Personal data is any information that relates to an individual. Before disclosing such data to third parties, data controllers must be aware of the de-anonymization risks associated with their datasets and take appropriate anonymization measures. To carry out such actions, data controllers require tools that can analyze the risks in their datasets while also providing the necessary anonymization methods for addressing those risks. Existing tools of this type are insufficient for handling high-dimensional data as well as visualizing their risks. In this paper, we demonstrate DaRAV (De-anonymization Risk Analysis through Visualizations), a tool that addresses these limitations by providing risk analysis methods for five types of complex, high-dimensional data through interactive visualizations, as well as anonymization methods that allow users to create anonymized versions of their data.

Recent work has shown that cell phone mobility data has the unique potential to create accurate models for human mobility and consequently the spread of infected diseases [74]. While prior studies have exclusively relied on a mobile network operator’s subscribers’ aggregated data in modelling disease dynamics, it may be preferable to contemplate aggregated mobility data of infected individuals only. Clearly, naively linking mobile phone data with health records would violate privacy by either allowing to track mobility patterns of infected individuals, leak information on who is infected, or both. This work aims to develop a solution that reports the aggregated mobile phone location data of infected individuals while still maintaining compliance with privacy expectations. To achieve privacy, we use homomorphic encryption, validation techniques derived from zero-knowledge proofs, and differential privacy. Our protocol’s open-source implementation can process eight million subscribers in 70 minutes.

Machine learning research, particularly in genomics, is often based on wide shaped datasets, i.e. datasets having a large number of features, but a small number of samples. Such configurations raise the possibility of chance influence (the increase of measured accuracy due to chance correlations) on the learning process and the evaluation results. Prior research underlined the problem of generalization of models obtained based on such data. In this paper, we investigate the influence of chance on prediction and show its significant effects on wide shaped datasets. First, we empirically demonstrate how significant the influence of chance in such datasets is by showing that prediction models trained on thousands of randomly generated datasets can achieve high accuracy. This is the case even when using cross-validation. We then provide a formal analysis of chance influence and design formal chance influence estimators based on the dataset parameters, namely its sample size, the number of features, the number of classes and the class distribution. Finally, we provide an in-depth discussion of the formal analysis including applications of the findings and recommendations on chance influence mitigation.

We motivate the need for, and describe the contents of a novel patent research collection, publicly available and for free, covering multimodal and multilingual data from six patent authorities. The new patent test collection complements existing patent test collections, which are vertical (one domain or one authority over many years). Instead, the new collection is horizontal: it includes all technical domains from the major patenting authorities over the relatively short time span of two years. In addition to bringing together documents currently scattered across different test collections, the collection provides, for the first time, Korean documents, to complement those from Europe, US, Japan, and China. This new collection can be used on a variety of tasks beyond traditional information retrieval. We exemplify this with a task of high-relevance today: de-anonymisation.