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2. On Achieving Privacy-Preserving State-of-the-Art Edge Intelligence

Author

Chabal, Daphnee, Sapra, Dolly, and Mann, Zoltán Ádám

Subjects
Computer Science - Cryptography and Security, Computer Science - Artificial Intelligence, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Deep Neural Network (DNN) Inference in Edge Computing, often called Edge Intelligence, requires solutions to insure that sensitive data confidentiality and intellectual property are not revealed in the process. Privacy-preserving Edge Intelligence is only emerging, despite the growing prevalence of Edge Computing as a context of Machine-Learning-as-a-Service. Solutions are yet to be applied, and possibly adapted, to state-of-the-art DNNs. This position paper provides an original assessment of the compatibility of existing techniques for privacy-preserving DNN Inference with the characteristics of an Edge Computing setup, highlighting the appropriateness of secret sharing in this context. We then address the future role of model compression methods in the research towards secret sharing on DNNs with state-of-the-art performance.

Published
2023

3. Predicting the Execution Time of Secure Neural Network Inference

Author

Zhang, Eloise, Mann, Zoltán Ádám, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Carette, Jacques, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Stettner, Lukasz, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Rettberg, Achim, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Pitropakis, Nikolaos, editor, Katsikas, Sokratis, editor, and Markantonakis, Konstantinos, editor

Published
2024
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5. Solutions to Data Protection Challenges in Distributed Ledger and Blockchain Technologies: A Combined Legal and Technical Approach

Author

Povše, Danaja Fabčič, Favenza, Alfredo, Frey, Davide, Mann, Zoltán Ádám, Palomares, Angel, Piatti, Lorenzo, Schroers, Jessica, Celebi, Emre, Series Editor, Chen, Jingdong, Series Editor, Gopi, E. S., Series Editor, Neustein, Amy, Series Editor, Liotta, Antonio, Series Editor, Di Mauro, Mario, Series Editor, El Madhoun, Nour, editor, Dionysiou, Ioanna, editor, and Bertin, Emmanuel, editor

Published
2024
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6. Data Protection Challenges in Distributed Ledger and Blockchain Technologies: A Combined Legal and Technical Analysis

Author

Povše, Danaja Fabčič, Favenza, Alfredo, Frey, Davide, Mann, Zoltán Ádám, Palomares, Angel, Piatti, Lorenzo, Schroers, Jessica, Celebi, Emre, Series Editor, Chen, Jingdong, Series Editor, Gopi, E. S., Series Editor, Neustein, Amy, Series Editor, Liotta, Antonio, Series Editor, Di Mauro, Mario, Series Editor, El Madhoun, Nour, editor, Dionysiou, Ioanna, editor, and Bertin, Emmanuel, editor

Published
2024
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7. UMLsec4Edge: Extending UMLsec to model data-protection-compliant edge computing systems

Author

Smolka, Sven, Laufer, Jan, Mann, Zoltán Ádám, and Pohl, Klaus

Subjects
Computer Science - Software Engineering
Abstract

Edge computing enables the processing of data - frequently personal data - at the edge of the network. For personal data, legislation such as the European General Data Protection Regulation requires data protection by design. Hence, data protection has to be accounted for in the design of edge computing systems whenever personal data is involved. This leads to specific requirements for modeling the architecture of edge computing systems, e.g., representation of data and network properties. To the best of our knowledge, no existing modeling language fulfils all these requirements. In our previous work we showed that the commonly used UML profile UMLsec fulfils some of these requirements, and can thus serve as a starting point. The aim of this paper is to create a modeling language which meets all requirements concerning the design of the architecture of edge computing systems accounting for data protection. Thus, we extend UMLsec to satisfy all requirements. We call the resulting UML profile UMLsec4Edge. We follow a systematic approach to develop UMLsec4Edge. We apply UMLsec4Edge to real-world use cases from different domains, and create appropriate deployment diagrams and class diagrams. These diagrams show UMLsec4Edge is capable of meeting the requirements., Comment: 8 pages, 4 figures, 2 tables, published at the 48th Euromicro Conference Series on Software Engineering and Advanced Applications (SEAA)

Published
2022

8. Feature-Model-Guided Online Learning for Self-Adaptive Systems

Author

Metzger, Andreas, Quinton, Clément, Mann, Zoltán Ádám, Baresi, Luciano, and Pohl, Klaus

Subjects
Computer Science - Software Engineering, Computer Science - Machine Learning
Abstract

A self-adaptive system can modify its own structure and behavior at runtime based on its perception of the environment, of itself and of its requirements. To develop a self-adaptive system, software developers codify knowledge about the system and its environment, as well as how adaptation actions impact on the system. However, the codified knowledge may be insufficient due to design time uncertainty, and thus a self-adaptive system may execute adaptation actions that do not have the desired effect. Online learning is an emerging approach to address design time uncertainty by employing machine learning at runtime. Online learning accumulates knowledge at runtime by, for instance, exploring not-yet executed adaptation actions. We address two specific problems with respect to online learning for self-adaptive systems. First, the number of possible adaptation actions can be very large. Existing online learning techniques randomly explore the possible adaptation actions, but this can lead to slow convergence of the learning process. Second, the possible adaptation actions can change as a result of system evolution. Existing online learning techniques are unaware of these changes and thus do not explore new adaptation actions, but explore adaptation actions that are no longer valid. We propose using feature models to give structure to the set of adaptation actions and thereby guide the exploration process during online learning. Experimental results involving four real-world systems suggest that considering the hierarchical structure of feature models may speed up convergence by 7.2% on average. Considering the differences between feature models before and after an evolution step may speed up convergence by 64.6% on average. [...]

Published
2019
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10. Security- and Privacy-Aware IoT Application Placement and User Assignment

Author

Mann, Zoltán Ádám, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Katsikas, Sokratis, editor, Lambrinoudakis, Costas, editor, Cuppens, Nora, editor, Mylopoulos, John, editor, Kalloniatis, Christos, editor, Meng, Weizhi, editor, Furnell, Steven, editor, Pallas, Frank, editor, Pohle, Jörg, editor, Sasse, M. Angela, editor, Abie, Habtamu, editor, Ranise, Silvio, editor, Verderame, Luca, editor, Cambiaso, Enrico, editor, Maestre Vidal, Jorge, editor, and Sotelo Monge, Marco Antonio, editor

Published
2022
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12. JASPER: Joint Optimization of Scaling, Placement, and Routing of Virtual Network Services

Author

Dräxler, Sevil, Karl, Holger, and Mann, Zoltán Ádám

Subjects
Computer Science - Networking and Internet Architecture
Abstract

To adapt to continuously changing workloads in networks, components of the running network services may need to be replicated (scaling the network service) and allocated to physical resources (placement) dynamically, also necessitating dynamic re-routing of flows between service components. In this paper, we propose JASPER, a fully automated approach to jointly optimizing scaling, placement, and routing for complex network services, consisting of multiple (virtualized) components. JASPER handles multiple network services that share the same substrate network; services can be dynamically added or removed and dynamic workload changes are handled. Our approach lets service designers specify their services on a high level of abstraction using service templates. From the service templates and a description of the substrate network, JASPER automatically makes scaling, placement and routing decisions, enabling quick reaction to changes. We formalize the problem, analyze its complexity, and develop two algorithms to solve it. Extensive empirical results show the applicability and effectiveness of the proposed approach.

Published
2017
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13. FogProtect: Protecting Sensitive Data in the Computing Continuum

Author

Ayed, Dhouha, Jaho, Eva, Lachner, Clemens, Mann, Zoltán Ádám, Seidl, Robert, Surridge, Mike, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Zirpins, Christian, editor, Paraskakis, Iraklis, editor, Andrikopoulos, Vasilios, editor, Kratzke, Nane, editor, Pahl, Claus, editor, El Ioini, Nabil, editor, Andreou, Andreas S., editor, Feuerlicht, George, editor, Lamersdorf, Winfried, editor, Ortiz, Guadalupe, editor, Van den Heuvel, Willem-Jan, editor, Soldani, Jacopo, editor, Villari, Massimo, editor, Casale, Giuliano, editor, and Plebani, Pierluigi, editor

Published
2021
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14. Feature Model-Guided Online Reinforcement Learning for Self-Adaptive Services

Author

Metzger, Andreas, Quinton, Clément, Mann, Zoltán Ádám, Baresi, Luciano, Pohl, Klaus, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Kafeza, Eleanna, editor, Benatallah, Boualem, editor, Martinelli, Fabio, editor, Hacid, Hakim, editor, Bouguettaya, Athman, editor, and Motahari, Hamid, editor

Published
2020
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15. Towards Understanding Adaptation Latency in Self-adaptive Systems

Author

Keller, Claas, Mann, Zoltán Ádám, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yangui, Sami, editor, Bouguettaya, Athman, editor, Xue, Xiao, editor, Faci, Noura, editor, Gaaloul, Walid, editor, Yu, Qi, editor, Zhou, Zhangbing, editor, Hernandez, Nathalie, editor, and Nakagawa, Elisa Y., editor

Published
2020
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17. Secure Data Processing in the Cloud

Author

Mann, Zoltán Ádám, Salant, Eliot, Surridge, Mike, Ayed, Dhouha, Boyle, John, Heisel, Maritta, Metzger, Andreas, Mundt, Paul, Barbosa, Simone Diniz Junqueira, Series Editor, Chen, Phoebe, Series Editor, Filipe, Joaquim, Series Editor, Kotenko, Igor, Series Editor, Sivalingam, Krishna M., Series Editor, Washio, Takashi, Series Editor, Yuan, Junsong, Series Editor, Zhou, Lizhu, Series Editor, Mann, Zoltán Ádám, editor, and Stolz, Volker, editor

Published
2018
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18. Optimized Application Deployment in the Fog

Author

Mann, Zoltán Ádám, Metzger, Andreas, Prade, Johannes, Seidl, Robert, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yangui, Sami, editor, Bouassida Rodriguez, Ismael, editor, Drira, Khalil, editor, and Tari, Zahir, editor

Published
2019
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21. Cloud Topology and Orchestration Using TOSCA: A Systematic Literature Review

Author

Bellendorf, Julian, Mann, Zoltán Ádám, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Kritikos, Kyriakos, editor, Plebani, Pierluigi, editor, and de Paoli, Flavio, editor

Published
2018
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22. Using Risk Patterns to Identify Violations of Data Protection Policies in Cloud Systems

Author

Schoenen, Stefan, Mann, Zoltán Ádám, Metzger, Andreas, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Braubach, Lars, editor, Murillo, Juan M., editor, Kaviani, Nima, editor, Lama, Manuel, editor, Burgueño, Loli, editor, Moha, Naouel, editor, and Oriol, Marc, editor

Published
2018
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23. Modeling Data Protection Vulnerabilities of Cloud Systems Using Risk Patterns

Author

Palm, Alexander, Mann, Zoltán Ádám, Metzger, Andreas, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Khendek, Ferhat, editor, and Gotzhein, Reinhard, editor

Published
2018
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27. Two Are Better Than One: An Algorithm Portfolio Approach to Cloud Resource Management

Author

Mann, Zoltán Ádám, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, De Paoli, Flavio, editor, Schulte, Stefan, editor, and Broch Johnsen, Einar, editor

Published
2017
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29. Interplay of Virtual Machine Selection and Virtual Machine Placement

Author

Mann, Zoltán Ádám, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Aiello, Marco, editor, Johnsen, Einar Broch, editor, Dustdar, Schahram, editor, and Georgievski, Ilche, editor

Published
2016
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35. Accelerating backtrack search with a best-first-search strategy

Author

Mann Zoltán Ádám and Szép Tamás

Subjects
best-first search, backtrack, branch-and-bound, constraint satisfaction problem, frequent restarting, Mathematics, QA1-939, Electronic computers. Computer science, QA75.5-76.95
Abstract

Backtrack-style exhaustive search algorithms for NP-hard problems tend to have large variance in their runtime. This is because “fortunate” branching decisions can lead to finding a solution quickly, whereas “unfortunate” decisions in another run can lead the algorithm to a region of the search space with no solutions. In the literature, frequent restarting has been suggested as a means to overcome this problem. In this paper, we propose a more sophisticated approach: a best-firstsearch heuristic to quickly move between parts of the search space, always concentrating on the most promising region. We describe how this idea can be efficiently incorporated into a backtrack search algorithm, without sacrificing optimality. Moreover, we demonstrate empirically that, for hard solvable problem instances, the new approach provides significantly higher speed-up than frequent restarting.

Published
2014
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38. Component-Based Hardware-Software Co-design

Author

Arató, Péter, Mann, Zoltán Ádám, Orbán, András, Goos, Gerhard, editor, Hartmanis, Juris, editor, van Leeuwen, Jan, editor, Müller-Schloer, Christian, editor, Ungerer, Theo, editor, and Bauer, Bernhard, editor

Published
2004
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45. Optimized Application Deployment using Fog and Cloud Computing Environments

Author

Mann, Zoltán Ádám, Metzger, Andreas, Prade, Johannes, and Seidl, Robert

Subjects
Cost-optimized Software Deployment, Resource Optimization, Fog Computing
Abstract

Fog computing uses geographically distributed fog nodes that can supply nearby end devices with low-latency access to cloud-like compute resources. If the load of a fog node exceeds its capacity, some non-latency-critical application components may be offloaded to the cloud. Using commercial cloud offerings for such offloading incurs financial costs. Optimally deciding which application components to keep in the fog node and which ones to offload to the cloud is a difficult combinatorial problem. We introduce an optimization algorithm that (i) guarantees that the deployment always satisfies capacity constraints and affinity requirements, (ii) achieves near-optimal cloud usage costs, and (iii) is fast enough to be run online. The practical use of the algorithm is illustrated by applying it to optimizing the applications in a mobile factory.

Published
2020
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