Stefany Cruz (Northwestern University), Logan Danek (Northwestern University), Shinan Liu (University of Chicago), Christopher Kraemer (Georgia Institute of Technology), Zixin Wang (Zhejiang University), Nick Feamster (University of Chicago), Danny Yuxing Huang (New York University), Yaxing Yao (University of Maryland), Josiah Hester (Georgia Institute of Technology)

Users face various privacy risks in smart homes, yet there are limited ways for them to learn about the details of such risks, such as the data practices of smart home devices and their data flow. In this paper, we present Privacy Plumber, a system that enables a user to inspect and explore the privacy “leaks” in their home using an augmented reality tool. Privacy Plumber allows the user to learn and understand the volume of data leaving the home and how that data may affect a user’s privacy— in the same physical context as the devices in question, because we visualize the privacy leaks with augmented reality. Privacy Plumber uses ARP spoofing to gather aggregate network traffic information and presents it through an overlay on top of the device in an smartphone app. The increased transparency aims to help the user make privacy decisions and mend potential privacy leaks, such as instruct Privacy Plumber on what devices to block, on what schedule (i.e., turn off Alexa when sleeping), etc. Our initial user study with six participants demonstrates participants’ increased awareness of privacy leaks in smart devices, which further contributes to their privacy decisions (e.g., which devices to block).

View More Papers

Cloud-Hosted Security Operations Center (SOC)

Drew Walsh, Kevin Conklin (Deloitte)

Read More

Private Certifier Intersection

Bishakh Chandra Ghosh (Indian Institute of Technology Kharagpur), Sikhar Patranabis (IBM Research - India), Dhinakaran Vinayagamurthy (IBM Research - India), Venkatraman Ramakrishna (IBM Research - India), Krishnasuri Narayanam (IBM Research - India), Sandip Chakraborty (Indian Institute of Technology Kharagpur)

Read More

BlockScope: Detecting and Investigating Propagated Vulnerabilities in Forked Blockchain...

Xiao Yi (The Chinese University of Hong Kong), Yuzhou Fang (The Chinese University of Hong Kong), Daoyuan Wu (The Chinese University of Hong Kong), Lingxiao Jiang (Singapore Management University)

Read More

AdvCAPTCHA: Creating Usable and Secure Audio CAPTCHA with Adversarial...

Hao-Ping (Hank) Lee (Carnegie Mellon University), Wei-Lun Kao (National Taiwan University), Hung-Jui Wang (National Taiwan University), Ruei-Che Chang (University of Michigan), Yi-Hao Peng (Carnegie Mellon University), Fu-Yin Cherng (National Chung Cheng University), Shang-Tse Chen (National Taiwan University)

Read More