Martin Henze | RWTH Aachen University (original) (raw)

Papers by Martin Henze

Network-based deployments within the Internet of Things increasingly rely on the cloud-controlled... more Network-based deployments within the Internet of Things increasingly rely on the cloud-controlled federation of individual networks to configure, authorize, and manage devices across network borders. While this approach allows the convenient and reliable interconnection of networks, it raises severe security and safety concerns. These concerns range from a curious cloud provider accessing confidential data to a malicious cloud provider being able to physically control safety-critical devices. To overcome these concerns, we present D-CAM, which enables secure and distributed configuration, authorization, and management across network borders in the cloud-based Internet of Things. With D-CAM, we constrain the cloud to act as highly available and scalable storage for control messages. Consequently, we achieve reliable network control across network borders and strong security guarantees. Our evaluation confirms that D-CAM adds only a modest overhead and can scale to large networks.

Proceedings of the 2016 ACM on Workshop on Privacy in the Electronic Society - WPES'16, 2016

ABSTRACT Bit errors regularly occur in wireless communications. While many media streaming codecs... more ABSTRACT Bit errors regularly occur in wireless communications. While many media streaming codecs in principle provide bit error tolerance and resilience, packet-based communication typically drops packets that are not transmitted perfectly. We present PICCETT , a method to heuristically identify which connections corrupted packets belong to, and to assign them to the correct applications instead of dropping them. PICCETT is a receiver-side classifier that requires no support from the sender or network, and no information which communication protocols are used. We show that PICCETT can assign virtually all packets to the correct connections at bit error rates up to 7–10%, and prevents misassignments even during error bursts. PICCETT's classification algorithm needs no prior offline training and both trains and classifies fast enough to easily keep up with IEEE 802.11 communication speeds.

2015 IEEE Security and Privacy Workshops, 2015

ABSTRACT Secure Two-Party Computation (STC), despite being a powerful tool for privacy engineers,... more ABSTRACT Secure Two-Party Computation (STC), despite being a powerful tool for privacy engineers, is rarely used practically due to two reasons: i) STCs incur significant overheads and ii) developing efficient STCs requires expert knowledge. Recent works propose a variety of frameworks that address these problems. However, the varying assumptions, scenarios, and benchmarks in these works render results incomparable. It is thus hard, if not impossible, for an inexperienced developer of STCs to choose the best framework for her task. In this paper, we present a thorough quantitative performance analysis of recent STC frameworks. Our results reveal significant performance differences and we identify potential for optimizations as well as new research directions for STC. Complemented by a qualitative discussion of the frameworks' usability, our results provide privacy engineers with a dependable information basis to take the decision for the right STC framework fitting their application.

4th IEEE International Conference on Cloud Computing Technology and Science Proceedings, 2012

Ubiquitous sensing environments such as sensor networks collect large amounts of data. This data ... more Ubiquitous sensing environments such as sensor networks collect large amounts of data. This data volume is destined to grow even further with the vision of the Internet of Things. Cloud computing promises to elastically store and process such sensor data. As an additional benefit, storage and processing in the Cloud enables the efficient aggregation and analysis of information from different data sources. However, sensor data often contains privacy-relevant or otherwise sensitive information. For current Cloud platforms, the data owner looses control over her data once it enters the Cloud. This imposes adoption barriers due to legal or privacy concerns. Hence, a Cloud design is required that the data owner can trust to handle her sensitive data securely. In this paper, we analyze and define properties that a trusted Cloud design has to fulfill. Based on this analysis, we present the security architecture of SensorCloud. Our proposed security architecture enforces end-to-end data access control by the data owner reaching from the sensor network to the Cloud storage and processing subsystems as well as strict isolation up to the service-level. We evaluate the validity and feasibility of our Cloud design with an analysis of our early prototype. Our results show that our proposed security architecture is a promising extension of today's Cloud offers.

2013 IEEE Security and Privacy Workshops, 2013

ABSTRACT Nowadays, an ever-increasing number of service providers takes advantage of the cloud co... more ABSTRACT Nowadays, an ever-increasing number of service providers takes advantage of the cloud computing paradigm in order to efficiently offer services to private users, businesses, and governments. However, while cloud computing allows to transparently scale back-end functionality such as computing and storage, the implied distributed sharing of resources has severe implications when sensitive or otherwise privacy-relevant data is concerned. These privacy implications primarily stem from the in-transparency of the involved backend providers of a cloud-based service and their dedicated data handling processes. Likewise, back-end providers cannot determine the sensitivity of data that is stored or processed in the cloud. Hence, they have no means to obey the underlying privacy regulations and contracts automatically. As the cloud computing paradigm further evolves towards federated cloud environments, the envisioned integration of different cloud platforms adds yet another layer to the existing in-transparencies. In this paper, we discuss initial ideas on how to overcome these existing and dawning data handling in-transparencies and the accompanying privacy concerns. To this end, we propose to annotate data with sensitivity information as it leaves the control boundaries of the data owner and travels through to the cloud environment. This allows to signal privacy properties across the layers of the cloud computing architecture and enables the different stakeholders to react accordingly.

2014 IEEE Symposium on Computers and Communications (ISCC), 2014

Procedia Computer Science, 2014

As sensor networks get increasingly deployed in real-world scenarios such as home and industrial ... more As sensor networks get increasingly deployed in real-world scenarios such as home and industrial automation, there is a similarly growing demand in analyzing, consolidating, and storing the data collected by these networks. The dynamic, on-demand resources offered by today's cloud computing environments promise to satisfy this demand. However, prevalent security concerns still hinder the integration of sensor networks and cloud computing. In this paper, we show how recent progress in standardization can provide the basis for protecting data from diverse sensor devices when outsourcing data processing and storage to the cloud. To this end, we present our Sensor Cloud Security Library (SCSlib) that enables cloud service developers to transparently access cryptographically protected sensor data in the cloud. SCSlib specifically allows domain specialists who are not security experts to build secure cloud services. Our evaluation proves the feasibility and applicability of SCSlib for commodity cloud computing environments.

2013 IEEE 5th International Conference on Cloud Computing Technology and Science, 2013

ABSTRACT The adoption of the cloud computing paradigm is hindered by severe security and privacy ... more ABSTRACT The adoption of the cloud computing paradigm is hindered by severe security and privacy concerns which arise when outsourcing sensitive data to the cloud. One important group are those concerns regarding the handling of data. On the one hand, users and companies have requirements how their data should be treated. On the other hand, lawmakers impose requirements and obligations for specific types of data. These requirements have to be addressed in order to enable the affected users and companies to utilize cloud computing. However, we observe that current cloud offers, especially in an intercloud setting, fail to meet these requirements. Users have no way to specify their requirements for data handling in the cloud and providers in the cloud stack – even if they were willing to meet these requirements – can thus not treat the data adequately. In this paper, we identify and discuss the challenges for enabling data handling requirements awareness in the (inter-)cloud. To this end, we show how to extend a data storage service, AppScale, and Cassandra to follow data handling requirements. Thus, we make an important step towards data handling requirements-aware cloud computing.

Proceedings of the 5th ACM Conference on Data and Application Security and Privacy - CODASPY '15, 2015

ABSTRACT Bitcoin is a digital currency that uses anonymous cryptographic identities to achieve fi... more ABSTRACT Bitcoin is a digital currency that uses anonymous cryptographic identities to achieve financial privacy. However, Bitcoin's promise of anonymity is broken as recent work shows how Bitcoin's blockchain exposes users to reidentification and linking attacks. In consequence, different mixing services have emerged which promise to randomly mix a user's Bitcoins with other users' coins to provide anonymity based on the unlinkability of the mixing. However, proposed approaches suffer either from weak security guarantees and single points of failure, or small anonymity sets and missing deniability. In this paper, we propose CoinParty a novel, decentralized mixing service for Bitcoin based on a combination of decryption mixnets with threshold signatures. CoinParty is secure against malicious adversaries and the evaluation of our prototype shows that it scales easily to a large number of participants in real-world network settings. By the application of threshold signatures to Bitcoin mixing, CoinParty achieves anonymity by orders of magnitude higher than related work as we quantify by analyzing transactions in the actual Bitcoin blockchain and is first among related approaches to provide plausible deniability.

2014 International Conference on Future Internet of Things and Cloud, 2014

Internet of Things devices are envisioned to penetrate essentially all aspects of life, including... more Internet of Things devices are envisioned to penetrate essentially all aspects of life, including homes and urban spaces, in use cases such as health care, assisted living, and smart cities. One often proposed solution for dealing with the massive amount of data collected by these devices and offering services on top of them is the federation of the Internet of Things and cloud computing. However, user acceptance of such systems is a critical factor that hinders the adoption of this promising approach due to severe privacy concerns. We present UPECSI, an approach for user-driven privacy enforcement for cloud-based services in the Internet of Things to address this critical factor. UPECSI enables enforcement of all privacy requirements of the user once her sensitive data leaves the border of her network, provides a novel approach for the integration of privacy functionality into the development process of cloud-based services, and offers the user an adaptable and transparent configuration of her privacy requirements. Hence, UPECSI demonstrates an approach for realizing user-accepted cloud services in the Internet of Things.

International Journal of Grid and High Performance Computing, 2013

Trusted Cloud Computing, 2014

ABSTRACT The SensorCloud project aims at enabling the use of elastic, on-demand resources of toda... more ABSTRACT The SensorCloud project aims at enabling the use of elastic, on-demand resources of today’s Cloud offers for the storage and processing of sensed information about the physical world. Recent privacy concerns regarding the Cloud computing paradigm, however, constitute an adoption barrier that must be overcome to leverage the full potential of the envisioned scenario. To this end, a key goal of the SensorCloud project is to develop a security architecture that offers full access control to the data owner when outsourcing her sensed information to the Cloud. The central idea of this security architecture is the introduction of the trust point, a security-enhanced gateway at the border of the information sensing network. Based on a security analysis of the SensorCloud scenario, this chapter presents the design and implementation of the main components of our proposed security architecture. Our evaluation results confirm the feasibility of our proposed architecture with respect to the elastic, on-demand resources of today’s commodity Cloud offers.

Proceedings of the sixth ACM conference on Security and privacy in wireless and mobile networks - WiSec '13, 2013

6LoWPAN is an IPv6 adaptation layer that defines mechanisms to make IP connectivity viable for ti... more 6LoWPAN is an IPv6 adaptation layer that defines mechanisms to make IP connectivity viable for tightly resourceconstrained devices that communicate over low power, lossy links such as IEEE 802.15.4. It is expected to be used in a variety of scenarios ranging from home automation to industrial control systems. To support the transmission of IPv6 packets exceeding the maximum frame size of the link layer, 6LoWPAN defines a packet fragmentation mechanism. However, the best effort semantics for fragment transmissions, the lack of authentication at the 6LoWPAN layer, and the scarce memory resources of the networked devices render the design of the fragmentation mechanism vulnerable.

2013 IEEE 9th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), 2013

The HIP Diet EXchange (DEX) is an end-to-end security protocol designed for constrained network e... more The HIP Diet EXchange (DEX) is an end-to-end security protocol designed for constrained network environments in the IP-based Internet of Things (IoT). It is a variant of the IETF-standardized Host Identity Protocol (HIP) with a refined protocol design that targets performance improvements of the original HIP protocol. To stay compatible with existing protocol extensions, the HIP DEX specification thereby aims at preserving the general HIP architecture and protocol semantics. As a result, HIP DEX inherits the verbose HIP packet structure and currently does not consider the available potential to tailor the transmission overhead to constrained IoT environments. In this paper, we present Slimfit, a novel compression layer for HIP DEX. Most importantly, Slimfit i) preserves the HIP DEX security guarantees, ii) allows for stateless (de-)compression at the communication end-points or an on-path gateway, and iii) maintains the flexible packet structure of the original HIP protocol. Moreover, we show that Slimfit is also directly applicable to the original HIP protocol. Our evaluation results indicate a maximum compression ratio of 1.55 for Slimfit-compressed HIP DEX packets. Furthermore, Slimfit reduces HIP DEX packet fragmentation by 25 % and thus further decreases the transmission overhead for lossy network links. Finally, the compression of HIP DEX packets leads to a reduced processing time at the network layers below Slimfit. As a result, processing of Slimfit-compressed packets shows an overall performance gain at the HIP DEX peers.

2014 Sixth International Conference on Communication Systems and Networks (COMSNETS), 2014

Research has shown that the availability of crosslayer information from different protocol layers... more Research has shown that the availability of crosslayer information from different protocol layers enable adaptivity advantages of applications and protocols which significantly enhance the system performance. However, the development of such cross-layer interactions typically residing in the OS is very difficult mainly due to limited interfaces. The development gets even more complex for multiple running cross-layer interactions which may be added by independent developers without coordination causing (i) redundancy in cross-layer interactions leading to a waste of memory and CPU time and (ii) conflicting crosslayer interactions. In this paper, we focus on the former problem and propose a graph-based redundancy removal algorithm that automatically detects and resolves such redundancies without any feedback from the developer. We demonstrate the applicability of our approach for the cross-layer architecture CRAWLER that utilizes module compositions to realize cross-layer interactions. Our evaluation shows that our approach effectively resolves redundancies at runtime.

The increasing deployment of sensor networks, ranging from home networks to industrial automation... more The increasing deployment of sensor networks, ranging from home networks to industrial automation, leads to a similarly growing demand for storing and processing the collected sensor data. To satisfy this demand, the most promising approach to date is the utilization of the dynamically scalable, ondemand resources made available via the cloud computing paradigm. However, prevalent security and privacy concerns are a huge obstacle for the outsourcing of sensor data to the cloud. Hence, sensor data needs to be secured properly before it can be outsourced to the cloud. When securing the outsourcing of sensor data to the cloud, one important challenge lies in the representation of sensor data and the choice of security measures applied to it. In this paper, we present the SensorCloud protocol, which enables the representation of sensor data and actuator commands using JSON as well as the encoding of the object security mechanisms applied to a given sensor data item. Notably, we solely utilize mechanisms that have been or currently are in the process of being standardized at the IETF to aid the wide applicability of our approach.

Upcoming WiFi-based localization systems for indoor environments face a conflict of privacy inter... more Upcoming WiFi-based localization systems for indoor environments face a conflict of privacy interests: Server-side localization violates location privacy of the users, while localization on the user's device forces the localization provider to disclose the details of the system, e.g., sophisticated classification models. We show how Secure Two-Party Computation can be used to reconcile privacy interests in a state-ofthe-art localization system. Our approach provides strong privacy guarantees for all involved parties, while achieving room-level localization accuracy at reasonable overheads.