This article surveys existing vocabularies, ontologies and policy languages that can be used to represent informational items referenced in GDPR rights and obligations, such as the ‘notification of a data breach’, the ‘controller’s identity’ or a ‘DPIA’. Rights and obligations in GDPR are analyzed in terms of information flows between different stakeholders, and a complete collection of 57 different informational items that are mentioned by GDPR is described. 13 privacy-related policy languages and 9 data protection vocabularies and ontologies are studied in relation to this list of informational items. ODRL and LegalRuleML emerge as the languages that can respond positively to a greater number of the defined comparison criteria if complemented with DPV and GDPRtEXT, since 39 out of the 57 informational items can be modelled. Online supplementary material is provided, including a simple search application and a taxonomy of the identified entities.

/pdf/analysis-of-ontologies-and-policy-languages-to-represent-w9hgvjgv.pdf

Analysis of ontologies and policy languages to represent information flows in GDPR

The processing of personal information is omnipresent in our datadriven society enabling personalized services, which are regulated by privacy policies. Although privacy policies are strictly defined by the General Data Protection Regulation (GDPR), no systematic mechanism is in place to enforce them. Especially if data is merged from several sources into a data-set with different privacy policies associated, the management and compliance to all privacy requirements is challenging during the processing of the data-set. Privacy policies can vary hereby due to different policies for each source or personalization of privacy policies by individual users. Thus, the risk for negligent or malicious processing of personal data due to defiance of privacy policies exists. To tackle this challenge, a privacy-preserving framework is proposed. Within this framework privacy policies are expressed in the proposed Layered Privacy Language (LPL) which allows to specify legal privacy policies and privacy-preserving de-identification methods. The policies are enforced by a Policy-based De-identification (PD) process. The PD process enables efficient compliance to various privacy policies simultaneously while applying pseudonymization, personal privacy anonymization and privacy models for de-identification of the data-set. Thus, the privacy requirements of each individual privacy policy are enforced filling the gap between legal privacy policies and their technical enforcement.

/pdf/modelling-of-a-privacy-language-and-efficient-policy-based-4rlsif2ia0.pdf

Modelling of a privacy language and efficient policy-based de-identification

Since 25th May 2018, the EU-wide General Data Protection Regulation (GDPR) applies in order to strengthen
 the rights of Data Subjects. Although the GDPR specifies the required information, which has to be presented
 to a Data Subject, it can still be argued for a lack of transparency due to unfavourable presentation of
 the privacy policy. Furthermore, no systematic approach for the enforcement of privacy policies in technical
 systems is deployed. These issues are tackled by the both human- and machine-readable Layered Privacy Language
 (LPL), which models legal privacy policies. This work introduces an extension for LPL to comply with Art.
 12 – 14 GDPR. Additionally, user interface prototypes will be introduced to allow the creation of
 LPL privacy policies by the Data Protection Officer as well as a structured presentation of the LPL privacy
 policy for web-applications.

The Layered Privacy Language Art. 12 - 14 GDPR Extension - Privacy Enhancing User Interfaces

Not only are common privacy policies hard to read and understand, but users are usually not experiencing any noticeable reactions of the application to their personal choices, if any are given. The Layered Privacy Language (LPL) is used to present legal privacy policies to the user in a concise way allowing the user fine-grained personalization options. Our contribution consists of the definition and detailing of possible personalization interaction scenarii in the context of web-applications and their validation within our evaluation environment. To simulate a negotiation of a privacy policy, a scenario environment is proposed, simulating several personal privacy preferences and users. Users can hereby perform different privacy policy personalization actions.

Let Users Control Their Data – Privacy Policy-Based User Interface Design

The Layered Privacy Language Art 12 - 14 GDPR Extension - Privacy Enhancing User Interfaces.

s We present the LPL Personal Privacy Policy User Interface (LPL PPP UI), which is intended to inform Data Subjects about the contents of the privacy policy and to allow personalisation of purposes to support free and informed consent. The capabilities of the LPL PPP UI, consisting in informing the Data Subject about the contents of a privacy policy in a structured way and personal privacy interactions are presented. The LPL PPP UI is evaluated against regular designed privacy policies. Furthermore, future challenges and objectives for privacy policy user interfaces are given.

LPL Personal Privacy Policy User Interface: Design and Evaluation

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LPL Personal Privacy Policy User Interface: Design and Evaluation