The deep learning-based profiling attacks have received significant attention for their potential against masking-protected devices. Currently, additional capabilities like exploiting only a segment of the side-channel traces or having knowledge of the specific countermeasure scheme have been granted to attackers during the profiling phase. In case either capability is removed, a practical profiling attack faces great difficulty and complexity. To address this challenge, we propose an efficient and scheme-agnostic Leakage Distillation-based Profiling Attack (LD-PA). By distilling univariate leakage from a reference, we can train an encoder that extracts multivariate leakage from raw traces and transforms it into an effective representation (transitional leakage). An indirect connection between multivariate leakage and the target variable is established by bridging through the transitional leakage, thereby facilitating the inference of leaked values. Remarkably, LD-PA achieves successful attacks on multiple public datasets using a simple multilayer perceptron (MLP) without necessitating an exhaustive hyperparameter search, while its performance is competitive with state-of-the-art methods. Simultaneously, we delve into the nature of transitional leakage, confirming the existence of combined leakage. This, in turn, validates that the guidance from univariate leakage references aids in the combination of multivariate leakage. Besides that, each component of the multivariate leakage is extracted and stacked in a highly aligned manner. Moreover, we explored several factors impacting LD-PA performance, covering scenarios with limited profiling traces, noisy references, alternative references, and hyperparameter tuning.

Field studies on public displays can be difficult, expensive, and time-consuming. We investigate the feasibility of using virtual reality (VR) as a test-bed to evaluate deployments of public displays. Specifically, we investigate whether results from virtual field studies, conducted in a virtual public space, would match the results from a corresponding real-world setting. We report on two empirical user studies where we compared audience behavior around a virtual public display in the virtual world to audience behavior around a real public display. We found that virtual field studies can be a powerful research tool, as in both studies we observed largely similar behavior between the settings. We discuss the opportunities, challenges, and limitations of using virtual reality to conduct field studies, and provide lessons learned from our work that can help researchers decide whether to employ VR in their research and what factors to account for if doing so.