Publications
in
Artificial Intelligence for Security
Enhancing Protection in a Changing World
ISBN: 978-3-031-57452-8
together with Tommi Kärkkäinen:
Artificial Intelligence and Differential Privacy: Review of Protection Estimate Models
Abstract
Differential Privacy (DP) can provide strong guarantees that personal information is not disclosed in data sets. This is ensured from mathematical, theoretical, and relational proof of privacy, which makes it important to understand the actual behavior of the DP-based protection models. For this purpose, we will review what kind of frameworks or models are available to estimate how well an implemented differential privacy model works. Special attention is paid to how to assess that a certain level of privacy has been reached, what configurations were used, and how to estimate the privacy loss. Our goal is to locate a common framework that could help one decide, based on privacy requirements, which model and configuration should be used and how its protection can be ensured.
DOI: https://doi.org/10.1007/978-3-031-57452-8_3
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in
Artificial Intelligence and Cybersecurity
Theory and Applications
ISBN: 978-3-031-15030-2
Differential Privacy: An Umbrella Review
Abstract
Privacy-preserving analysis of data refers to possibilities of using personal information from individuals in a completely anonymous fashion. In a statistical sense, this means that statistics and models derived and learned from data are insensitive to individual observations. Differential Privacy as defined by Cynthia Dwork in (Dwork 2006) has become a popular approach for ensuring privacy. In contrast to earlier definitions, Dwork defined differential privacy as a relative guarantee that nothing more could be learned from data whether an individual observation is included or excluded from the analysis. This was achieved by adding random noise that is bigger than the effect of a change due to the largest single participant. The approach was referred as 𝜖-differential privacy. Such an actionable definition gave more room for practitioners to define how, for example, machine learning algorithms can ensure differential privacy. In this paper, we present an umbrella review on differential privacy related studies based on a methodology proposed by Aromataris et al. (Int J Evidence-Based Healthcare 13(3):132–140, 2015).
DOI: 10.1007/978-3-031-15030-2_8
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Abstact:
Global software development using agile methods is commonplace in software industry nowadays. Scrum, as the agile development management framework, can be distributed in many ways, especially concerning how the key roles are presented in different sites. We describe here a single case study of a distributed Scrum, mainly for maintenance of the already constructed web portal. Using a qualitative method, both working well and challenging parts of the software work, as experienced by the project stakeholders, are revealed and discussed.
I'm currently working with my next article. It will be systematic literature review of agile and/or Scrum usage as a management model in software maintenance.
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