Modeling and preventing inferences from sensitive value distributions in data release

Data sharing and dissemination are becoming increasingly important for conducting our daily life activities. The main consequence of this trend is that huge collections of data are easily available and accessible, leading to growing privacy concerns. The research community has devoted many efforts aiming at addressing the complex privacy requirements that characterize the modern Information Society. Although several advancements have been made, still many open issues need to be investigated. In this paper, we consider a scenario where data are incrementally released and we address the privacy problem arising when sensitive non released properties depend on (and can therefore be inferred from) non-sensitive released data. We propose a model capturing this inference problem, where sensitive information is characterized by peculiar value distributions of non sensitive released data. We then describe how to counteract possible inferences that an observer can draw by applying different statistical metrics on released data. Finally, we perform an experimental evaluation of our solution, showing its efficacy.