05 Fakultät Informatik, Elektrotechnik und Informationstechnik
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6
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Item Open Access Editorial - special issue on security and privacy in blockchains and the IoT volume II(2023) Stach, Christoph; Gritti, ClémentineItem Open Access SMARTEN : a sample-based approach towards privacy-friendly data refinement(2022) Stach, Christoph; Behringer, Michael; Bräcker, Julia; Gritti, Clémentine; Mitschang, BernhardTwo factors are crucial for the effective operation of modern-day smart services: Initially, IoT-enabled technologies have to capture and combine huge amounts of data on data subjects. Then, all these data have to be processed exhaustively by means of techniques from the area of big data analytics. With regard to the latter, thorough data refinement in terms of data cleansing and data transformation is the decisive cornerstone. Studies show that data refinement reaches its full potential only by involving domain experts in the process. However, this means that these experts need full insight into the data in order to be able to identify and resolve any issues therein, e.g., by correcting or removing inaccurate, incorrect, or irrelevant data records. In particular for sensitive data (e.g., private data or confidential data), this poses a problem, since these data are thereby disclosed to third parties such as domain experts. To this end, we introduce SMARTEN, a sample-based approach towards privacy-friendly data refinement to smarten up big data analytics and smart services. SMARTEN applies a revised data refinement process that fully involves domain experts in data pre-processing but does not expose any sensitive data to them or any other third-party. To achieve this, domain experts obtain a representative sample of the entire data set that meets all privacy policies and confidentiality guidelines. Based on this sample, domain experts define data cleaning and transformation steps. Subsequently, these steps are converted into executable data refinement rules and applied to the entire data set. Domain experts can request further samples and define further rules until the data quality required for the intended use case is reached. Evaluation results confirm that our approach is effective in terms of both data quality and data privacy.Item Open Access Solving high-dimensional dynamic portfolio choice models with hierarchical B-splines on sparse grids(2021) Schober, Peter; Valentin, Julian; Pflüger, DirkDiscrete time dynamic programming to solve dynamic portfolio choice models has three immanent issues: firstly, the curse of dimensionality prohibits more than a handful of continuous states. Secondly, in higher dimensions, even regular sparse grid discretizations need too many grid points for sufficiently accurate approximations of the value function. Thirdly, the models usually require continuous control variables, and hence gradient-based optimization with smooth approximations of the value function is necessary to obtain accurate solutions to the optimization problem. For the first time, we enable accurate and fast numerical solutions with gradient-based optimization while still allowing for spatial adaptivity using hierarchical B-splines on sparse grids. When compared to the standard linear bases on sparse grids or finite difference approximations of the gradient, our approach saves an order of magnitude in total computational complexity for a representative dynamic portfolio choice model with varying state space dimensionality, stochastic sample space, and choice variables.Item Open Access Editorial - special Issue on security and privacy in blockchains and the IoT - 3rd edition(2026) Stach, Christoph; Gritti, Clémentine; Litou, IoulianaItem Open Access Protecting sensitive data in the information age : state of the art and future prospects(2022) Stach, Christoph; Gritti, Clémentine; Bräcker, Julia; Behringer, Michael; Mitschang, BernhardThe present information age is characterized by an ever-increasing digitalization. Smart devices quantify our entire lives. These collected data provide the foundation for data-driven services called smart services. They are able to adapt to a given context and thus tailor their functionalities to the user’s needs. It is therefore not surprising that their main resource, namely data, is nowadays a valuable commodity that can also be traded. However, this trend does not only have positive sides, as the gathered data reveal a lot of information about various data subjects. To prevent uncontrolled insights into private or confidential matters, data protection laws restrict the processing of sensitive data. One key factor in this regard is user-friendly privacy mechanisms. In this paper, we therefore assess current state-of-the-art privacy mechanisms. To this end, we initially identify forms of data processing applied by smart services. We then discuss privacy mechanisms suited for these use cases. Our findings reveal that current state-of-the-art privacy mechanisms provide good protection in principle, but there is no compelling one-size-fits-all privacy approach. This leads to further questions regarding the practicality of these mechanisms, which we present in the form of seven thought-provoking propositions.Item Open Access Proximity-based service discovery for distributed digital twin systems(2025) Rothermel, Kurt; Herzog, Otthein; Wu, Zhiqiang SiegfriedOver the past decade, there has been a significant increase in interest in digital twin (DT) technology in a variety of domains. While research on DTs of single assets was initially prevalent, there has been a notable shift towards distributed systems of DTs, which connect to each other to collaborate. Typically, collaboration is enabled by DTs providing services that can be consumed by other DTs. In service-oriented systems, a service is typically identified by type information. However, this is not sufficient in distributed DT systems, where DTs associated with different physical entities may provide the same type of service. Consequently, selecting the appropriate service depends not only on the service type, but also on the associated physical entity. However, requiring DTs to know the mapping of services to their physical environment is not feasible for large dynamic systems. This paper presents a novel proximity-based service discovery method that allows DTs to select services based on service type and their proximity to other objects. That is, service specifications are fully abstracted from the mapping of services to physical objects, relieving DTs from maintaining information about this mapping. Furthermore, service discovery is robust to changes in the physical environment and service population. The proposed service discovery method has been implemented on top of a spatial DBMS. We argue that this implementation is optimal in terms of network utilization and latency, and perform comprehensive evaluations to show the performance of discovery queries as a function of their complexity.Item Open Access Query processing in blockchain systems : current state and future challenges(2021) Przytarski, Dennis; Stach, Christoph; Gritti, Clémentine; Mitschang, BernhardWhen, in 2008, Satoshi Nakamoto envisioned the first distributed database management system that relied on cryptographically secured chain of blocks to store data in an immutable and tamper-resistant manner, his primary use case was the introduction of a digital currency. Owing to this use case, the blockchain system was geared towards efficient storage of data, whereas the processing of complex queries, such as provenance analyses of data history, is out of focus. The increasing use of Internet of Things technologies and the resulting digitization in many domains, however, have led to a plethora of novel use cases for a secure digital ledger. For instance, in the healthcare sector, blockchain systems are used for the secure storage and sharing of electronic health records, while the food industry applies such systems to enable a reliable food-chain traceability, e.g., to prove compliance with cold chains. In these application domains, however, querying the current state is not sufficient - comprehensive history queries are required instead. Due to these altered usage modes involving more complex query types, it is questionable whether today’s blockchain systems are prepared for this type of usage and whether such queries can be processed efficiently by them. In our paper, we therefore investigate novel use cases for blockchain systems and elicit their requirements towards a data store in terms of query capabilities. We reflect the state of the art in terms of query support in blockchain systems and assess whether it is capable of meeting the requirements of such more sophisticated use cases. As a result, we identify future research challenges with regard to query processing in blockchain systems.Item Open Access Efficient and scalable initialization of partitioned coupled simulations with preCICE(2021) Totounferoush, Amin; Simonis, Frédéric; Uekermann, Benjamin; Schulte, MiriampreCICE is an open-source library, that provides comprehensive functionality to couple independent parallelized solver codes to establish a partitioned multi-physics multi-code simulation environment. For data communication between the respective executables at runtime, it implements a peer-to-peer concept, which renders the computational cost of the coupling per time step negligible compared to the typical run time of the coupled codes. To initialize the peer-to-peer coupling, the mesh partitions of the respective solvers need to be compared to determine the point-to-point communication channels between the processes of both codes. This initialization effort can become a limiting factor, if we either reach memory limits or if we have to re-initialize communication relations in every time step. In this contribution, we remove two remaining bottlenecks: (i) We base the neighborhood search between mesh entities of two solvers on a tree data structure to avoid quadratic complexity, and (ii) we replace the sequential gather-scatter comparison of both mesh partitions by a two-level approach that first compares bounding boxes around mesh partitions in a sequential manner, subsequently establishes pairwise communication between processes of the two solvers, and finally compares mesh partitions between connected processes in parallel. We show, that the two-level initialization method is fives times faster than the old one-level scheme on 24,567 CPU-cores using a mesh with 628,898 vertices. In addition, the two-level scheme is able to handle much larger computational meshes, since the central mesh communication of the one-level scheme is replaced with a fully point-to-point mesh communication scheme.Item Open Access CLAIRE : parallelized diffeomorphic image registration for large-scale biomedical imaging applications(2022) Himthani, Naveen; Brunn, Malte; Kim, Jae-Youn; Schulte, Miriam; Mang, Andreas; Biros, GeorgeWe study the performance of CLAIRE - a diffeomorphic multi-node, multi-GPU image-registration algorithm and software-in large-scale biomedical imaging applications with billions of voxels. At such resolutions, most existing software packages for diffeomorphic image registration are prohibitively expensive. As a result, practitioners first significantly downsample the original images and then register them using existing tools. Our main contribution is an extensive analysis of the impact of downsampling on registration performance. We study this impact by comparing full-resolution registrations obtained with CLAIRE to lower resolution registrations for synthetic and real-world imaging datasets. Our results suggest that registration at full resolution can yield a superior registration quality-but not always. For example, downsampling a synthetic image from 10243 to 2563 decreases the Dice coefficient from 92% to 79%. However, the differences are less pronounced for noisy or low contrast high resolution images. CLAIRE allows us not only to register images of clinically relevant size in a few seconds but also to register images at unprecedented resolution in reasonable time. The highest resolution considered are CLARITY images of size 2816×3016×1162. To the best of our knowledge, this is the first study on image registration quality at such resolutions.Item Open Access New generic coupling adapters for ice sheet and subglacial hydrology models (ISSM-preCICE Adapter 0.4, CUAS-MPI 0.1)(2026) Abele, Daniel; Kleiner, Thomas; Fischler, Yannic; Uekermann, Benjamin; Chourdakis, Gerasimos; Morlighem, Mathieu; Basermann, Achim; Bischof, Christian; Bungartz, Hans-Joachim; Humbert, AngelikaAdequate Earth system simulations require interactions between the atmosphere, the ocean, and the ice sheets. To this end, numerical solvers that compute the evolution of the different Earth system components are coupled. There are frameworks and libraries for coupling that handle the complex tasks of coordinating solver execution, communicating between processes, and mapping between different meshes. This allows solvers to be developed independently without compromises on numerical methods or technology. Code reuse is improved, both over large, monolithic software systems that reimplement each coupled model as well as over ad-hoc coupling scripts. In this work, we use the preCICE coupling library to couple the Ice-sheet and Sea-level System Model (ISSM) with the subglacial hydrology model CUAS-MPI. An adapter for each model is required to pass meshes and coupled variables between the model and preCICE. We focus mainly on the technical aspects (design, development, and use of the adapters, choice of coupling library, and large-scale performance analysis), using a synthetic setup to verify functionality and correctness. The adapters we developed are generic and reusable for use cases other than ice-hydrology coupling. Computational performance for the coupled system is measured on a high-performance computing cluster. We find that coupling with preCICE has low computational overhead and does not negatively impact scaling. A comparison between unidirectional and bidirectional coupling for the synthetic ice sheet shows that the coupling captures the anticipated feedback mechanisms between the two systems. The coupled simulations are numerically stable, despite the nonlinearities in the physical system. The generic coupling library preCICE is well suited for our use case and has advantages as well as disadvantages over Earth System Model-specific libraries. The new framework and code enable studies of the subglacial hydrological systems of ice sheets, as well as coupling ISSM or CUAS-MPI with other codes, such as in global Earth System Models or process models.