This book examines how cloud-based services challenge the current application of antitrust and privacy laws in the EU and the US. The author looks at the elements of data centers, the way information is organized, and how antitrust, competition and privacy laws in the US and the EU regulate cloud-based services and their market practices. She discusses how platform interoperability can be a driver of incremental innovation and the consequences of not promoting radical innovation. She evaluates applications of predictive analysis based on big data as well as deriving privacy-invasive conduct. She looks at the way antitrust and privacy laws approach consumer protection and how lawmakers can reach more balanced outcomes by understanding the technical background of cloud-based services.
2 What is (big) data?
2 What is (big) data?
2.1 Definition and types of data
Data is Latin for Things Given. For this thesis, I will present and work with the definitions provided in legal statute. The EU Commission’s General Data Protection Regulation18 (GDPR) provides a definition of the terms “personal data” and “data subject” in Art. 4 para. 1 GDPR.
“Personal data means any information relating to a data subject”19
“Data subject means an identified natural person or a natural person who can be identified, directly or indirectly, in particular by reference to an identifier such as a name, an identification number, location data, an online identifier or to one or more factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that person.”20
As a general rule, data can appear in digital or analogue form. An example for analogue data is an LP disk. An example for a comparable content in digital form is an mp3-file with the same soundtrack. Data can for example vary in its source, type, format (i.e. audio, video and image), speed of occurrence (i.e. real-time data), and level of structure.
For the purposes of a legal discussion, this level of precision in the definition of “data” is sufficient to move forward with the analysis. However, for a discussion in the field of information science, this would not suffice. There is no uniform definition of the term “data” in STEM....
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