This thesis deals with the evidence that enable accountability, the privacy risks involved in using them and a privacy-aware solution to the problem of unauthorized evidence disclosure. 

Legal means to protect privacy of an individual is anchored on the data protection perspective i.e., on the responsible collection and use of personal data. Accountability plays a crucial role in such legal privacy frameworks for assuring an individual’s privacy. In the European context, accountability principle is pervasive in the measures that are mandated by the General Data Protection Regulation. In general, these measures are technically achieved through automated privacy audits. System traces that record the system activities are the essential inputs to those automated audits. Nevertheless, the traces that enable accountability are themselves subject to privacy risks, because in most cases, they inform about processing of the personal data. Therefore, ensuring the privacy of the accountability traces is equally important as ensuring the privacy of the personal data. However, by and large, research involving accountability traces is concerned with storage, interoperability and analytics challenges rather than on the privacy implications involved in processing them.

This dissertation focuses on both the application of accountability evidence such as in the automated privacy audits and the privacy aware use of them. The overall aim of the thesis is to provide a conceptual understanding of the privacy compliance research domain and to contribute to the solutions that promote privacy-aware use of the traces that enable accountability. To address the first part of the objective, a systematic study of existing body of knowledge on automated privacy compliance is conducted. As a result, the state-of-the-art is conceptualized as taxonomies. The second part of the objective is accomplished through two results; first, a systematic understanding of the privacy challenges involved in processing of the system traces is obtained, second, a model for privacy aware access restrictions are proposed and formalized in order to prevent illegitimate access to the system traces. Access to accountability traces such as provenance are required for automatic fulfillment of accountability obligations, but they themselves contain personally identifiable information, hence in this thesis we provide a solution to prevent unauthorized access to the provenance traces.

This thesis deals with the evidence that enables accountability, the privacy risks involved in using it and proposes a privacy-aware solution for preventing unauthorized evidence disclosure.

Accountability plays a crucial role in the legal privacy frameworks for assuring individuals’ privacy.  In the European context, accountability principle is pervasive in the measures that are mandated by the General Data Protection Regulation. In general, these measures are technically achieved through automated privacy audits. Traces that record the system activities are the essential inputs to those audits. Nevertheless, such traces that enable accountability are themselves subject to privacy risks, because in most cases, they inform about the processing of the personal data. Therefore, ensuring the privacy of the traces is equally important as ensuring the privacy of the personal data. The aim of the thesis is to provide a conceptual understanding of the automated privacy compliance research and to contribute to the solutions that promote privacy-aware use of the accountability traces. This is achieved in this dissertation through a systematic study of the existing body of knowledge in automated privacy compliance, a systematic analysis of the privacy challenges involved in processing the traces and a proposal of a privacy-aware access control model for preventing illegitimate access to the traces.

The thesis considers a systematic approach to design and develop techniques for preventing personal data exposure in next generation information management systems with the aim of ensuring accountability of data controllers (entities that process personal data).

With a rapid growth in the communication technologies, heterogenous computing environments that offer cost-effective data processing alternatives are emerging. Thus, the information-flow of personal data spans beyond the information processing practices of data controllers thereby involving other parties that process personal data. Moreover, in order to enable interoperability, data in such environments is given well-defined structure and meaning by means of graph-based data models. Graphs, inherently emphasize connections between things, and when graphs are used to model personal data records, the connections and the network structure may reveal intimate details about our inter-connected society.

In the European context, the General Data Protection Regulation (GDPR) provides a legal framework for personal data processing. The GDPR stipulates specific consequences for non-compliance to the data protection principles, in the view of ensuring accountability of data controllers in their personal data processing practices. Widely recognized approaches to implement the Privacy by Design (PbD) principle in the software application development process, are broader in scope. Hence, processes to implement personal data protection techniques for specific systems are not the central aspect of the aforementioned approaches.

In order to influence the implementation of techniques for preventing personal data misuse associated with sharing of data represented as graphs, a conceptual mechanism for building privacy techniques is developed. The conceptual mechanism consists of three elements, namely, a risk analysis for Semantic Web information management systems using Privacy Impact Assessment (PIA) approach, two privacy protection techniques for graphs enriched with semantics and a model to approach evaluation of adherence to the goals resulted from the risk analysis. The privacy protection techniques include an access control model that embodies purpose limitation principle—an essential aspect of GDPR—and adaptations of the differential privacy model for graphs with edge labels. The access control model takes into account the semantics of the graph elements for authorizing access to the graph data. In our differential privacy adaptations, we define and study through experiments, four different approaches to adapt the differential privacy model to edge-labeled graph datasets.

The thesis considers a systematic approach to design and develop techniques for preventing personal data exposure in next generation information management systems with the aim of ensuring accountability of data controllers (entities that process personal data).

With a rapid growth in the communication technologies, heterogenous computing environments that offer cost-effective data processing alternatives are emerging. Thus, the information-flow of personal data spans beyond the information processing practices of data controllers thereby involving other parties that process personal data. Moreover, in order to enable interoperability, data in such environments is given well-defined structure and meaning by means of graph-based data models. Graphs, inherently emphasize connections between things, and when graphs are used to model personal data records, the connections and the network structure may reveal intimate details about our inter-connected society.

The GDPR stipulates specific consequences for non-compliance to the data protection principles, in the view of ensuring accountability of data controllers in their personal data processing practices. Widely recognized approaches to implement the Privacy by Design (PbD) principle in the software application development process, are broader in scope. Hence, processes to implement privacy techniques for specific systems are not the central aspect of the aforementioned approaches.

In order to influence the implementation of techniques for preventing personal data misuse associated with sharing of data represented as graphs, a conceptual mechanism for building privacy techniques is developed. The conceptual mechanism consists of three elements, namely, a risk analysis for Semantic Web information management systems using Privacy Impact Assessment (PIA) approach, two privacy protection techniques for graphs enriched with semantics and a model to approach evaluation of adherence to the goals resulted from the risk analysis.