Biblio

Ensuring sustainable sourcing of crude materials and production of goods is a pressing problem in consideration of the growing world population and rapid climate change. Supply-chain traceability systems based on distributed ledgers can help to enforce sustainability policies like production limits. We propose two mutually independent distributed-ledger-based protocols that enable public verifiability of policy compliance. They are designed for different supply-chain scenarios and use different privacy-enhancing technologies in order to protect confidential supply-chain data: secret sharing and homomorphic encryption. The protocols can be added to existing supply-chain traceability solutions with minor effort. They ensure confidentiality of transaction details and offer public verifiability of producers' compliance, enabling institutions and even end consumers to evaluate sustainability of supply chains. Through extensive theoretical and empirical evaluation, we show that both protocols perform verification for lifelike supply-chain scenarios in perfectly practical time.

State-of-the-art approaches in gait analysis usually rely on one isolated tracking system, generating insufficient data for complex use cases such as sports, rehabilitation, and MedTech. We address the opportunity to comprehensively understand human motion by a novel data model combining several motion-tracking methods. The model aggregates pose estimation by captured videos and EMG and EIT sensor data synchronously to gain insights into muscle activities. Our demonstration with biceps curl and sitting/standing pose generates time-synchronous data and delivers insights into our experiment’s usability, advantages, and challenges.

Recent technological advances in developing intelligent telecommunication networks, ultra-compact bendable wireless transceiver chips, adaptive wearable sensors and actuators, and secure computing infrastructures along with the progress made in psychology and neuroscience for understanding neu-rocognitive and computational principles of human behavior combined have paved the way for a new field of research: Tactile Internet with Human-in-the-Loop (TaHiL). This emerging field of transdisciplinary research aims to promote next generation digitalized human-machine interactions in perceived real time. To achieve this goal, mechanisms and principles of human goal-directed multisensory perception and action need to be integrated into technological designs for breakthrough innovations in mobile telecommunication, electronics and materials engineering, as well as computing. This overview highlights key challenges and the frontiers of research in the new field of TaHiL. Revolutionizing the current Internet as a digital infrastructure for sharing visual and auditory information globally, the TaHiL research will enable humans to share tactile and haptic information and thus veridically immerse themselves into virtual, remote, or inaccessible real environments to exchange skills and expertise with other humans or machines for applications in medicine, industry, and the Internet of Skills.

Benchmarking is an important measure for companies to investigate their performance and to increase efficiency. As companies usually are reluctant to provide their key performance indicators (KPIs) for public benchmarks, privacy-preserving benchmarking systems are required. In this paper, we present an enhanced privacy-preserving benchmarking protocol, which we implemented and evaluated based on the real-world scenario of product cost optimisation. It is based on homomorphic encryption and enables cloud-based KPI comparison, providing a variety of statistical measures. The theoretical and empirical evaluation of our benchmarking system underlines its practicability.

Responding to network security incidents requires interference with ongoing attacks to restore the security of services running on production systems. This approach prevents damage, but drastically impedes the collection of threat intelligence and the analysis of vulnerabilities, exploits, and attack strategies. We propose the live confinement of suspicious microservices into a sandbox network that allows to monitor and analyze ongoing attacks under quarantine and that retains an image of the vulnerable and open production network. A successful sandboxing requires that it happens completely transparent to and cannot be detected by an attacker. Therefore, we introduce a novel metric to measure the Quality of Deception (QoD) and use it to evaluate three proposed network deception mechanisms. Our evaluation results indicate that in our evaluation scenario in best case, an optimal QoD is achieved. In worst case, only a small downtime of approx. 3s per microservice (MS) occurs and thus a momentary drop in QoD to 70.26% before it converges back to optimum as the quarantined services are restored.

Contrary to popular belief, the paperless office has not yet established itself. Printer forensics is therefore still an important field today to protect the reliability of printed documents or to track criminals. An important task of this is to identify the source device of a printed document. There are many forensic approaches that try to determine the source device automatically and with commercially available recording devices. However, it is difficult to find intrinsic signatures that are robust against a variety of influences of the printing process and at the same time can identify the specific source device. In most cases, the identification rate only reaches up to the printer model. For this reason we reviewed document colour tracking dots, an extrinsic signature embedded in nearly all modern colour laser printers. We developed a refined and generic extraction algorithm, found a new tracking dot pattern and decoded pattern information. Through out we propose to reuse document colour tracking dots, in combination with passive printer forensic methods. From privacy perspective we additional investigated anonymization approaches to defeat arbitrary tracking. Finally we propose our toolkitdeda which implements the entire workflow of extracting, analysing and anonymisation of a tracking dot pattern.

Darknets, membership-concealing peer-to-peer networks, suffer from high message delivery delays due to insufficient routing strategies. They form topologies restricted to a subgraph of the social network of their users by limiting connections to peers with a mutual trust relationship in real life. Whereas centralized, highly successful social networking services entail a privacy loss of their users, Darknets at higher performance represent an optimal private and censorship-resistant communication substrate for social applications. Decentralized routing so far has been analyzed under the assumption that the network resembles a perfect lattice structure. Freenet, currently the only widely used Darknet, attempts to approximate this structure by embedding the social graph into a metric space. Considering the resulting distortion, the common greedy routing algorithm is adapted to account for local optima. Yet the impact of the adaptation has not been adequately analyzed. We thus suggest a model integrating inaccuracies in the embedding. In the context of this model, we show that the Freenet routing algorithm cannot achieve polylog performance. Consequently, we design NextBestOnce, a provable poylog algorithm based only on information about neighbors. Furthermore, we show that the routing length of NextBestOnce is further decreased by more than a constant factor if neighbor-of-neighbor information is included in the decision process.