• S. van den Braak, S. Choenni, and S. Verwer, "Combining and Analyzing Judicial Databases," , Custers, B., Calders, T., Schermer, B., and Zarsky, T., Eds., Springer Berlin Heidelberg, 2013, vol. 3, pp. 191-206. bibtex Go to document
    @incollection {MVI, author = {van den Braak, S. and Choenni, S. and Verwer, S.},
      title = {Combining and Analyzing Judicial Databases},
      booktitle = {Discrimination and Privacy in the Information Society},
      series = {Studies in Applied Philosophy, Epistemology and Rational Ethics},
      editor = {Custers, Bart and Calders, Toon and Schermer, Bart and Zarsky, Tal},
      publisher = {Springer Berlin Heidelberg},
      isbn = {978-3-642-30487-3},
      keyword = {Engineering},
      pages = {191-206},
      volume = {3},
      url = {http://dx.doi.org/10.1007/978-3-642-30487-3_10},
      note = {10.1007/978-3-642-30487-3_10},
      abstract = {To monitor crime and law enforcement, databases of several organizations, covering different parts of the criminal justice system, have to be integrated. Combined data from different organizations may then be analyzed, for instance, to investigate how specific groups of suspects move through the system. Such insight is useful for several reasons, for example, to define an effective and coherent safety policy. To integrate or relate judicial data two approaches are currently employed: a data warehouse and a dataspace approach. The former is useful for applications that require combined data on an individual level. The latter is suitable for data with a higher level of aggregation. However, developing applications that exploit combined judicial data is not without risk. One important issue while handling such data is the protection of the privacy of individuals. Therefore, several precautions have to be taken in the data integration process: use aggregate data, follow the Dutch Personal Data Protection Act, and filter out privacy-sensitive results. Another issue is that judicial data is essentially different from data in exact or technical sciences. Therefore, data mining should be used with caution, in particular to avoid incorrect conclusions and to prevent discrimination and stigmatization of certain groups of individuals.},
      year = {2013}
    }

To monitor crime and law enforcement, databases of several organizations, covering different parts of the criminal justice system, have to be integrated. Combined data from different organizations may then be analyzed, for instance, to investigate how specific groups of suspects move through the system. Such insight is useful for several reasons, for example, to define an effective and coherent safety policy. To integrate or relate judicial data two approaches are currently employed: a data warehouse and a dataspace approach. The former is useful for applications that require combined data on an individual level. The latter is suitable for data with a higher level of aggregation. However, developing applications that exploit combined judicial data is not without risk. One important issue while handling such data is the protection of the privacy of individuals. Therefore, several precautions have to be taken in the data integration process: use aggregate data, follow the Dutch Personal Data Protection Act, and filter out privacy-sensitive results. Another issue is that judicial data is essentially different from data in exact or technical sciences. Therefore, data mining should be used with caution, in particular to avoid incorrect conclusions and to prevent discrimination and stigmatization of certain groups of individuals.

  • S. W. van den Braak, S. Choenni, R. Meijer, and A. Zuiderwijk, "Trusted third parties for secure and privacy-preserving data integration and sharing in the public sector," in Proceedings of the 13th Annual International Conference on Digital Government Research, New York, NY, USA, 2012, pp. 135-144. bibtex Go to document
    @inproceedings{DGO12, author = {van den Braak, S.W. and Choenni, S. and Meijer, R. and Zuiderwijk, A.},
      title = {Trusted third parties for secure and privacy-preserving data integration and sharing in the public sector},
      booktitle = {Proceedings of the 13th Annual International Conference on Digital Government Research},
      series = {dg.o '12},
      year = {2012},
      isbn = {978-1-4503-1403-9},
      location = {College Park, Maryland},
      pages = {135--144},
      numpages = {10},
      url = {http://doi.acm.org/10.1145/2307729.2307752},
      doi = {10.1145/2307729.2307752},
      acmid = {2307752},
      publisher = {ACM},
      address = {New York, NY, USA},
      keywords = {data integration, data sharing, public sector, trusted third parties},
      abstract = {For public organizations data integration and sharing are important in delivering better services. However, when sensitive data are integrated and shared, privacy protection and information security become key issues. This means that information systems must be secured and that access to sensitive data must be controlled. In this paper, a framework is presented to support data sharing between public organizations for collaboration purposes. The framework focuses on solutions towards optimal data sharing and integration while ensuring the security and privacy of individuals. Data sharing is based on the need-to-know principle, that is, data are only made available when they are required to perform core processes. To facilitate this, an approach is introduced in the form of a trusted third party that manages access control to personal information and thus helps to protect the privacy of individuals. It is argued that the proposed framework is suitable for data integration and sharing on various levels. An example of best practices of data sharing in the Netherlands shows how this framework facilitates data sharing to perform knowledge transfer and other higher-level tasks.}
    }

For public organizations data integration and sharing are important in delivering better services. However, when sensitive data are integrated and shared, privacy protection and information security become key issues. This means that information systems must be secured and that access to sensitive data must be controlled. In this paper, a framework is presented to support data sharing between public organizations for collaboration purposes. The framework focuses on solutions towards optimal data sharing and integration while ensuring the security and privacy of individuals. Data sharing is based on the need-to-know principle, that is, data are only made available when they are required to perform core processes. To facilitate this, an approach is introduced in the form of a trusted third party that manages access control to personal information and thus helps to protect the privacy of individuals. It is argued that the proposed framework is suitable for data integration and sharing on various levels. An example of best practices of data sharing in the Netherlands shows how this framework facilitates data sharing to perform knowledge transfer and other higher-level tasks.