The use of synthetic data is a widely acknowledged privacy-preserving measure that reduces identity and attribute disclosure risks in micro-data. The idea is to learn the statistical properties of an original dataset, store this information in a model, and then use this model to generate artificial samples and build a synthetic dataset that resembles the original. One of the many different approaches of synthetization tools relies on describing the original dataset by using a Bayesian network. This method is implemented in the open-source tool DataSynthesizer and has proven particularly suitable for datasets with a small to moderate number of attributes. In this paper, we will substitute the greedy algorithm used for learning the Bayesian network by a substantially faster genetic algorithm. In addition, our goal is to protect particularly sensitive attributes by decreasing specific correlations in the synthetic data that may reveal personal information. We will thus show how to customize the network structures for specific machine learning tasks. Our experiments demonstrate that this technique allows to further decrease the disclosure risks and, hence, add to the applicability of synthetic data as technique for privacy preservation.

Personal, sensitive information contained in data sets is often discouraging the exchange and sharing of data, or even rendering it impossible. To still enable data sharing, anonymisation is a strategy often employed to avoid possible record identification o r i nference. A nonymisation s trategies are often data-type or modality dependent, as besides the actual attributes contained within a dataset, also certain other aspects might reveal information on the data subjects. For example in graph data, such as knowledge graphs, the structure within the graph, i.e. the connection between nodes, might allow to re-identify a specific p erson, e .g. b y k nowledge o f t he n umber of connections for some individuals within the dataset.Therefore, also the structure needs to undergo anonymisation to achieve privacy. In this paper, we optimise an algorithm that extended previous state of the art by considering multiple, different types of connections (relations) between nodes to achieve anonymity among each of these types. Our novel, open-source implementation scales to much larger graphs than previous work, which is important for efficiently a nonymising ever-increasing volumes of big, linked data.

Digitalization of knowledge work in communication-intensive domains such as intellectual property protection poses great challenges but also opportunities to improve today’s working environments. The legal domain is strongly characterized by knowledge work, whereby, despite a common legal framework, creativity of individual experts is decisive. This knowledge-intensive work deals with a great amount of data objects, not only as a working basis, but also as a result. While experts heavily follow individual working styles, they still rely on a vast amount of administrative tasks, which are carried out by the supporting staff. These tasks are expected to be performed regularly, reliably and without errors, despite necessary adjustments to the current case and the changing legal framework. Today, knowledge work and administrative tasks are typically supported by different tools that are hardly integrated. Therefore, the tracing of continuous work processes based on exchanged data objects is a great challenge. This traceability is crucial, not only for legal security reasons, but also to enable mining and learning of applicable knowledge about processes. In this paper, we propose a bottom-up approach, which applies a continuously evolving graph of integrated data objects and tasks to model and store static and dynamic aspects of administrative as well as knowledge work, and test the approach in a real-world setting in the domain of intellectual property. We further present initial results of a novel dependency-based mining approach to learn data-dependent task sequences in the graph-based model and discuss several methods for enabling privacy-preserving sharing and mining.

With ever increasing capacity for collecting, storing, and processing of data, there is also a high demand for intelligent data analysis methods. While there have been impressive advances in machine learning and similar domains in recent years, this also gives rise to concerns regarding the protection of personal and otherwise sensitive data, especially if it is to be analysed by third parties. Besides anonymisation, which becomes challenging with high dimensional data, one approach for privacy-preserving data mining lies in the usage of synthetic data, which comes with the promise of protecting the users' data and producing analysis results close to those achieved by using real data. In this paper, we analyse a number of different approaches for creating synthetic data, and study the utility of the created datasets for regression tasks, i.e. the prediction of a numeric value. We further investigate the similarity of real and synthetic data samples. Finally, we contribute to privacy assessments and measurements of the risk of attribute disclosure on synthetic data by extending an approach developed for categorical data.

Abstract This paper introduces an evolving cybersecurity knowledge graph that integrates and links critical information on real-world vulnerabilities, weaknesses and attack patterns from various publicly available sources. Cybersecurity constitutes a particularly interesting domain for the development of a domain-specific public knowledge graph, particularly due to its highly dynamic landscape characterized by time-critical, dispersed, and heterogeneous information. To build and continually maintain a knowledge graph, we provide and describe an integrated set of resources, including vocabularies derived from well-established standards in the cybersecurity domain, an ETL workflow that updates the knowledge graph as new information becomes available, and a set of services that provide integrated access through multiple interfaces. The resulting semantic resource offers comprehensive and integrated up-to-date instance information to security researchers and professionals alike. Furthermore, it can be easily linked to locally available information, as we demonstrate by means of two use cases in the context of vulnerability assessment and intrusion detection.