  Social Virtual Reality Learning Environment (VRLE) is a novel edge computing
platform for collaboration amongst distributed users. Given that VRLEs are used
for critical applications (e.g., special education, public safety training), it
is important to ensure security and privacy issues. In this paper, we present a
novel framework to obtain quantitative assessments of threats and
vulnerabilities for VRLEs. Based on the use cases from an actual social VRLE
viz., vSocial, we first model the security and privacy using the attack trees.
Subsequently, these attack trees are converted into stochastic timed automata
representations that allow for rigorous statistical model checking. Such an
analysis helps us adopt pertinent design principles such as hardening,
diversity and principle of least privilege to enhance the resilience of social
VRLEs. Through experiments in a vSocial case study, we demonstrate the
effectiveness of our attack tree modeling with a reduction of 26% in
probability of loss of integrity (security) and 80% in privacy leakage
(privacy) in before and after scenarios pertaining to the adoption of the
design principles.
