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Although there is no well-established definition of big data, its main characteristic is its sheer volume. Large volumes of data are generated by people (e.g., via social media) and by technology, including sensors (e.g., cameras, microphones), trackers (e.g., RFID tags, web surfing behavior) and other devices (e.g., mobile phones, wearables for self-surveillance/quantified self), whether or not they are connected to the Internet of Things. However, the large volumes of data needed to capitalize on the benefits of big data can to some extent also be established by the reuse of existing data, a source that is sometimes overlooked.
Data can be reused for purposes similar to that for which it was initially collected, but also beyond these purposes. Similarly, data can be reused in its original context, but also beyond this context. However, such repurposing and recontextualizing of data may lead to privacy issues. For instance, data reuse may lead to issues regarding informed consent and informational self-determination. When the data is used for profiling and other types of predictive analytics, also issues regarding stigmatization and discrimination may arise. This presentation by Bart Custers, Head of Research, eLaw – Center for Law and Digital Technologies at Leiden University, The Netherlands, focuses on the privacy issues of big data sharing and reuse and how these issues could be addressed.