Origin of digital artifacts is asserted by digital provenance information. Provenance information is queried for proof statement validations, failure analysis, as well as replication and attribution validations. The history of data that specifies dependency among different data items that produce the data is better captured using semantic web technologies. However, such provenance information contains sensitive information such as personally identifiable information. Further, in the context of Semantic Web knowledge representation, the interrelationships among different provenance elements imply additional knowledge. In this paper, we propose an authorization model that enforces the purpose limitation principle for such semantically related information. We present the formalization of the security policy, however the policy does not reflect the direct implementation of the desired authorization. Therefore, security properties for important relationships such as sub set, set union and set intersection are defined in order to ensure consistency of the security policy. Finally, a use case scenario demonstrating the defined security policy and the properties is presented to indicate the applicability of the proposed model

Increasingly, more and more information is represented as graphs such as social network data, financial transactions and semantic assertions in Semantic Web context. Mining such data about people for useful insights has enormous social and commercial benefits. However, the privacy of the individuals in datasets is a major concern. Hence, the challenge is to enable analyses over a dataset while preserving the privacy of the individuals in the dataset. Differential privacy is a privacy model that offers a rigorous definition of privacy, which says that from the released results of an analysis it is ’difficult’ to determine if an individual contributes to the results or not. The differential privacy model is extensively studied in the context of relational databases. Nevertheless, there has been growing interest in the adaptation of differential privacy to graph data. Previous research in applying differential privacy model to graphs focuses on unlabeled graphs. However, in many applications graphs consist of labeled edges, and the analyses can be more expressive, which now takes into account the labels. Thus, it would be of interest to study the adaptation of differential privacy to edge-labeled directed graphs. In this paper, we present our foundational work towards that aim. First we present three variant notions of an individual’s information being/not being in the analyzed graph, which is the basis for formalizing the differential privacy guarantee. Next, we present our plan to study particular graph statistics using the differential privacy model, given the choice of the notion that represent the individual’s information being/not being in the analyzed graph.

Histograms and synthetic data are of key importance in data analysis. However, researchers have shown that even aggregated data such as histograms, containing no obvious sensitive attributes, can result in privacy leakage. To enable data analysis, a strong notion of privacy is required to avoid risking unintended privacy violations. Such a strong notion of privacy is differential privacy, a statistical notion of privacy that makes privacy leakage quantifiable. The caveat regarding differential privacy is that while it has strong guarantees for privacy, privacy comes at a cost of accuracy. Despite this trade-off being a central and important issue in the adoption of differential privacy, there exists a gap in the literature regarding providing an understanding of the trade-off and how to address it appropriately. Through a systematic literature review (SLR), we investigate the state-of-the-art within accuracy improving differentially private algorithms for histogram and synthetic data publishing. Our contribution is two-fold: 1) we identify trends and connections in the contributions to the field of differential privacy for histograms and synthetic data and 2) we provide an understanding of the privacy/accuracy trade-off challenge by crystallizing different dimensions to accuracy improvement. Accordingly, we position and visualize the ideas in relation to each other and external work, and deconstruct each algorithm to examine the building blocks separately with the aim of pinpointing which dimension of accuracy improvement each technique/approach is targeting. Hence, this systematization of knowledge (SoK) provides an understanding of in which dimensions and how accuracy improvement can be pursued without sacrificing privacy.

Log audits are the technical means to retrospectively reconstruct and analyze system activities for determining if the system events are in accordance with the rules. In the case of privacy compliance, compliance by detection approaches are promoted for achieving data protection obligations such as accountability and transparency. However significant challenges remain to fulfill privacy requirements through these approaches. This paper presents a systematic literature review that reveals the theoretical foundations of the state-of-art detective approaches for privacy compliance. We developed a taxonomy based on the technical design describing the contextual relationships of the existing solutions. The technical designs of the existing privacy detection solutions are primarily classified into privacy misuse detection and privacy anomaly detection. However, the design principles of these solutions are, to validate need-to-know and access control obligations hence the state-of-art privacy compliance validation mechanisms focus on usage limitations and accountability. The privacy compliance guarantee they provide is subtle when compared to the requirements arising from privacy regulations and data protection obligations.