11 citations · 13 across the 11 of their papers we have counts for
8 papers · 1 filter
ParTEETor: A System for Partial Deployments of TEEs within Tor
Rachel King, Quinn Burke, Yohan Beugin +5
The Tor anonymity network allows users such as political activists and those under repressive governments to protect their privacy when communicating over the internet. At the same…
Systematic Evaluation of Geolocation Privacy Mechanisms
Alban Héon, Ryan Sheatsley, Quinn Burke +4
Location data privacy has become a serious concern for users as Location Based Services (LBSs) have become an important part of their life. It is possible for malicious parties hav…
Securing Cloud File Systems with Trusted Execution
Quinn Burke, Yohan Beugin, Blaine Hoak +7
Cloud file systems offer organizations a scalable and reliable file storage solution. However, cloud file systems have become prime targets for adversaries, and traditional designs…
Secure IP Address Allocation at Cloud Scale
Eric Pauley, Kyle Domico, Blaine Hoak +5
Public clouds necessitate dynamic resource allocation and sharing. However, the dynamic allocation of IP addresses can be abused by adversaries to source malicious traffic, bypass…
Privacy-Preserving Protocols for Smart Cameras and Other IoT Devices
Yohan Beugin, Quinn Burke, Blaine Hoak +5
Millions of consumers depend on smart camera systems to remotely monitor their homes and businesses. However, the architecture and design of popular commercial systems require user…
Measuring and Mitigating the Risk of IP Reuse on Public Clouds
Eric Pauley, Ryan Sheatsley, Blaine Hoak +3
Public clouds provide scalable and cost-efficient computing through resource sharing. However, moving from traditional on-premises service management to clouds introduces new chall…
Building a Privacy-Preserving Smart Camera System
Yohan Beugin, Quinn Burke, Blaine Hoak +5
Millions of consumers depend on smart camera systems to remotely monitor their homes and businesses. However, the architecture and design of popular commercial systems require user…
On the Robustness of Domain Constraints
Ryan Sheatsley, Blaine Hoak, Eric Pauley +3
Machine learning is vulnerable to adversarial examples-inputs designed to cause models to perform poorly. However, it is unclear if adversarial examples represent realistic inputs…