Google Security Blog

Final removal of trust in WoSign and StartCom Certificates

Google Security Blog - Thu, 07/20/2017 - 2:19pm
Posted by Andrew Whalley and Devon O'Brien, Chrome Security

As previously announced, Chrome has been in the process of removing trust from certificates issued by the CA WoSign and its subsidiary StartCom, as a result of several incidents not in keeping with the high standards expected of CAs.

We started the phase out in Chrome 56 by only trusting certificates issued prior to October 21st 2016, and subsequently restricted trust to a set of whitelisted hostnames based on the Alexa Top 1M. We have been reducing the size of the whitelist over the course of several Chrome releases.

Beginning with Chrome 61, the whitelist will be removed, resulting in full distrust of the existing WoSign and StartCom root certificates and all certificates they have issued.

Based on the Chromium Development Calendar, this change is visible in the Chrome Dev channel now, the Chrome Beta channel around late July 2017, and will be released to Stable around mid September 2017.

Sites still using StartCom or WoSign-issued certificates should consider replacing these certificates as a matter of urgency to minimize disruption for Chrome users.
Categories: Google Security Blog

Identifying Intrusive Mobile Apps Using Peer Group Analysis

Google Security Blog - Wed, 07/12/2017 - 1:08pm
Posted by Martin Pelikan, Giles Hogben, and Ulfar Erlingsson of Google’s Security and Privacy team
Mobile apps entertain and assist us, make it easy to communicate with friends and family, and provide tools ranging from maps to electronic wallets. But these apps could also seek more device information than they need to do their job, such as personal data and sensor data from components, like cameras and GPS trackers.

To protect our users and help developers navigate this complex environment, Google analyzes privacy and security signals for each app in Google Play. We then compare that app to other apps with similar features, known as functional peers. Creating peer groups allows us to calibrate our estimates of users’ expectations and set adequate boundaries of behaviors that may be considered unsafe or intrusive. This process helps detect apps that collect or send sensitive data without a clear need, and makes it easier for users to find apps that provide the right functionality and respect their privacy. For example, most coloring book apps don’t need to know a user’s precise location to function and this can be established by analyzing other coloring book apps. By contrast, mapping and navigation apps need to know a user’s location, and often require GPS sensor access.

One way to create app peer groups is to create a fixed set of categories and then assign each app into one or more categories, such as tools, productivity, and games. However, fixed categories are too coarse and inflexible to capture and track the many distinctions in the rapidly changing set of mobile apps. Manual curation and maintenance of such categories is also a tedious and error-prone task.

To address this, Google developed a machine-learning algorithm for clustering mobile apps with similar capabilities. Our approach uses deep learning of vector embeddings to identify peer groups of apps with similar functionality, using app metadata, such as text descriptions, and user metrics, such as installs. Then peer groups are used to identify anomalous, potentially harmful signals related to privacy and security, from each app’s requested permissions and its observed behaviors. The correlation between different peer groups and their security signals helps different teams at Google decide which apps to promote and determine which apps deserve a more careful look by our security and privacy experts. We also use the result to help app developers improve the privacy and security of their apps.
Apps are split into groups of similar functionality, and in each cluster of similar apps the established baseline is used to find anomalous privacy and security signals.
These techniques build upon earlier ideas, such as using peer groups to analyze privacy-related signals, deep learning for language models to make those peer groups better, and automated data analysis to draw conclusions.

Many teams across Google collaborated to create this algorithm and the surrounding process. Thanks to several, essential team members including Andrew Ahn, Vikas Arora, Hongji Bao, Jun Hong, Nwokedi Idika, Iulia Ion, Suman Jana, Daehwan Kim, Kenny Lim, Jiahui Liu, Sai Teja Peddinti, Sebastian Porst, Gowdy Rajappan, Aaron Rothman, Monir Sharif, Sooel Son, Michael Vrable, and Qiang Yan.

For more information on Google’s efforts to detect and fight potentially harmful apps (PHAs) on Android, see Google Android Security Team’s Classifications for Potentially Harmful Applications.
References

S. Jana, Ú. Erlingsson, I. Ion (2015). Apples and Oranges: Detecting Least-Privilege Violators with Peer Group Analysis. arXiv:1510.07308 [cs.CR].

T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, J. Dean (2013). Distributed Representations of Words and Phrases and their Compositionality. Advances in Neural Information Processing Systems 26 (NIPS 2013).

Ú. Erlingsson (2016). Data-driven software security: Models and methods. Proceedings of the 29th IEEE Computer Security Foundations Symposium (CSF'16), Lisboa, Portugal.
Categories: Google Security Blog