# The Need for Machine-Processable Agreements in Health Data Management +
## 3 The Need for New Policy Languages and Policy Management Systems  
Our use cases expose a need different to traditional data privacy or security. Traditional data privacy approaches such as privacy [12], k-anonymity [13], or l-diversity [14], offer top-down, coarse-grained and authoritative privacy policies. These approaches coming from the Web, Cubersecurity, Artificial Intelligence or Data Management communities focus on either suppressing or altering (e.g., by masking, generalizing or inserting noise) information before it is released in order to achieve de-identification or ensure confidentiality of individuals' identities. Differently, approaches that focus on denying access rather than distorting information, are access control approaches, such as the common role-based [15] and attribute-based [16]. These approaches aim to completely disallow access to certain data, depending on roles/attributes of users and purposes, in order to maintain confidentiality of private information. The basic assumption made by privacy technologies is that they protect against a non-trusted party, or an adversary. Thus they restrict data access or aim to protect a secret; such as the identity of an individual. In contrast there is not much focus on supporting automated approaches for privacy enforcing against relatively trusted parties. Limited focus in this direction was given by the so-called Privacy Enhancing Technologies, that offer languages for large scale systems and the World Wide Web. The now deprecated W3C standard of the P3P language ([[https://www.w3.org/P3P/](https://www.w3.org/P3P/)]([https://www.w3.org/P3P/](https://www.w3.org/P3P/))) would be used by web servers to specify their data handling practices in policies and web clients to state their own privacy preferences; based on an automatic match/mismatch between the two parts a webpage would be visited or not. These languages, similarly to \"Terms and Conditions\", are top-down, coarse-grained and they do not offer much customisation. Moreover they present \"accept all or nothing\" options and do not offer negotiation or partial access. These reasons led to an abandonment of most approaches and the proliferation of legal, natural language, \"Terms of Use\" agreements. In this paper, we advocate the need for a paradigm shift, and suggest the design of machine-processable privacy language able to express sharing agreements, tailored to health data sharing and based on semantic web technologies which can provide common shared vocabularies for data sharing intentions and agreements. We also believe such a language should come with the algorithmic machinery that is needed to process these agreements. One can build on top of more general schemas such as FOAF, and schema.org that can be used to describe persons and personal data, or DICOM [17] for modeling healthcare and medical imaging metadata. Mulitple ongoing approaches use knowledge graphs to capture aspects of data sharing agreements. The Open Digital Rights Language (ODRL) [18] is a policy expression language that models content, services, actions, prohibitions, and obligations. PROV-O [19] models provenance information generated in different systems and under different contexts. More interestingly, the SPECIAL [20] project provides a vocabulary for expressing consent together with data processing workflows which take such consent into account, while in [21] the authors develop an ontology that models privacy policies described in actual medical research data sharing agreements. Starting from such Knowledge Graphs we envision different entities able encode their preferences and intentions of data usage in a machine understandable way and have data processing algorithms automatically enforce these preferences. In order to achieve this, the developed vocabularies have to be backed by the development of generic and re-applicable algorithms; possibly borrowing from data integration [22] or ontology-based query answering [23].