are split into groups of similar functionality, and in each cluster of similar 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 groups better, and automated data analysis to draw conclusions.

Many teams across 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 , 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.



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