Posted by David Konerding, Staff Software Engineer
In April 2011, we announced the Google Exacycle for Visiting Faculty, a new academic research awards program donating one billion core-hours of computational capacity to researchers. The Exacycle project enables massive parallelism for doing science in the cloud, and inspired multiple proposals aiming to take advantage of cloud scale. Today, we would like to share some exciting results from a project built on Google’s infrastructure.
Google Research Scientist Kai Kohlhoff, in collaboration with Stanford University and Google engineers, investigated how an important signalling protein in the membrane of human cells can switch off and on by changing its three-dimensional structure following a sequence of local conformational changes. This research can help to better understand the effects of certain chemical compounds on the human body and assist future development of more potent drug molecules with fewer side effects.
The protein, known as the beta-2 adrenergic receptor, is a G protein-coupled receptor (GPCR), a primary drug target that plays a role in several debilitating health conditions. These include asthma, type-2 diabetes, obesity, and hypertension. The receptor and its close GPCR relatives bind to many familiar molecules, such as epinephrine, beta-blockers, and caffeine. Understanding their structure, function, and the underlying dynamics during binding and activation increases our chances to decode the causes and mechanisms of diseases.
To gain insights into the receptor’s dynamics, Kai performed detailed molecular simulations using hundreds of millions of core hours on Google’s infrastructure, generating hundreds of terabytes of valuable molecular dynamics data. The Exacycle program enabled the realization of simulations with longer sampling and higher accuracy than previous experiments, exposing the complex processes taking place on the nanoscale during activation of this biological switch.
The paper summarizing the results of Kai’s and his collaborators’ work is featured on the January cover of Nature Chemistry, with artwork by Google R&D UX Creative Lead Thor Lewis, to be published on December 17, 2013. The online version of his paper was published on their website today.
We are extremely pleased with the results of this program. We look forward to seeing this research continue to develop.
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Posted by Úlfar Erlingsson, Tech Lead Manager, Security Research
(Cross-posted on the Chromium Blog and the Google Online Security Blog)
At Google, we are constantly trying to improve the techniques we use to protect our users security and privacy. One such project, RAPPOR (Randomized Aggregatable Privacy-Preserving Ordinal Response), provides a new state-of-the-art, privacy-preserving way to learn software statistics that we can use to better safeguard our users’ security, find bugs, and improve the overall user experience.
Building on the concept of randomized response, RAPPOR enables learning statistics about the behavior of users’ software while guaranteeing client privacy. The guarantees of differential privacy, which are widely accepted as being the strongest form of privacy, have almost never been used in practice despite intense research in academia. RAPPOR introduces a practical method to achieve those guarantees.
To understand RAPPOR, consider the following example. Let’s say you wanted to count how many of your online friends were dogs, while respecting the maxim that, on the Internet, nobody should know you’re a dog. To do this, you could ask each friend to answer the question “Are you a dog?” in the following way. Each friend should flip a coin in secret, and answer the question truthfully if the coin came up heads; but, if the coin came up tails, that friend should always say “Yes” regardless. Then you could get a good estimate of the true count from the greater-than-half fraction of your friends that answered “Yes”. However, you still wouldn’t know which of your friends was a dog: each answer “Yes” would most likely be due to that friend’s coin flip coming up tails.
RAPPOR builds on the above concept, allowing software to send reports that are effectively indistinguishable from the results of random coin flips and are free of any unique identifiers. However, by aggregating the reports we can learn the common statistics that are shared by many users. We’re currently testing the use of RAPPOR in Chrome, to learn statistics about how unwanted software is hijacking users’ settings.
We believe that RAPPOR has the potential to be applied for a number of different purposes, so were making it freely available for all to use. Well continue development of RAPPOR as a standalone open-source project so that anybody can inspect and test its reporting and analysis mechanisms, and help develop the technology. We’ve written up the technical details of RAPPOR in a report that will be published next week at the ACM Conference on Computer and Communications Security.
We’re encouraged by the feedback we’ve received so far from academics and other stakeholders, and we’re looking forward to additional comments from the community. We hope that everybody interested in preserving user privacy will review the technology and share their feedback at rappor-discuss@googlegroups.com
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