We first launched our bug bounty program in 2014, with initial bounties for critical bugs in the range of $5,000, ramping up to (currently) over $10,000 for critical bugs. Over the past three years, leading security researchers from around the world have participated in our programs with some amazing, often original research. Beyond just the individual bugs, we have learned many a lesson, uncovering unique, interesting threats, exploit vectors, and new research as well as rejigged our priorities based on the bug bounty reports. From Dropbox and all our users, a big THANK YOU to all the researchers that help secure Dropbox for our users!
In our previous post, we provided an overview of the global edge network that we deployed to improve performance for our users around the world. We built this edge network over the last two years as part of a strategy to deliver the benefits of Magic Pocket.
Alongside our edge network, we launched a global backbone network that connects our data centers in North America not only to each other, but also to the edge nodes around the world. In this blog, we’ll first review how we went about building out this backbone network and then discuss the benefits that it’s delivering for us and for our users.
This is an expanded version of my talk at NginxConf 2017 on September 6, 2017. As an SRE on the Dropbox Traffic Team, I’m responsible for our Edge network: its reliability, performance, and efficiency. The Dropbox edge network is an nginx-based proxy tier designed to handle both latency-sensitive metadata transactions and high-throughput data transfers. In a system that is handling tens of gigabits per second while simultaneously processing tens of thousands latency-sensitive transactions, there are efficiency/performance optimizations throughout the proxy stack, from drivers and interrupts, through TCP/IP and kernel, to library, and application level tunings.
Since launching Magic Pocket last year, we’ve been storing and serving more than 90 percent of our users’ data on our own custom-built infrastructure, which has helped us to be more efficient and improved performance for our users globally.
But with about 75 percent of our users located outside of the United States, moving onto our own custom-built data center was just the first step in realizing these benefits. As our data centers grew, the rest of our network also expanded to serve our users — more than 500 million around the globe — at light-speed with a consistent level of reliability,
In this post we will take you behind the scenes on how we built a state-of-the-art Optical Character Recognition (OCR) pipeline for our mobile document scanner. We used computer vision and deep learning advances such as bi-directional Long Short Term Memory (LSTMs), Connectionist Temporal Classification (CTC), convolutional neural nets (CNNs), and more. In addition, we will also dive deep into what it took to actually make our OCR pipeline production-ready at Dropbox scale.