An Economic Study of the Effect of Android Platform Fragmentation on Security Updates

February 2018·
Sadegh Farhang
Aron Laszka
Aron Laszka
,
Jens Grossklags
PDF
Abstract

Vendors in the Android ecosystem typically customize their devices by modifying Android Open Source Project (AOSP) code, adding in-house developed proprietary software, and pre-installing third-party applications. However, research has documented how various security problems are associated with this customization process.

We develop a model of the Android ecosystem utilizing the concepts of game theory and product differentiation to capture the competition involving two vendors customizing the AOSP platform. We show how the vendors are incentivized to differentiate their products from AOSP and from each other, and how prices are shaped through this differentiation process. We also consider two types of consumers: security-conscious consumers who understand and care about security, and na¨ıve consumers who lack the ability to correctly evaluate security properties of vendor-supplied Android products or simply ignore security. It is evident that vendors shirk on security investments in the latter case.

Regulators such as the U.S. Federal Trade Commission have sanctioned Android vendors for underinvestment in security, but the exact effects of these sanctions are difficult to disentangle with empirical data. Here, we model the impact of a regulator-imposed fine that incentivizes vendors to match a minimum security level. Interestingly, we show how product prices will decrease for the same cost of customization in the presence of a fine, or a higher level of regulator-imposed minimum security.

Type
Venue
22nd International Conference on Financial Cryptography and Data Security (FC 2018), 119–137
publications

Acceptance Rate: 26%

Authors

Aron Laszka
Associate Professor

Aron Laszka is an Associate Professor in the College of Information Sciences and Technology at the Pennsylvania State University. Previously, he was an Assistant Professor at the University of Houston, a Research Assistant Professor at Vanderbilt University, and a Postdoctoral Scholar at the University of California, Berkeley.

His research interests revolve around artificial intelligence and cyber-physical systems, focusing on machine learning for decision making and optimization, with applications in smart and connected communities, critical infrastructure, and cybersecurity.

His research is funded by the National Science Foundation, the Department of Energy, the Department of Transportation, and other agencies.