis in Data-Driven Markets - An Economic Policy Perspective}}}, year = {{2019}}, } @misc{38096, author = {{Shanmugaratnam, Suganya}}, title = {{{Marktmacht in digitalen Märkten}}}, year = {{2019}}, } @misc{38099, author = {{Faizan, Ahmed}}, title = {{{The Effectiveness of Leniency Programs and Whistleblowing in Discouraging Cartel Activities}}}, year = {{2019}}, } @inproceedings{9913, abstract = {{Reconfigurable hardware has received considerable attention as a platform that enables dynamic hardware updates and thus is able to adapt new configurations at runtime. However, due to their dynamic nature, e.g., field-programmable gate arrays (FPGA) are subject to a constant possibility of attacks, since each new configuration might be compromised. Trojans for reconfigurable hardware that evade state-of-the-art detection techniques and even formal verification, are thus a large threat to these devices. One such stealthy hardware Trojan, that is inserted and activated in two stages by compromised electronic design automation (EDA) tools, has recently been presented and shown to evade all forms of classical pre-configuration detection techniques. This paper presents a successful pre-configuration countermeasure against this ``Malicious Look-up-table (LUT)''-hardware Trojan, by employing bitstream-level Proof-Carrying Hardware (PCH). We show that the method is able to alert innocent module creators to infected EDA tools, and to prohibit malicious ones to sell infected modules to unsuspecting customers.}}, author = {{Ahmed, Qazi Arbab and Wiersema, Tobias and Platzner, Marco}}, booktitle = {{Applied Reconfigurable Computing}}, editor = {{Hochberger, Christian and Nelson, Brent and Koch, Andreas and Woods, Roger and Diniz, Pedro}}, isbn = {{978-3-030-17227-5}}, location = {{Darmstadt, Germany}}, pages = {{127--136}}, publisher = {{Springer International Publishing}}, title = {{{Proof-Carrying Hardware Versus the Stealthy Malicious LUT Hardware Trojan}}}, doi = {{10.1007/978-3-030-17227-5_10}}, volume = {{11444}}, year = {{2019}}, }