A recent NSF report and a number of security and privacy disasters in the IoT space (see the blog post on Schneier’s blog) highlighted the challenges and opportunities in Edge Computing, leveraging the high processing capabilities and low latency offered at the edge of the network (IoT devices, smartphones, cloudlets) for achieving scalable yet secure and private analytics. Recently we put two papers on ArXiv, focusing on Privacy-Preserving Analytics using smartphones and constrained devices on the network (such as a Raspberry Pi). I encourage the privacy, machine learning, and mobile computing enthusiasts to read these papers and kindly provide us with any feedback on the analytics which can improve the research efforts in this space.
Chris Greenhalgh explains the basic concept of the databox in a few simple slides. See the videos on YouTube:
We have had a lot of interest in some of our recent work: “A Glance through the VPN Looking Glass: IPv6 Leakage and DNS Hijacking in Commercial VPN Clients”, which has just been published at the Privacy Enhancing Technologies Symposium in Philadelphia. We’re delighted this has made a positive impact, with many VPNs announcing fixes to deal with the issues we raised and notified them all months before publishing the paper. However, we wanted to add a few extra comments in light of the statements made in recent days.