Sensors are widely used in modern mobile devices (e.g., smartphones, watches) and may gather abundant information from environments as well as about users, e.g., photos, sounds and locations. The rich set of sensor data enables various applications (e.g., health monitoring) and personalized apps as well. However, the powerful sensing abilities provide opportunities for attackers to steal both personal sensitive data and commercial secrets like never before. Unfortunately, the current design of smart devices only provides a coarse access control on sensors and does not have the capability to audit sensing. We argue that knowing how often the sensors are accessed and how much sensor data are collected is the first-line defense against sensor data breach. Such an ability is yet to be designed. In this paper, we propose a framework that allows users to acquire sensor data usages. In particular, we leverage a hook-based track method to track sensor accesses. Thus, with no need to change the source codes of the Android system and applications, we can intercept sensing operations to graphic sensors, audio sensors, location sensors, and standard sensors, and audit them from four aspects: flow audit, frequency audit, duration audit and invoker audit. Then, we implement a prototype, referred to as senDroid, which visually shows the quantitative usages of these sensors in real time at a performance overhead of [0.04-8.05] percent. senDroid allows Android users to audit the applications even when they bypass the Android framework via JNI invocations or when the malicious codes are dynamically loaded from the server side. Our empirical study on 1,489 popular apps in three well-known Android app markets shows that 26.32 percent apps access sensors when the apps are launched, and 11.01 percent apps access sensors while the apps run in the background. Furthermore, we analyze the relevance between sensor usage patterns and third-party libraries, and reverse-engineering on suspicious third-party libraries shows that 77.27 percent apps access sensors via third-party libraries. Our results call attentions to address the users' privacy concerns caused by sensor access.

Dynamic revocation of permissions of installed Android applications has been gaining popularity, because of the increasing concern of security and privacy in the Android platform. However, applications often crash or misbehave when their permissions are revoked, rendering applications completely unusable. Even though Google has officially introduced the new permission mechanism in Android 6.0 to explicitly support dynamic permission revocation, the issue still exists. In this paper, we conduct an empirical study to understand the latest application practice post Android 6.0. Specifically, we design a practical tool, referred to as revDroid, to help us to empirically analyze how often the undesirable side effects, especially application crash, can occur in off-the-shelf Android applications. From the analysis of 248 popular applications from Google Play Store, revDroid finds out that 70% applications and 46% permission-relevant calls do not appropriately catch exceptions caused by permission revocation, while third-party libraries pay much more attention to permission revocation. We also use revDroid to analyze 132 recent malware samples. The result shows that only 27% malwares and 36% permission-relevant API calls of malwares fail to consider the permission revocation. In fact, many of them perform specialized handling of permission revocation to keep the core malicious logic running. Finally, revDroid can be used to help developers uncover the unhandled permission revocations during development time and greatly improve the application quality.

Policy-based management is a very effective method to protect sensitive information. However, the overclaim of privileges is widespread in emerging applications, including mobile applications and social network services, because the applications' users involved in policy administration have little knowledge of policy-based management. The overclaim can be leveraged by malicious applications, then lead to serious privacy leakages and financial loss. To resolve this issue, this paper proposes a novel policy administration mechanism, referred to as collaborative policy administration (CPA for short), to simplify the policy administration. In CPA, a policy administrator can refer to other similar policies to set up their own policies to protect privacy and other sensitive information. This paper formally defines CPA and proposes its enforcement framework. Furthermore, to obtain similar policies more effectively, which is the key step of CPA, a text mining-based similarity measure method is presented. We evaluate CPA with the data of Android applications and demonstrate that the text mining-based similarity measure method is more effective in obtaining similar policies than the previous category-based method.