Publications

Publications:

Additional references: Google Scholar and DBLP.

2020

  • Roman Matzutt, Jan Pennekamp, and Klaus Wehrle. A Secure and Practical Decentralized Ecosystem for Shareable Education Material. In Proceedings of the 34th International Conference on Information Networking (ICOIN ’20), 01 2020.
    [BibTeX] [Abstract]
    The university landscape traditionally is highly federated, which hinders potentials for coordinated collaborations. While the lack of a strict hierarchy on the inter-university level is critical for ensuring free research and higher education, it limits the access to high-quality education materials. Especially regarding resources such as lecture notes or exercise tasks we observe a high susceptibility to redundant work and lacking quality assessment of material created in isolation by individual university institutes. To remedy this situation, in this paper we propose CORALIS, a decentralized marketplace for offering, acquiring, discussing, and improving education resources across university borders. Our design is based on a permissioned blockchain to (a) realize accountable access control via simple on-chain license terms, (b) trace the evolution of encrypted containers accumulating bundles of shareable education resources, and (c) record user comments and ratings for further improving the quality of offered education material.
    @inproceedings{MPW19,
    author = {Matzutt, Roman and Pennekamp, Jan and Wehrle, Klaus},
    title = {{A Secure and Practical Decentralized Ecosystem for Shareable Education Material}},
    booktitle = {Proceedings of the 34th International Conference on Information Networking (ICOIN '20)},
    month = {01},
    year = {2020},
    abstract = {The university landscape traditionally is highly federated, which hinders potentials for coordinated collaborations. While the lack of a strict hierarchy on the inter-university level is critical for ensuring free research and higher education, it limits the access to high-quality education materials. Especially regarding resources such as lecture notes or exercise tasks we observe a high susceptibility to redundant work and lacking quality assessment of material created in isolation by individual university institutes. To remedy this situation, in this paper we propose CORALIS, a decentralized marketplace for offering, acquiring, discussing, and improving education resources across university borders. Our design is based on a permissioned blockchain to (a) realize accountable access control via simple on-chain license terms, (b) trace the evolution of encrypted containers accumulating bundles of shareable education resources, and (c) record user comments and ratings for further improving the quality of offered education material.},
    meta = {},
    }

2019

  • Jan Pennekamp, Markus Dahlmanns, Lars Gleim, Stefan Decker, and Klaus Wehrle. Security Considerations for Collaborations in an Industrial IoT-based Lab of Labs. In Proceedings of the 3rd IEEE Global Conference on Internet of Things (GCIoT ’19), 12 2019.
    [BibTeX] [Abstract] [PDF]
    The productivity and sustainability advances for (smart) manufacturing resulting from (globally) interconnected Industrial IoT devices in a lab of labs are expected to be significant. While such visions introduce opportunities for the involved parties, the associated risks must be considered as well. In particular, security aspects are crucial challenges and remain unsolved. So far, single stakeholders only had to consider their local view on security. However, for a global lab, we identify several fundamental research challenges in (dynamic) scenarios with multiple stakeholders: While information security mandates that models must be adapted wrt. confidentiality to address these new influences on business secrets, from a network perspective, the drastically increasing amount of possible attack vectors challenges today’s approaches. Finally, concepts addressing these security challenges should provide backwards compatibility to enable a smooth transition from today’s isolated landscape towards globally interconnected IIoT environments.
    @inproceedings{PDGDW19,
    author = {Pennekamp, Jan and Dahlmanns, Markus and Gleim, Lars and Decker, Stefan and Wehrle, Klaus},
    title = {{Security Considerations for Collaborations in an Industrial IoT-based Lab of Labs}},
    booktitle = {Proceedings of the 3rd IEEE Global Conference on Internet of Things (GCIoT '19)},
    month = {12},
    year = {2019},
    abstract = {The productivity and sustainability advances for (smart) manufacturing resulting from (globally) interconnected Industrial IoT devices in a lab of labs are expected to be significant. While such visions introduce opportunities for the involved parties, the associated risks must be considered as well. In particular, security aspects are crucial challenges and remain unsolved. So far, single stakeholders only had to consider their local view on security. However, for a global lab, we identify several fundamental research challenges in (dynamic) scenarios with multiple stakeholders: While information security mandates that models must be adapted wrt. confidentiality to address these new influences on business secrets, from a network perspective, the drastically increasing amount of possible attack vectors challenges today's approaches. Finally, concepts addressing these security challenges should provide backwards compatibility to enable a smooth transition from today's isolated landscape towards globally interconnected IIoT environments.},
    meta = {},
    }
  • Wladimir De la Cadena, Asya Mitseva, Jan Pennekamp, Jens Hiller, Fabian Lanze, Thomas Engel, Klaus Wehrle, and Andriy Panchenko. POSTER: Traffic Splitting to Counter Website Fingerprinting. In Proceedings of the 26th ACM SIGSAC Conference on Computer and Communications Security (CCS ’19), 11 2019.
    [BibTeX] [Abstract] [DOI] [PDF]
    Website fingerprinting (WFP) is a special type of traffic analysis, which aims to infer the websites visited by a user. Recent studies have shown that WFP targeting Tor users is notably more effective than previously expected. Concurrently, state-of-the-art defenses have been proven to be less effective. In response, we present a novel WFP defense that splits traffic over multiple entry nodes to limit the data a single malicious entry can use. Here, we explore several traffic-splitting strategies to distribute user traffic. We establish that our \emph{weighted random} strategy dramatically reduces the accuracy from nearly 95\% to less than 35\% for \emph{four} state-of-the-art WFP attacks without adding any artificial delays or dummy traffic.
    @inproceedings{DMP+19,
    author = {De la Cadena, Wladimir and Mitseva, Asya and Pennekamp, Jan and Hiller, Jens and Lanze, Fabian and Engel, Thomas and Wehrle, Klaus and Panchenko, Andriy},
    title = {{POSTER: Traffic Splitting to Counter Website Fingerprinting}},
    booktitle = {Proceedings of the 26th ACM SIGSAC Conference on Computer and Communications Security (CCS '19)},
    month = {11},
    year = {2019},
    doi = {10.1145/3319535.3363249},
    abstract = {Website fingerprinting (WFP) is a special type of traffic analysis, which aims to infer the websites visited by a user. Recent studies have shown that WFP targeting Tor users is notably more effective than previously expected. Concurrently, state-of-the-art defenses have been proven to be less effective. In response, we present a novel WFP defense that splits traffic over multiple entry nodes to limit the data a single malicious entry can use. Here, we explore several traffic-splitting strategies to distribute user traffic. We establish that our \emph{weighted random} strategy dramatically reduces the accuracy from nearly 95\% to less than 35\% for \emph{four} state-of-the-art WFP attacks without adding any artificial delays or dummy traffic.},
    meta = {},
    }
  • Jan Pennekamp, Martin Henze, Simo Schmidt, Philipp Niemietz, Marcel Fey, Daniel Trauth, Thomas Bergs, Christian Brecher, and Klaus Wehrle. Dataflow Challenges in an Internet of Production: A Security & Privacy Perspective. In Proceedings of the 5th ACM Workshop on Cyber-Physical Systems Security and PrivaCy (CPS-SPC ’19), co-located with the 26th ACM SIGSAC Conference on Computer and Communications Security (CCS ’19), 11 2019.
    [BibTeX] [Abstract] [DOI] [PDF]
    The Internet of Production (IoP) envisions the interconnection of previously isolated CPS in the area of manufacturing across institutional boundaries to realize benefits such as increased profit margins and product quality as well as reduced product development costs and time to market. This interconnection of CPS will lead to a plethora of new dataflows, especially between (partially) distrusting entities. In this paper, we identify and illustrate these envisioned inter-organizational dataflows and the participating entities alongside two real-world use cases from the production domain: a fine blanking line and a connected job shop. Our analysis allows us to identify distinct security and privacy demands and challenges for these new dataflows. As a foundation to address the resulting requirements, we provide a survey of promising technical building blocks to secure inter-organizational dataflows in an IoP and propose next steps for future research. Consequently, we move an important step forward to overcome security and privacy concerns as an obstacle for realizing the promised potentials in an Internet of Production.
    @inproceedings{PHS+19,
    author = {Pennekamp, Jan and Henze, Martin and Schmidt, Simo and Niemietz, Philipp and Fey, Marcel and Trauth, Daniel and Bergs, Thomas and Brecher, Christian and Wehrle, Klaus},
    title = {{Dataflow Challenges in an Internet of Production: A Security & Privacy Perspective}},
    booktitle = {Proceedings of the 5th ACM Workshop on Cyber-Physical Systems Security and PrivaCy (CPS-SPC '19), co-located with the 26th ACM SIGSAC Conference on Computer and Communications Security (CCS '19)},
    month = {11},
    year = {2019},
    doi = {10.1145/3338499.3357357},
    abstract = {The Internet of Production (IoP) envisions the interconnection of previously isolated CPS in the area of manufacturing across institutional boundaries to realize benefits such as increased profit margins and product quality as well as reduced product development costs and time to market. This interconnection of CPS will lead to a plethora of new dataflows, especially between (partially) distrusting entities. In this paper, we identify and illustrate these envisioned inter-organizational dataflows and the participating entities alongside two real-world use cases from the production domain: a fine blanking line and a connected job shop.
    Our analysis allows us to identify distinct security and privacy demands and challenges for these new dataflows. As a foundation to address the resulting requirements, we provide a survey of promising technical building blocks to secure inter-organizational dataflows in an IoP and propose next steps for future research. Consequently, we move an important step forward to overcome security and privacy concerns as an obstacle for realizing the promised potentials in an Internet of Production.},
    meta = {},
    }
  • Thomas Bergs, Philipp Niemietz, Jan Pennekamp, Ike Kunze, Daniel Trauth, and Klaus Wehrle. Stamping Process Modelling in an Internet of Production. In Proceedings of the 8th CIRP International Conference on Through-Life Engineering Service (TESConf ’19), 10 2019.
    [BibTeX] [Abstract]
    Sharing data between companies throughout the supply chain is expected to be beneficial for product quality as well as for the economical savings in the manufacturing industry. To utilize the available data in the vision of an Internet of Production (IoP) a precise condition monitoring of manufacturing and production processes that facilitates the quantification of influences throughout the supply chain is inevitable. In this paper, we consider stamping processes in the context of an Internet of Production and the preliminaries for analytical models that utilize the ever-increasing available data. Three research objectives to cope with the amount of data and for a methodology to monitor, analyze and evaluate the influence of available data onto stamping processes have been identified: (i) State detection based on cyclic sensor signals, (ii) mapping of in- and output parameter variations onto process states, and (iii) models for edge and in-network computing approaches. After discussing state-of-the-art approaches to monitor stamping processes and the introduction of the fineblanking process as an exemplary stamping process, a research roadmap for an IoP enabling modeling framework is presented.
    @inproceedings{BNP+19,
    author = {Bergs, Thomas and Niemietz, Philipp and Pennekamp, Jan and Kunze, Ike and Trauth, Daniel and Wehrle, Klaus},
    title = {{Stamping Process Modelling in an Internet of Production}},
    booktitle = {Proceedings of the 8th CIRP International Conference on Through-Life Engineering Service (TESConf '19)},
    month = {10},
    year = {2019},
    abstract = {Sharing data between companies throughout the supply chain is expected to be beneficial for product quality as well as for the economical savings in the manufacturing industry. To utilize the available data in the vision of an Internet of Production (IoP) a precise condition monitoring of manufacturing and production processes that facilitates the quantification of influences throughout the supply chain is inevitable. In this paper, we consider stamping processes in the context of an Internet of Production and the preliminaries for analytical models that utilize the ever-increasing available data. Three research objectives to cope with the amount of data and for a methodology to monitor, analyze and evaluate the influence of available data onto stamping processes have been identified: (i) State detection based on cyclic sensor signals, (ii) mapping of in- and output parameter variations onto process states, and (iii) models for edge and in-network computing approaches. After discussing state-of-the-art approaches to monitor stamping processes and the introduction of the fineblanking process as an exemplary stamping process, a research roadmap for an IoP enabling modeling framework is presented.},
    meta = {},
    }
  • Jens Hiller, Jan Pennekamp, Markus Dahlmanns, Martin Henze, Andriy Panchenko, and Klaus Wehrle. Tailoring Onion Routing to the Internet of Things: Security and Privacy in Untrusted Environments. In Proceedings of the 27th IEEE International Conference on Network Protocols (ICNP ’19), 10 2019.
    [BibTeX] [Abstract] [DOI] [PDF] [CODE]
    An increasing number of IoT scenarios involve mobile, resource-constrained IoT devices that rely on untrusted networks for Internet connectivity. In such environments, attackers can derive sensitive private information of IoT device owners, e.g., daily routines or secret supply chain procedures, when sniffing on IoT communication and linking IoT devices and owner. Furthermore, untrusted networks do not provide IoT devices with any protection against attacks from the Internet. Anonymous communication using onion routing provides a well-proven mechanism to keep the relationship between communication partners secret and (optionally) protect against network attacks. However, the application of onion routing is challenged by protocol incompatibilities and demanding cryptographic processing on constrained IoT devices, rendering its use infeasible. To close this gap, we tailor onion routing to the IoT by bridging protocol incompatibilities and offloading expensive cryptographic processing to a router or web server of the IoT device owner. Thus, we realize resource-conserving access control and end-to-end security for IoT devices. To prove applicability, we deploy onion routing for the IoT within the well-established Tor network enabling IoT devices to leverage its resources to achieve the same grade of anonymity as readily available to traditional devices.
    @inproceedings{HPD+19,
    author = {Hiller, Jens and Pennekamp, Jan and Dahlmanns, Markus and Henze, Martin and Panchenko, Andriy and Wehrle, Klaus},
    title = {{Tailoring Onion Routing to the Internet of Things: Security and Privacy in Untrusted Environments}},
    booktitle = {Proceedings of the 27th IEEE International Conference on Network Protocols (ICNP '19)},
    month = {10},
    year = {2019},
    doi = {10.1109/ICNP.2019.8888033},
    code = {https://github.com/COMSYS/tor4iot-tor},
    code2 = {https://github.com/COMSYS/tor4iot-contiki},
    abstract = {An increasing number of IoT scenarios involve mobile, resource-constrained IoT devices that rely on untrusted networks for Internet connectivity. In such environments, attackers can derive sensitive private information of IoT device owners, e.g., daily routines or secret supply chain procedures, when sniffing on IoT communication and linking IoT devices and owner. Furthermore, untrusted networks do not provide IoT devices with any protection against attacks from the Internet.
    Anonymous communication using onion routing provides a well-proven mechanism to keep the relationship between communication partners secret and (optionally) protect against network attacks. However, the application of onion routing is challenged by protocol incompatibilities and demanding cryptographic processing on constrained IoT devices, rendering its use infeasible.
    To close this gap, we tailor onion routing to the IoT by bridging protocol incompatibilities and offloading expensive cryptographic processing to a router or web server of the IoT device owner. Thus, we realize resource-conserving access control and end-to-end security for IoT devices. To prove applicability, we deploy onion routing for the IoT within the well-established Tor network enabling IoT devices to leverage its resources to achieve the same grade of anonymity as readily available to traditional devices.},
    meta = {},
    }
  • Markus Dahlmanns, Chris Dax, Roman Matzutt, Jan Pennekamp, Jens Hiller, and Klaus Wehrle. Privacy-Preserving Remote Knowledge System. In Proceedings of the 27th IEEE International Conference on Network Protocols (ICNP ’19), 10 2019.
    [BibTeX] [Abstract] [DOI] [PDF]
    More and more traditional services, such as malware detectors or collaboration services in industrial scenarios, move to the cloud. However, this behavior poses a risk for the privacy of clients since these services are able to generate profiles containing very sensitive information, e.g., vulnerability information or collaboration partners. Hence, a rising need for protocols that enable clients to obtain knowledge without revealing their requests exists. To address this issue, we propose a protocol that enables clients (i) to query large cloud-based knowledge systems in a privacy-preserving manner using Private Set Intersection and (ii) to subsequently obtain individual knowledge items without leaking the client’s requests via few Oblivious Transfers. With our preliminary design, we allow clients to save a significant amount of time in comparison to performing Oblivious Transfers only.
    @inproceedings{DDM+19,
    author = {Dahlmanns, Markus and Dax, Chris and Matzutt, Roman and Pennekamp, Jan and Hiller, Jens and Wehrle, Klaus},
    title = {{Privacy-Preserving Remote Knowledge System}},
    booktitle = {Proceedings of the 27th IEEE International Conference on Network Protocols (ICNP '19)},
    month = {10},
    year = {2019},
    doi = {10.1109/ICNP.2019.8888121},
    abstract = {More and more traditional services, such as malware detectors or collaboration services in industrial scenarios, move to the cloud. However, this behavior poses a risk for the privacy of clients since these services are able to generate profiles containing very sensitive information, e.g., vulnerability information or collaboration partners. Hence, a rising need for protocols that enable clients to obtain knowledge without revealing their requests exists. To address this issue, we propose a protocol that enables clients (i) to query large cloud-based knowledge systems in a privacy-preserving manner using Private Set Intersection and (ii) to subsequently obtain individual knowledge items without leaking the client's requests via few Oblivious Transfers. With our preliminary design, we allow clients to save a significant amount of time in comparison to performing Oblivious Transfers only.},
    meta = {},
    }
  • Jan Pennekamp, Jens Hiller, Sebastian Reuter, Wladimir De la Cadena, Asya Mitseva, Martin Henze, Thomas Engel, Klaus Wehrle, and Andriy Panchenko. Multipathing Traffic to Reduce Entry Node Exposure in Onion Routing. In Proceedings of the 27th IEEE International Conference on Network Protocols (ICNP ’19), 10 2019.
    [BibTeX] [Abstract] [DOI] [PDF]
    Users of an onion routing network, such as Tor, depend on its anonymity properties. However, especially malicious entry nodes, which know the client’s identity, can also observe the whole communication on their link to the client and, thus, conduct several de-anonymization attacks. To limit this exposure and to impede corresponding attacks, we propose to multipath traffic between the client and the middle node to reduce the information an attacker can obtain at a single vantage point. To facilitate the deployment, only clients and selected middle nodes need to implement our approach, which works transparently for the remaining legacy nodes. Furthermore, we let clients control the splitting strategy to prevent any external manipulation.
    @inproceedings{PHR+19,
    author = {Pennekamp, Jan and Hiller, Jens and Reuter, Sebastian and De la Cadena, Wladimir and Mitseva, Asya and Henze, Martin and Engel, Thomas and Wehrle, Klaus and Panchenko, Andriy},
    title = {{Multipathing Traffic to Reduce Entry Node Exposure in Onion Routing}},
    booktitle = {Proceedings of the 27th IEEE International Conference on Network Protocols (ICNP '19)},
    month = {10},
    year = {2019},
    doi = {10.1109/ICNP.2019.8888029},
    abstract = {Users of an onion routing network, such as Tor, depend on its anonymity properties. However, especially malicious entry nodes, which know the client's identity, can also observe the whole communication on their link to the client and, thus, conduct several de-anonymization attacks. To limit this exposure and to impede corresponding attacks, we propose to multipath traffic between the client and the middle node to reduce the information an attacker can obtain at a single vantage point. To facilitate the deployment, only clients and selected middle nodes need to implement our approach, which works transparently for the remaining legacy nodes. Furthermore, we let clients control the splitting strategy to prevent any external manipulation.},
    meta = {},
    }
  • Jan Pennekamp, Martin Henze, Oliver Hohlfeld, and Andriy Panchenko. Hi Doppelgänger: Towards Detecting Manipulation in News Comments. In Companion Proceedings of the 2019 World Wide Web Conference (WWW ’19 Companion), 4th Workshop on Computational Methods in Online Misbehavior (CyberSafety ’19), 05 2019.
    [BibTeX] [Abstract] [DOI] [PDF]
    Public opinion manipulation is a serious threat to society, potentially influencing elections and the political situation even in established democracies. The prevalence of online media and the opportunity for users to express opinions in comments magnifies the problem. Governments, organizations, and companies can exploit this situation for biasing opinions. Typically, they deploy a large number of pseudonyms to create an impression of a crowd that supports specific opinions. Side channel information (such as IP addresses or identities of browsers) often allows a reliable detection of pseudonyms managed by a single person. However, while spoofing and anonymizing data that links these accounts is simple, a linking without is very challenging. In this paper, we evaluate whether stylometric features allow a detection of such doppelgängers within comment sections on news articles. To this end, we adapt a state-of-the-art doppelgängers detector to work on small texts (such as comments) and apply it on three popular news sites in two languages. Our results reveal that detecting potential doppelgängers based on linguistics is a promising approach even when no reliable side channel information is available. Preliminary results following an application in the wild shows indications for doppelgängers in real world data sets.
    @inproceedings{PHHP19,
    author = {Pennekamp, Jan and Henze, Martin and Hohlfeld, Oliver and Panchenko, Andriy},
    title = {{Hi Doppelgänger: Towards Detecting Manipulation in News Comments}},
    booktitle = {Companion Proceedings of the 2019 World Wide Web Conference (WWW '19 Companion), 4th Workshop on Computational Methods in Online Misbehavior (CyberSafety '19)},
    month = {05},
    year = {2019},
    doi = {10.1145/3308560.3316496},
    abstract = {Public opinion manipulation is a serious threat to society, potentially influencing elections and the political situation even in established democracies. The prevalence of online media and the opportunity for users to express opinions in comments magnifies the problem. Governments, organizations, and companies can exploit this situation for biasing opinions. Typically, they deploy a large number of pseudonyms to create an impression of a crowd that supports specific opinions. Side channel information (such as IP addresses or identities of browsers) often allows a reliable detection of pseudonyms managed by a single person. However, while spoofing and anonymizing data that links these accounts is simple, a linking without is very challenging.
    In this paper, we evaluate whether stylometric features allow a detection of such doppelgängers within comment sections on news articles. To this end, we adapt a state-of-the-art doppelgängers detector to work on small texts (such as comments) and apply it on three popular news sites in two languages. Our results reveal that detecting potential doppelgängers based on linguistics is a promising approach even when no reliable side channel information is available. Preliminary results following an application in the wild shows indications for doppelgängers in real world data sets.},
    meta = {},
    }
  • Jan Pennekamp, René Glebke, Martin Henze, Tobias Meisen, Christoph Quix, Rihan Hai, Lars Gleim, Philipp Niemietz, Maximilian Rudack, Simon Knape, Alexander Epple, Daniel Trauth, Uwe Vroomen, Thomas Bergs, Christian Brecher, Andreas Bührig-Polaczek, Matthias Jarke, and Klaus Wehrle. Towards an Infrastructure Enabling the Internet of Production. In Proceedings of the 2nd IEEE International Conference on Industrial Cyber-Physical Systems (ICPS ’19), 05 2019.
    [BibTeX] [Abstract] [DOI] [PDF]
    New levels of cross-domain collaboration between manufacturing companies throughout the supply chain are anticipated to bring benefits to both suppliers and consumers of products. Enabling a fine-grained sharing and analysis of data among different stakeholders in an automated manner, such a vision of an Internet of Production (IoP) introduces demanding challenges to the communication, storage, and computation infrastructure in production environments. In this work, we present three example cases that would benefit from an IoP (a fine blanking line, a high pressure die casting process, and a connected job shop) and derive requirements that cannot be met by today’s infrastructure. In particular, we identify three orthogonal research objectives: (i) real-time control of tightly integrated production processes to offer seamless low-latency analysis and execution, (ii) storing and processing heterogeneous production data to support scalable data stream processing and storage, and (iii) secure privacy-aware collaboration in production to provide a basis for secure industrial collaboration. Based on a discussion of state-of-the-art approaches for these three objectives, we create a blueprint for an infrastructure acting as an enabler for an IoP.
    @inproceedings{PGH+19,
    author = {Pennekamp, Jan and Glebke, Ren{\'e} and Henze, Martin and Meisen, Tobias and Quix, Christoph and Hai, Rihan and Gleim, Lars and Niemietz, Philipp and Rudack, Maximilian and Knape, Simon and Epple, Alexander and Trauth, Daniel and Vroomen, Uwe and Bergs, Thomas and Brecher, Christian and B{\"u}hrig-Polaczek, Andreas and Jarke, Matthias and Wehrle, Klaus},
    title = {{Towards an Infrastructure Enabling the Internet of Production}},
    booktitle = {Proceedings of the 2nd IEEE International Conference on Industrial Cyber-Physical Systems (ICPS '19)},
    month = {05},
    year = {2019},
    doi = {10.1109/ICPHYS.2019.8780276},
    abstract = {New levels of cross-domain collaboration between manufacturing companies throughout the supply chain are anticipated to bring benefits to both suppliers and consumers of products. Enabling a fine-grained sharing and analysis of data among different stakeholders in an automated manner, such a vision of an Internet of Production (IoP) introduces demanding challenges to the communication, storage, and computation infrastructure in production environments. In this work, we present three example cases that would benefit from an IoP (a fine blanking line, a high pressure die casting process, and a connected job shop) and derive requirements that cannot be met by today’s infrastructure. In particular, we identify three orthogonal research objectives: (i) real-time control of tightly integrated production processes to offer seamless low-latency analysis and execution, (ii) storing and processing heterogeneous production data to support scalable data stream processing and storage, and (iii) secure privacy-aware collaboration in production to provide a basis for secure industrial collaboration. Based on a discussion of state-of-the-art approaches for these three objectives, we create a blueprint for an infrastructure acting as an enabler for an IoP.},
    meta = {},
    }

2017

  • Jan Pennekamp, Martin Henze, and Klaus Wehrle. A Survey on the Evolution of Privacy Enforcement on Smartphones and the Road Ahead. Pervasive and Mobile Computing, 42, 12 2017.
    [BibTeX] [Abstract] [DOI] [PDF]
    With the increasing proliferation of smartphones, enforcing privacy of smartphone users becomes evermore important. Nowadays, one of the major privacy challenges is the tremendous amount of permissions requested by applications, which can significantly invade users’ privacy, often without their knowledge. In this paper, we provide a comprehensive review of approaches that can be used to report on applications’ permission usage, tune permission access, contain sensitive information, and nudge users towards more privacy-conscious behavior. We discuss key shortcomings of privacy enforcement on smartphones so far and identify suitable actions for the future.
    @article{PHW17,
    author = {Pennekamp, Jan and Henze, Martin and Wehrle, Klaus},
    title = {{A Survey on the Evolution of Privacy Enforcement on Smartphones and the Road Ahead}},
    journal = {Pervasive and Mobile Computing},
    volume = {42},
    month = {12},
    year = {2017},
    doi = {10.1016/j.pmcj.2017.09.005},
    abstract = {With the increasing proliferation of smartphones, enforcing privacy of smartphone users becomes evermore important. Nowadays, one of the major privacy challenges is the tremendous amount of permissions requested by applications, which can significantly invade users' privacy, often without their knowledge. In this paper, we provide a comprehensive review of approaches that can be used to report on applications' permission usage, tune permission access, contain sensitive information, and nudge users towards more privacy-conscious behavior. We discuss key shortcomings of privacy enforcement on smartphones so far and identify suitable actions for the future.},
    meta = {},
    }
  • Martin Henze, Jan Pennekamp, David Hellmanns, Erik Mühmer, Jan Henrik Ziegeldorf, Arthur Drichel, and Klaus Wehrle. CloudAnalyzer: Uncovering the Cloud Usage of Mobile Apps. In Proceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous), 11 2017.
    [BibTeX] [Abstract] [DOI] [PDF] [CODE]
    Developers of smartphone apps increasingly rely on cloud services for ready-made functionalities, e.g., to track app usage, to store data, or to integrate social networks. At the same time, mobile apps have access to various private information, ranging from users’ contact lists to their precise locations. As a result, app deployment models and data flows have become too complex and entangled for users to understand. We present CloudAnalyzer, a transparency technology that reveals the cloud usage of smartphone apps and hence provides users with the means to reclaim informational self-determination. We apply CloudAnalyzer to study the cloud exposure of 29 volunteers over the course of 19 days. In addition, we analyze the cloud usage of the 5000 most accessed mobile websites as well as 500 popular apps from five different countries. Our results reveal an excessive exposure to cloud services: 90 % of apps use cloud services and 36 % of apps used by volunteers solely communicate with cloud services. Given the information provided by CloudAnalyzer, users can critically review the cloud usage of their apps.
    @inproceedings{HPH+17,
    author = {Henze, Martin and Pennekamp, Jan and Hellmanns, David and M{\"u}hmer, Erik and Ziegeldorf, Jan Henrik and Drichel, Arthur and Wehrle, Klaus},
    title = {{CloudAnalyzer: Uncovering the Cloud Usage of Mobile Apps}},
    booktitle = {Proceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous)},
    month = {11},
    year = {2017},
    doi = {10.1145/3144457.3144471},
    code = {https://github.com/COMSYS/CloudAnalyzer},
    abstract = {Developers of smartphone apps increasingly rely on cloud services for ready-made functionalities, e.g., to track app usage, to store data, or to integrate social networks. At the same time, mobile apps have access to various private information, ranging from users' contact lists to their precise locations. As a result, app deployment models and data flows have become too complex and entangled for users to understand. We present CloudAnalyzer, a transparency technology that reveals the cloud usage of smartphone apps and hence provides users with the means to reclaim informational self-determination. We apply CloudAnalyzer to study the cloud exposure of 29 volunteers over the course of 19 days. In addition, we analyze the cloud usage of the 5000 most accessed mobile websites as well as 500 popular apps from five different countries. Our results reveal an excessive exposure to cloud services: 90 % of apps use cloud services and 36 % of apps used by volunteers solely communicate with cloud services. Given the information provided by CloudAnalyzer, users can critically review the cloud usage of their apps.},
    meta = {},
    }
  • Jan Henrik Ziegeldorf, Jan Pennekamp, David Hellmanns, Felix Schwinger, Ike Kunze, Martin Henze, Jens Hiller, Roman Matzutt, and Klaus Wehrle. BLOOM: BLoom filter based Oblivious Outsourced Matchings. BMC Medical Genomics, 10(Suppl 2), 07 2017. Proceedings of the 5th iDASH Privacy and Security Workshop 2016
    [BibTeX] [Abstract] [DOI] [PDF] [CODE]
    Whole genome sequencing has become fast, accurate, and cheap, paving the way towards the large-scale collection and processing of human genome data. Unfortunately, this dawning genome era does not only promise tremendous advances in biomedical research but also causes unprecedented privacy risks for the many. Handling storage and processing of large genome datasets through cloud services greatly aggravates these concerns. Current research efforts thus investigate the use of strong cryptographic methods and protocols to implement privacy-preserving genomic computations. We propose FHE-Bloom and PHE-Bloom, two efficient approaches for genetic disease testing using homomorphically encrypted Bloom filters. Both approaches allow the data owner to securely outsource storage and computation to an untrusted cloud. FHE-Bloom is fully secure in the semi-honest model while PHE-Bloom slightly relaxes security guarantees in a trade-off for highly improved performance. We implement and evaluate both approaches on a large dataset of up to 50 patient genomes each with up to 1000000 variations (single nucleotide polymorphisms). For both implementations, overheads scale linearly in the number of patients and variations, while PHE-Bloom is faster by at least three orders of magnitude. For example, testing disease susceptibility of 50 patients with 100000 variations requires only a total of 308.31 s (σ=8.73 s) with our first approach and a mere 0.07 s (σ=0.00 s) with the second. We additionally discuss security guarantees of both approaches and their limitations as well as possible extensions towards more complex query types, e.g., fuzzy or range queries. Both approaches handle practical problem sizes efficiently and are easily parallelized to scale with the elastic resources available in the cloud. The fully homomorphic scheme, FHE-Bloom, realizes a comprehensive outsourcing to the cloud, while the partially homomorphic scheme, PHE-Bloom, trades a slight relaxation of security guarantees against performance improvements by at least three orders of magnitude.
    @article{ZPH+17,
    author = {Ziegeldorf, Jan Henrik and Pennekamp, Jan and Hellmanns, David and Schwinger, Felix and Kunze, Ike and Henze, Martin and Hiller, Jens and Matzutt, Roman and Wehrle, Klaus},
    title = {{BLOOM: BLoom filter based Oblivious Outsourced Matchings}},
    journal = {BMC Medical Genomics},
    note = {Proceedings of the 5th iDASH Privacy and Security Workshop 2016},
    volume = {10},
    number = {Suppl 2},
    month = {07},
    year = {2017},
    doi = {10.1186/s12920-017-0277-y},
    code = {https://github.com/COMSYS/bloom},
    abstract = {Whole genome sequencing has become fast, accurate, and cheap, paving the way towards the large-scale collection and processing of human genome data. Unfortunately, this dawning genome era does not only promise tremendous advances in biomedical research but also causes unprecedented privacy risks for the many. Handling storage and processing of large genome datasets through cloud services greatly aggravates these concerns. Current research efforts thus investigate the use of strong cryptographic methods and protocols to implement privacy-preserving genomic computations.
    We propose FHE-Bloom and PHE-Bloom, two efficient approaches for genetic disease testing using homomorphically encrypted Bloom filters. Both approaches allow the data owner to securely outsource storage and computation to an untrusted cloud. FHE-Bloom is fully secure in the semi-honest model while PHE-Bloom slightly relaxes security guarantees in a trade-off for highly improved performance.
    We implement and evaluate both approaches on a large dataset of up to 50 patient genomes each with up to 1000000 variations (single nucleotide polymorphisms). For both implementations, overheads scale linearly in the number of patients and variations, while PHE-Bloom is faster by at least three orders of magnitude. For example, testing disease susceptibility of 50 patients with 100000 variations requires only a total of 308.31 s (σ=8.73 s) with our first approach and a mere 0.07 s (σ=0.00 s) with the second. We additionally discuss security guarantees of both approaches and their limitations as well as possible extensions towards more complex query types, e.g., fuzzy or range queries.
    Both approaches handle practical problem sizes efficiently and are easily parallelized to scale with the elastic resources available in the cloud. The fully homomorphic scheme, FHE-Bloom, realizes a comprehensive outsourcing to the cloud, while the partially homomorphic scheme, PHE-Bloom, trades a slight relaxation of security guarantees against performance improvements by at least three orders of magnitude.},
    meta = {},
    }

2016

  • Andriy Panchenko, Fabian Lanze, Andreas Zinnen, Martin Henze, Jan Pennekamp, Klaus Wehrle, and Thomas Engel. Website Fingerprinting at Internet Scale. In Proceedings of the 23rd Annual Network and Distributed System Security Symposium (NDSS ’16), 02 2016.
    [BibTeX] [Abstract] [DOI] [PDF] [CODE]
    The website fingerprinting attack aims to identify the content (i.e., a webpage accessed by a client) of encrypted and anonymized connections by observing patterns of data flows such as packet size and direction. This attack can be performed by a local passive eavesdropper – one of the weakest adversaries in the attacker model of anonymization networks such as Tor. In this paper, we present a novel website fingerprinting attack. Based on a simple and comprehensible idea, our approach outperforms all state-of-the-art methods in terms of classification accuracy while being computationally dramatically more efficient. In order to evaluate the severity of the website fingerprinting attack in reality, we collected the most representative dataset that has ever been built, where we avoid simplified assumptions made in the related work regarding selection and type of webpages and the size of the universe. Using this data, we explore the practical limits of website fingerprinting at Internet scale. Although our novel approach is by orders of magnitude computationally more efficient and superior in terms of detection accuracy, for the first time we show that no existing method – including our own – scales when applied in realistic settings. With our analysis, we explore neglected aspects of the attack and investigate the realistic probability of success for different strategies a real-world adversary may follow.
    @inproceedings{PLZ+16,
    author = {Panchenko, Andriy and Lanze, Fabian and Zinnen, Andreas and Henze, Martin and Pennekamp, Jan and Wehrle, Klaus and Engel, Thomas},
    title = {{Website Fingerprinting at Internet Scale}},
    booktitle = {Proceedings of the 23rd Annual Network and Distributed System Security Symposium (NDSS '16)},
    month = {02},
    year = {2016},
    doi = {10.14722/ndss.2016.23477},
    code = {https://www.informatik.tu-cottbus.de/~andriy/zwiebelfreunde/},
    abstract = {The website fingerprinting attack aims to identify the content (i.e., a webpage accessed by a client) of encrypted and anonymized connections by observing patterns of data flows such as packet size and direction. This attack can be performed by a local passive eavesdropper - one of the weakest adversaries in the attacker model of anonymization networks such as Tor.
    In this paper, we present a novel website fingerprinting attack. Based on a simple and comprehensible idea, our approach outperforms all state-of-the-art methods in terms of classification accuracy while being computationally dramatically more efficient. In order to evaluate the severity of the website fingerprinting attack in reality, we collected the most representative dataset that has ever been built, where we avoid simplified assumptions made in the related work regarding selection and type of webpages and the size of the universe. Using this data, we explore the practical limits of website fingerprinting at Internet scale. Although our novel approach is by orders of magnitude computationally more efficient and superior in terms of detection accuracy, for the first time we show that no existing method - including our own - scales when applied in realistic settings. With our analysis, we explore neglected aspects of the attack and investigate the realistic probability of success for different strategies a real-world adversary may follow.},
    meta = {},
    }

University paper:

  • Master Thesis: Uncovering Doppelgängers in Online Communities
    Advised by Dr. Andriy Panchenko1 (SecanLab, University of Luxembourg) & Dr. Oliver Hohlfeld2 (COMSYS, RWTH Aachen University)
  • Seminar: Challenges for Privacy Enforcing on Smartphones *2nd best paper* (COMSYS, RWTH Aachen University)
    [Journal-Submission]
  • Seminar: MOOCs and Authentication *Best Paper Award* (School of Science, Aalto University)
    [Proceedings]
  • Bachelor Thesis: Evaluating Website Fingerprinting Attacks in Real-World Settings
    Advised by Dr. Andriy Panchenko1 (SecanLab, University of Luxembourg) & Martin Henze3 (COMSYS, RWTH Aachen University)

Code collaboration:

  • CloudAnalyzer (COMSYS, RWTH Aachen University)
    [APK] [Code]
    Among others used in:
    – “CloudAnalyzer: Uncovering the Cloud Usage of Mobile Apps” Proceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous 2017)
    – “Privacy-preserving Comparison of Cloud Exposure Induced by Mobile Apps” Proceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous 2017)
  • MailAnalyzer (COMSYS, RWTH Aachen University)
    [Code]
    Used in “Veiled in Clouds? Assessing the Prevalence of Cloud Computing in the Email Landscape”
    Proceedings of the 2017 Network Traffic Measurement and Analysis Conference (TMA 2017)
  • Secure Genome Outsourcing (COMSYS, RWTH Aachen University)
    [Code]
    Used in “BLOOM: BLoom-filter-based Oblivious Outsourced Matchings”
    BMC Medical Genomics 2017, Volume 10, July 2017, Issue 2 Supplement.
  • Website Fingerprinting Toolkit (SecanLab, University of Luxembourg)
    [Code] Only a subset, the CUMUL classifier is publicly available.
    Among others used in:
    – “Analysis of Fingerprinting Techniques for Tor Hidden Services” Proceedings of the 16th Workshop on Privacy in the Electronic Society (WPES 2017)
    – “POSTER: Fingerprinting Tor Hidden Services” Proceedings of the 23rd ACM Conference on Computer and Communications Security (CCS 2016)
  • TBA

1. [Now full professor for IT Security at Brandenburg University of Technology]
2. [Now full professor for Computer Networks and Communication Systems at Brandenburg University of Technology]
3. [Now postdoctoral researcher at Cyber ​​Analysis & Defense, Fraunhofer FKIE]