Lachlan Moore, Tatsuya Mori (Waseda University, NICT)

This study delves into the utilization patterns, perceptions, and misconceptions surrounding Virtual Private Networks (VPNs) among users in Canada and Japan. We administered a comprehensive survey to 234 VPN users in these two countries, aiming to elucidate the motivations behind VPN usage, users’ comprehension of VPN functionality, and prevalent misconceptions. A distinctive feature of our research lies in its cross-cultural comparison, a departure from previous studies predominantly centered on users within a Western context. Our findings underscore noteworthy distinctions among participant groups. Specifically, Japanese users predominantly employ VPNs for security purposes, whereas Canadian users leverage VPNs for a more diverse array of services, encompassing privacy and access to region-specific content. Furthermore, disparities in VPN understanding emerged, with Canadians demonstrating a superior grasp of VPN applications despite limited technical knowledge, while Japanese participants exhibited a more profound understanding of VPNs, particularly in relation to encrypting transmitted traffic. Notably, both groups exhibited a constrained awareness regarding the data logging practices associated with VPNs. This research significantly contributes to the broader comprehension of VPN usage and sheds light on the cultural intricacies that shape VPN adoption and perceptions, offering valuable insights into the diverse motivations and behaviors of users in Canada and Japan.

View More Papers

Security-Performance Tradeoff in DAG-based Proof-of-Work Blockchain Protocols

Shichen Wu (1. School of Cyber Science and Technology, Shandong University 2. Key Laboratory of Cryptologic Technology and Information Security, Ministry of Education), Puwen Wei (1. School of Cyber Science and Technology, Shandong University 2. Quancheng Laboratory 3. Key Laboratory of Cryptologic Technology and Information Security, Ministry of Education), Ren Zhang (Cryptape Co. Ltd. and…

Read More

Low-Quality Training Data Only? A Robust Framework for Detecting...

Yuqi Qing (Tsinghua University), Qilei Yin (Zhongguancun Laboratory), Xinhao Deng (Tsinghua University), Yihao Chen (Tsinghua University), Zhuotao Liu (Tsinghua University), Kun Sun (George Mason University), Ke Xu (Tsinghua University), Jia Zhang (Tsinghua University), Qi Li (Tsinghua University)

Read More

WIP: Towards a Certifiably Robust Defense for Multi-label Classifiers...

Dennis Jacob, Chong Xiang, Prateek Mittal (Princeton University)

Read More

On the Vulnerability of Traffic Light Recognition Systems to...

Sri Hrushikesh Varma Bhupathiraju (University of Florida), Takami Sato (University of California, Irvine), Michael Clifford (Toyota Info Labs), Takeshi Sugawara (The University of Electro-Communications), Qi Alfred Chen (University of California, Irvine), Sara Rampazzi (University of Florida)

Read More