This study explores the critical importance of privacy preservation within the learning sciences (LS) domain, focusing on safeguarding individuals’ privacy in databases. Learning sciences usually have categorical data, which includes only a finite set of elements. This study examines strategies for maintaining individual privacy while the accuracy and utility of the dataset are still acceptable even though untrusted parties access the privatized results.
We are collaborating with an interdisciplinary team of researchers to build systems that can aid in social behavioral research. This includes storage for multimodal, unstructured and time series data from classrooms and cognitive science lab experiments. For more context: