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This paper examines digital data-driven platforms and their impact on contemporary regulatory paradigms. While these phenomena are increasingly proclaimed as paradigm altering in many respects, they remain relatively little understood, including in their regulatory dimension. Lawmakers around the globe including the European Commission are currently trying to make sense of these evolutions and determine how to regulate digital platforms. In its 2016 Communication on Online Platforms, the European Commission proposed various options for regulating the platform economy, including self-regulatory and co-regulatory models. The Commission's assumption that self-regulation or co-regulation can replace topdown legislative intervention in the platform economy forms the background of this paper, which examines these three options to determine their respective suitability. We shall conclude that as command-and-control regulation as well as self-regulation raise significant problems in their application to the platform economy, co-regulation emerges as the most adequate option if certain conditions are met

This article examines data protection on blockchains and other forms of distributed ledger technology. Whereas the General Data Protection Regulation was fashioned for centralised methods of data collection, storage and processing, blockchains decentralise each of these processes. We engage with the resulting tensions in the below analysis.

This article examines the potential and limitations of blockchain technology and blockchain-based smart contracts in relation to copyright. Copyright has long been enforced through technological means, specifically Digital Rights Management. With the emergence of blockchains, many are now predicting a new era regarding the administration and enforcement of copyright through computer code. The article introduces the technology and related potential and limitations while stressing its capacity to act as a form of normative ordering that can express public or private objectives.

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Article 5(1)(c) of the European Union's General Data Protection Regulation (GDPR) requires that "personal data shall be [...] adequate, relevant, and limited to what is necessary in relation to the purposes for which they are processed ('data minimisation')". To date, the legal and computational definitions of 'purpose limitation' and 'data minimization' remain largely unclear. In particular, the interpretation of these principles is an open issue for information access systems that optimize for user experience through personalization and do not strictly require personal data collection for the delivery of basic service.In this paper, we identify a lack of a homogeneous interpretation of the data minimization principle and explore two operational definitions applicable in the context of personalization. The focus of our empirical study in the domain of recommender systems is on providing foundational insights about the (i) feasibility of different data minimization definitions, (ii) robustness of different recommendation algorithms to minimization, and (iii) performance of different minimization strategies.We find that the performance decrease incurred by data minimization might not be substantial, but that it might disparately impact different users-a finding which has implications for the viability of different formal minimization definitions. Overall, our analysis uncovers the complexities of the data minimization problem in the context of personalization and maps the remaining computational and regulatory challenges.