Interim research assessment 2003-2005 - Computer Science

This report primarily serves as a source of information for the 2007 Interim Research Assessment Committee for Computer Science at the three technical universities in the Netherlands. The report also provides information for others interested in our research activities

Towards Mobility Data Science (Vision Paper)

Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of GPS-equipped mobile devices and other inexpensive location-tracking technologies, mobility data is collected ubiquitously. In recent years, the use of mobility data has demonstrated significant impact in various domains including traffic management, urban planning, and health sciences. In this paper, we present the emerging domain of mobility data science. Towards a unified approach to mobility data science, we envision a pipeline having the following components: mobility data collection, cleaning, analysis, management, and privacy. For each of these components, we explain how mobility data science differs from general data science, we survey the current state of the art and describe open challenges for the research community in the coming years.Comment: Updated arXiv metadata to include two authors that were missing from the metadata. PDF has not been change

Technical Privacy Metrics: a Systematic Survey

The file attached to this record is the author's final peer reviewed versionThe goal of privacy metrics is to measure the degree of privacy enjoyed by users in a system and the amount of protection offered by privacy-enhancing technologies. In this way, privacy metrics contribute to improving user privacy in the digital world. The diversity and complexity of privacy metrics in the literature makes an informed choice of metrics challenging. As a result, instead of using existing metrics, new metrics are proposed frequently, and privacy studies are often incomparable. In this survey we alleviate these problems by structuring the landscape of privacy metrics. To this end, we explain and discuss a selection of over eighty privacy metrics and introduce categorizations based on the aspect of privacy they measure, their required inputs, and the type of data that needs protection. In addition, we present a method on how to choose privacy metrics based on nine questions that help identify the right privacy metrics for a given scenario, and highlight topics where additional work on privacy metrics is needed. Our survey spans multiple privacy domains and can be understood as a general framework for privacy measurement

Secure and Scalable Multi-User Searchable Encryption

By allowing a large number of users to behave as readers or writers, Multi-User Searchable Encryption (MUSE) raises new security and performance challenges beyond the typical requirements of Symmetric Searchable Encryption (SSE). In this paper we identify two core mandatory requirements of MUSE protocols being privacy in face of users colluding with the CSP and low complexity for the users, pointing that no existing MUSE protocol satisfies these two requirements at the same time. We then come up with the first MUSE protocol that satisfies both of them. The design of the protocol also includes new constructions for a secure variant of Bloom Filters (BFs) and multi-query Oblivious Transfer (OT)