Free for All! Assessing User Data Exposure to Advertising Libraries on Android

Abstract

Many studies focused on detecting and measuring the security and privacy risks associated with the integration of advertising libraries in mobile apps. These studies consistently demonstrate the abuses of existing ad libraries. However, to fully assess the risks of an app that uses an advertising library, we need to take into account not only the current behaviors but all of the allowed behaviors that could result in the compromise of user data confidentiality. Ad libraries on Android have potential for greater data collection through at least four major channels: using unprotected APIs to learn other apps' information on the phone (e.g., app names); using protected APIs via permissions inherited from the host app to access sensitive information (e.g. Google and Facebook account information, geo locations); gaining access to files which the host app stores in its own protection domain; and observing user inputs into the host app.In this work, we systematically explore the potential reach of advertising libraries through these channels. We design a framework called Pluto that can be leveraged to analyze an app and discover whether it exposes targeted user data-such as contact information, interests, demographics, medical conditions and so on--to an opportunistic ad library. We present a prototype implementation of Pluto, that embodies novel strategies for using natural language processing to illustrate what targeted data can potentially be learned from an ad network using files and user inputs. Pluto also leverages machine learning and data mining models to reveal what advertising networks can learn from the list of installed apps. We validate Pluto with a collection of apps for which we have determined ground truth about targeted data they may reveal, together with a data set derived from a survey we conducted that gives ground truth for targeted data and corresponding lists of installed apps for about 300 users. We use these to show that Pluto, and hence also opportunistic ad networks, can achieve 75% recall and 80% precision for selected targeted data coming from app files and inputs, and even better results for certain targeted data based on the list of installed apps. Pluto is the first tool that estimates the risk associated with integrating advertising in apps based on the four available channels and arbitrary sets of targeted data.

Privacy and Security in Mobile Health: A Research Agenda

“…Second, apps commonly incorporate advertising libraries, which means they effectively share their privileges with advertisers, weakening the “least privilege” principle and opening the threat of privacy leakage via advertising libraries. 12 Research challenge: clarify threats to, and develop security and privacy protections for, smartphone apps that handle medical and health data—in particular, develop methods to isolate apps from advertisers.…”

Security Smells in Android

“…We rely on the Apktool to reverse engineer Android apk files and generate smali code. 11 We defined a set of rules to capture the symptoms of each security smell. In particular, we utilize Java XML Parser for parsing the Manifest files and use regular expressions to define and match the code pattern corresponding to the identified symptoms of each smell in the code.…”

Permissions snapshots: Assessing users' adaptation to the Android runtime permission model

“…Additionally, researchers demonstrated that users were often surprised by the ability of applications to collect personal data in the background and share data with third parties [8]. Advertising libraries for example have been consistently examined for data exposure and leakages; a recent study revealed a trend in advertising networks to become more aggressive in collecting reachable user data [9].…”