Volume 60, Issue 5-6 - Application of Blockchain Technology / Host Editor: Paul Mueller

In this article, we propose an architecture that allows to exchange messages or data via a blockchain solution, while keeping the business process independent from the concrete blockchain. The project is the consequence of a need to have fast development of a blockchain based proof of concept that shows the feasibility of a business process, while knowing that in a future step the underlying blockchain solution has to be replaced for reasons of licensing or maximal data throughput. This is done by providing an architecture on base of layers, similar to the OSI-model, and encapsulates the used blockchain within a wrapper layer that covers all blockchain specific properties and only provides a transparent view on the reading or writing from and to the blockchain. The higher layers re-implement point-to-point communication and introduce confidentiality by the use of encryption techniques. The architecture has been tested and proven by implementing two different blockchain solutions that are shielded by specific wrapper layer implementations. This wrapper layer is responsible for the fragmentation of the sent data and their encapsulation into the concrete blockchain solution. The reconstruction of the data takes care of the data fragments being sent potentially out of order or being incomplete. A payload layer is introduced to provide point-to-point communication and to embed the main message inside a message structure. In collaboration with underlying layer it identifies for which message one is not the addressee and allow the underling layer to stop collecting unnecessary data. A crypto-layer finally provides ways of encrypting messages for one or many recipients. Having fixed layers not only allows to replace the underlying blockchain solution but also to extend or replace the other layers in case new security features or optimized protocols need to embedded. The proposed solution does not allow to use specific features like smart contracts and only provides undeniable tamper-proofed existence of a sent message.

Driven by successful pilot projects in supply chain and logistics, Blockchain has become one of the industry’s latest technology hypes. In this paper, we cut through the hype and shed light on the expectations of industry professionals towards the benefits and challenges of Blockchain. Also, we categorize current Blockchain applications that are expected to provide tangible benefits for supply chain and logistics processes. To explore such potentials, we argue that companies should gain own first-hand experiences through small-scale experiments.

Currently, timestamps are certified by central timestamping authorities, which have disadvantages of centralization. The concept of the decentralized trusted timestamping (DTT) was developed by Gipp et al. to address these drawbacks. The paper provides insights into the architecture and implementation of a decentralized timestamp service taking the integration of multiple blockchain types into account. Furthermore, the components are introduced and the versatile application scenarios are presented. A future direction of research is the evaluation of blockchain technology and their suitability for timestamping.

Supply chains are the basis of most everyday life products. Both data integrity and authenticity of related information have severe implications for quality and safety of end-products. Hence, tamper-proof storage is necessary that prevents unauthorized modifications. We examine peer-reviewed blockchain technologies according to four criteria relevant to supply chains: On-chain storage, off-chain storage, verification cost and secure data sharing. Our evaluation yields an overview of concepts for modeling supply chain processes and points out that on-chain storage is currently not practical.

The blockchain technology enables entities to query and alter information without trusting a middle party while providing a secure data storage in a decentralized manner. In this paper, we focus on an IT data supply chain scenario, where multiple actors negotiate a tenancy agreement for virtualized network resources. This process consists of service providers (SPs) requesting the embedding of specified virtual networks across multiple infrastructure providers (InPs). Since InPs are typically not willing to disclose detailed internal network information, this is a major deal breaker that hampers the efficiency of the service negotiation process. After reviewing the related work on centralized and decentralized virtual network embedding (VNE) approaches, we briefly discuss the reasons for a blockchain approach for this problem. Our approach comprises of a brokerless blockchain based system, that uses smart contracts and a VN partitioning algorithm based on the Vickrey auction model. Finally, we investigate the feasibility of our approach, by first analyzing the behavior of the introduced auction model in adverse conditions and by secondly evaluating the blockchain performance given different consensus protocols.

This work introduces the design and implementation of an Android-based Peer-to-peer Purchase and Rental Application termed PuRSCA, which leverages Smart Contracts (SC) and the Ethereum public blockchain (BC). As a Device-to-device (D2D) communication protocol, WiFi-Direct is chosen to enable the P2P data transmission between two parties. This work results in a cost-efficient, secure, SC-based, P2P, and Decentralized application (Dapp). Evaluations on performance of this Dapp is specified in terms of its D2D deployment, transaction costs, scalability, security, and privacy.

The last decade has been characterized by the collection and availability of unprecedented amounts of data due to rapidly decreasing storage costs and the omnipresence of sensors and data-producing global online-services. In order to process and analyze this data deluge, novel distributed data processing systems resting on the paradigm of data flow such as Apache Hadoop, Apache Spark, or Apache Flink were built and have been scaled to tens of thousands of machines. However, writing efficient implementations of data analysis programs on these systems requires a deep understanding of systems programming, prohibiting large groups of data scientists and analysts from efficiently using this technology. In this article, we present some of the main achievements of the research carried out by the Berlin Big Data Cente (BBDC). We introduce the two domain-specific languages Emma and LARA, which are deeply embedded in Scala and enable declarative specification and the automatic parallelization of data analysis programs, the PEEL Framework for transparent and reproducible benchmark experiments of distributed data processing systems, approaches to foster the interpretability of machine learning models and finally provide an overview of the challenges to be addressed in the second phase of the BBDC.

The efficient and intelligent handling of large, often distributed and heterogeneous data sets increasingly determines the scientific and economic competitiveness in most application areas. Mobile applications, social networks, multimedia collections, sensor networks, data intense scientific experiments, and complex simulations nowadays generate a huge data deluge. Nonetheless, processing and analyzing these data sets with innovative methods open up new opportunities for its exploitation and new insights. Nevertheless, the resulting resource requirements exceed usually the possibilities of state-of-the-art methods for the acquisition, integration, analysis and visualization of data and are summarized under the term big data. ScaDS Dresden/Leipzig, as one Germany-wide competence center for collaborative big data research, bundles efforts to realize data-intensive applications for a wide range of applications in science and industry. In this article, we present the basic concept of the competence center and give insights in some of its research topics.

Modern processors are highly optimized systems where every single cycle of computation time matters. Many optimizations depend on the data that is being processed. Microarchitectural attacks leak this data (side channels) or exploit physical imperfections to take control of the entire system (fault attacks). In my thesis (D. Gruss. Software-based Microarchitectural Attacks . PhD thesis, Graz University of Technology, 2017), I improved over state of the art in microarchitectural attacks and defenses in three dimensions. I cover these briefly in this summary. First, I show that attacks can be fully automated. Second, I present several novel previously unknown side channels. Third, I show that attacks can be mounted in highly restricted environments such as sandboxed JavaScript code in websites, and on any computer system including smartphones, tablets, personal computers, and commercial cloud systems. These results formed one of the corner stones for attacks like Meltdown (M. Lipp et al. Meltdown: Reading kernel memory from user space. In USENIX Security Symposium , 2018) and Spectre (P. Kocher et al. Spectre attacks: Exploiting speculative execution. In S&P , 2019) which were discovered months after the thesis was concluded.