@MASTERSTHESIS\{IMM2013-06632,
    author       = "R. Pietri",
    title        = "Privacy in Computational Social Science",
    year         = "2013",
    school       = "Technical University of Denmark, Department of Applied Mathematics and Computer Science / {DTU} Co",
    address      = "Matematiktorvet, Building 303B, {DK-}2800 Kgs. Lyngby, Denmark, compute@compute.dtu.dk",
    type         = "",
    note         = "{DTU} supervisor: Sune Lehmann J{\o}rgensen, sljo@dtu.dk, {DTU} Compute",
    url          = "http://www.compute.dtu.dk/English.aspx",
    abstract     = "The goal of the thesis is to give an overview of privacy management in Computational Social Science (CSS), to show what is the current situation and to understand areas that can be improved. Computational Social Science is an interdisciplinary research process that gathers and mines wealth of sensitive data to study human behaviour and social interactions. It relies on the mixture of social studies and nowadays technologies such as smartphones and Online Social Networks. {CSS}’s studies are aimed in understanding causes and effects in human behaviour, giving insights in their interactions, and trying to explain the inner nature of their relationship.
In the first part, it is presented an overview of existing {CSS} studies and their approach to participants’ privacy. Section 2 introduces {CSS}’s capabilities and Section 3 categorizes the works studied for this overview. The current situation regarding privacy regulations and informed consent practises for social experiments is discussed in Section 4. Section 5 shows methods employed for securing users’ data and relative threats. Anonymization techniques are discussed in Section 6. Section 7 presents information sharing and disclosure techniques. Findings are summarized in Privacy Actionable Items.
Part {II} briefly illustrates sensible-data, a new service for data collection and analysis developed by the collaboration of {DTU} and {MIT} universities. sensible-data implements best practises and outlined improvements identified in Part {I,} de-facto setting new standards for privacy management in Big Data. In the {CSS} context, sensibledata’s contributions are two-fold: researchers have a unique tool to create, conduct, and share their studies in a secure way, while participants can closely monitor and control their personal data, empowering their privacy.
Part {III} shows the engineering process to create one of sensible-data framework’s components. sensible-auditor is a tamper-evident auditing system that records in a secure way all the interactions within sensible-data system, such as users’ enrolments, participants’ data flows, etc. Design, implementation, and evaluation of sensible-auditor ’s realization are presented after a general introduction that explains the role of auditing in system security."
}