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Within the past 20 years, the data-centered field of computer sciences has been massively improved: data bases, search engines, data mining, distributed storage and distributed processing, virtualization, real-time simulation, sensors, etc. These technologies represent the basis for the subject field of “Big Data”, a buzz word which is in itself rather unspecific. The idea behind the term is to combine and evaluate all the available data, whether it comes from wind sensors or personal smart phones. This approach results in interesting questions regarding data privacy up to questions regarding public safety and the public good.

Topics of the module:
o Open Data Initiatives
o The benefit of private data
o Applications for a sustainable cities, homes, grids
o Legal, bureaucratic, political , technical, judicial barriers

Data access – but in a legal way
o Personal rights regarding data in different countries
o Trust and consumer protection

Technical approaches to deal with Big Data
o Identification and authorization
o Security gap and hazard
o Access control and guidelines
o Big Data in the "Cloud"
o AI / LLMs, word embeddings, transformers
o Blockchains

A new social context for data usage
o Coordination of interests
o Data being a currency?
o Focus on the user
o ML and racism

While combining huge quantities of data from different sources in order to deduce further economic, social or even political relevant information, ethical questions arise. These questions are strongly connected with the term „responsibility“. The topic “Big Data” prompts ethical questions of how to deal scientifically and economically with heterogeneous data, which can be collected worldwide and is thus subject to different legal conditions.

The students learn how to deal with questions like:

- What are previous and new, specific challenges of this topic areas?

- What are the challenges in generating new information out of extensive heterogeneous databases and datasets available?

- To whom belong the collected data, which data may or should I not use? Which data should/must not be used or combined in order to derive further information? Are there agreements – beside ethical reasons – that should be retained even if there might be a big economic benefit on the horizon?

- Which technical possibilities can support complying with these boundaries?

In addition to the pure mathematic-technical perspective, strategies and tools in the context of data security are also taught. Thus, the students gain an insight into ethical aspects of scientific and economic values in terms of “What should possibly not be done even if it could be done?”

Further information on teaching evaluation: https://www.leuphana.de/en/teaching/quality-management/evaluation/course-evaluation.html