Research Topics

 

 

Distributed Ledger & Blockchain

Distributed Ledger Technologies (DLTs) are no longer only a hype topic, and effective DLT applications exist in many industries. They are often used in financial contexts but also in supply chain management, public services, and digital identity management.

Distributed Ledgers are databases that are redundantly stored on several nodes of a peer-to-peer network. This redundant storage in combination with cryptographic mechanisms makes Distributed Ledgers up to impossible to manipulate. Many DLTs support so-called smart contracts, that is, code that can be executed automatically by the DLT network. DLTs enable the secure sharing and tamper-resistant tracking of data in cross-organizational contexts. Blockchain is the most prominent kind of DLT.

DLT has a broad field of application. For example, the technology is used in payment, tamper-proof documentation, cross-organizational workflow management, ubiquitous services, tokenization or the machine economy.

Typically, DLT projects are highly interdisciplinary and require not only a technical but also an organizational and legal perspective. FINATRAX is one of the leading research groups in terms of DLT benchmarking and the analysis of DLT use cases. Moreover, we are involved in several large-scale DLT projects in the public sector, such as the European Blockchain Service Infrastructure and Germany’s Blockchain Infrastructure for Asylum Procedures.

Transfer Publications:

Academic Publications:

  • Sedlmeir, Johannes; Lockl, Jannik; Ross, Philipp; Miehle, Daniel; Luckow, Andre; Fridgen, Gilbert (2021) “The DLPS: A New Framework for Benchmarking Blockchains,” Proceedings of the 54th Hawaii International Conference on System Sciences. Available at: https://orbilu.uni.lu/bitstream/10993/45620/1/0670.pdf
  • Rieger, Alexander; Guggenmos, Florian; Lockl, Jannik; Fridgen, Gilbert; Urbach, Nils (2019) „Building a Blockchain Application that Complies with the EU General Data Protection Regulation,” MIS Quartlery Executive 18(4):263-279. Available at: https://www.researchgate.net/publication/337797310_Building_a_Blockchain_Application_that_Complies_with_the_EU_General_Data_Protection_Regulation
  • Sedlmeir, Johannes; Buhl, Hans Ulrich; Fridgen, Gilbert; and Keller, Robert (2020) "The Energy Consumption of Blockchain Technology: Beyond Myth," Business & Information Systems Engineering: Vol. 62: Iss. 6, 599-608.
    Available at: https://aisel.aisnet.org/bise/vol62/iss6/9
  • Rieger, Alexander; Roth, Tamara; Sedlmeir, Johannes; Fridgen, Gilbert (2021) „The privacy challenge in the race for digital vaccination certificates,” CORRESPONDENCE VOLUME 2, ISSUE 6, P633-634. Available at:  https://doi.org/10.1016/j.medj.2021.04.018

 

Digital Identities and Verifiable Credentials

Digital identity is an umbrella term that covers the attributes of persons, organisations or objects that are needed for the sake of authentication, authorisation, and identification. A person, organisation or object can make specific claims about their identity, such as their age is or the licences they hold. In the physical world, identity attributes are often bundled together and presented via a credential such as a passport, a driver’s licence, or a student ID card.

Credentials can also be issued in a digital format. Most digital credentials are stored in centralised data silos that are managed by governments or Big Tech companies; but there is an emerging, decentralised alternative known as a verifiable credential. Verifiable credentials can be stored in a digital wallet that is controlled by an individual’s mobile device. Verifiable credentials received an official recommendation from the World Wide Web Consortium (W3C) in 2019.

In today’s world, users increasingly rely on the convenient authentication and identification services of Big Tech companies. Well-known Single Sign-On services allow companies to aggregate users’ personal data. This comes with its own set of data protection concerns and security risks.  Similarly, personal data stored in governments' large databases is at risk of being targeted by cyber-attacks. In response to these concerns, decentralised identity management systems are gaining momentum. 

To further improve the security of digital identity services, tokenization can also be used. In this process, only relevant data is replaced by a token, which is then used as an identifier. All other information remains private.

Interesting research overlaps also arise with machine identities, since every machine that is integrated into a network also requires a digital identity.

Decentralised digital identity management is an interdisciplinary challenge. It intersects technological, legal, ethical, societal and political issues. The FINATRAX group participates in global efforts to support trusted, fair and inclusive digital identities.

Academic Publications:

  • Weigl, L., Barbereau, T., Fridgen, G., Rieger, A. (2022) "The Social Construction of Self-Sovereign Identity: An Extended Model of Interpretive Flexibility," Proceedings of the 55th Hawaii International Conference on System Sciences (HICSS), pp. xxxx–xxxx

 

Data Analytics, Artificial Intelligence & Federated Learnin

Data analytics and artificial intelligence leverage data to create models and algorithms which signify identification of patterns and regularity from the inputted data. The created models and algorithms are generally used for predictive estimations.

Artificial intelligence (AI), deals with the automation of intelligent behavior and machine learning. It refers to the attempt to emulate certain decision-making structures of humans, e.g. by building and programming a computer in such a way that it can process problems relatively independently.

Data is the next "big thing", as it is considered a strategic and long-term asset for businesses to increase their competitive edge in the market. Combined with an increasing need of privacy, Federated Learning and privacy preserving techniques like Differential Privacy (DP) or Secure Aggregation (SecAgg), came to squeeze the data utility by distributed learning environments (FL) applying secure protocols during the analysis (DP and SecAgg). In

The concepts and technologies of data analytics, artificial intelligence and federated learning can be explained in 3 parts: input, model, and output.

Input is concerned with how data is stored and communicated between users. There are centralized solutions where data is stored in a single location. There are also distributed solutions where data is split and stored in multiple locations but tethered to a network. Lastly, there are decentralized solutions where data is stored in multiple locations but untethered in terms of control. FL is a distributed solution in which models are created from geographically dispersed and tethered databases.

Model is concerned with how data is processed. Models represent a simplified version of reality and are created from evaluating data mathematically and statistically. Mathematical and statistical methods allow humans to identify patterns and trends and AI empowers humans by allowing computers or machines to identify patterns and trends more accurately and consistently.

Lastly, output is concerned with how information extracted from data is used. The extracted information is a compressed version of data and can be used in a variety of ways such as to improve business operations by optimizing processes or forecasting demand and supply.

FINATRAX’s role is to apply innovative technologies in different business contexts and demonstrating their uses as well as quantifying their expected impact. In doing so, we give industry guidance and forward expectation as to how their businesses could be improved from applying them.

Projects:

 

Internet-of-Things & 5G

5G and the Internet of Things (IoT) have the potential to enable fundamentally new applications, industries and business models and improve quality of life around the world. 5G enables instant high data rate communications, low latency and massive connectivity. This will lead to new IoT applications for mobile communications, autonomous vehicles or smart homes.

5G is the 5th generation mobile network and will become a global wireless standard. It is especially designed to establish a virtual connection between different IoT devices.

IoT refers to the large number of physical devices around the world that are connected to the internet, meanwhile collecting and sharing data. Thanks to the decreasing size and price of computer chips and fast wireless networks, it is possible to turn almost every item into a part of the IoT. This leads to digital intelligence and communication in real-time without a human intervention. To improve efficient communication between machines, it is also necessary to create digital identities for the machines and to create an effective machine economy the communication and the storage of data can be enhanced by the usage of DLT.

The application areas of the technologies are very broad and range from equipping pills with mini-sensors to the communication between airplanes. Generated information can also be managed transparently and easily accessible using DLT.

IoT is used for example in process and home automation, smart cars or smart grids. Businesses are increasingly using IoT data analytics to determine trends and patterns by analyzing structured and unstructured data to extract meaningful insights. It can be used to improve customer knowledge, enhance operational efficiency and create business value.