While mobile apps often need to transmit sensitive information out to support various functionalities, they may also abuse the privilege by leaking the data to unauthorized third parties. This makes us question: Is the given transmission required to fulfill the app functionality? In this paper, we make the first attempt to automatically identify suspicious transmissions from app visual interfaces, including app names, descriptions, and user interfaces. We design and implement a novel framework called FlowIntent to detect nonfunctional transmissions at both software and network levels. During the exercising of the given apps, FlowIntent automatically detects privacy-sharing transmissions and determines their purposes by utilizing the fact that mobile users rely on visible app interface to perceive the functionality of the app at certain context. The characterizations of nonfunctional network traffic are then summarized to provide network level protection. FlowIntent not only reduces the false alarms caused by traditional taint analysis, but also captures the sensitive transmissions missed by widely-used taint analysis system TaintDroid. Evaluation using 2125 sharing flows collected from more than a thousand running instances shows that our approach achieves about 94 percent accuracy in detecting nonfunctional transmissions.

Estimation of building occupancy has emerged as an important research problem with applications ranging from building energy efficiency, control and automation, safety, communication network resource allocation, etc. In this research work, we propose the estimation of occupancy using non-intrusive information that is already available from existing sensing modes, namely, number of WiFi devices, electrical energy demand and water consumption rate. Using data collected from 76 buildings in a university campus, we study the feasibility of multi-modal fusion between the three data sources for estimating fine-grained occupancy. In order to make the estimation model scalable, we propose three different clustering schemes to identify similarity in building characteristics and training per-cluster occupancy estimation models. The presented multi-modal fusion estimation framework achieves a mean absolute percentage error of 13.22% and we find that leveraging all three modalities provide an improvement of 48% in accuracy as compared to WiFi-only occupancy estimation. Our evaluation also shows that clustering buildings greatly increases the scalability of the proposed approach through significant reduction in training overhead, while providing an accuracy comparable to exhaustive, per-building estimation models.

The exponential growth of mobile devices has raised concerns about sensitive data leakage. In this paper, we make the first attempt to identify suspicious location-related HTTP transmission flows from the user's perspective, by answering the question: Is the transmission user-intended? In contrast to previous network-level detection schemes that mainly rely on a given set of suspicious hostnames, our approach can better adapt to the fast growth of app market and the constantly evolving leakage patterns. On the other hand, compared to existing system-level detection schemes built upon program taint analysis, where all sensitive transmissions as treated as illegal, our approach better meets the user needs and is easier to deploy. In particular, our proof-of- concept implementation (FlowIntent) captures sensitive transmissions missed by TaintDroid, the state-of-the-art dynamic taint analysis system on Android platforms. Evaluation using 1002 location sharing instances collected from more than 20,000 apps shows that our approach achieves about 91% accuracy in detecting illegitimate location transmissions.

The number of wireless-enabled devices owned by a user has had huge growth over the past few years. Over one third of adults in the United States currently own three wireless devices: a smartphone, laptop, and tablet. This article provides a study of the network usage behavior of today’s multidevice users. Using data collected from a large university campus, we provide a detailed multidevice user (MDU) measurement study of more than 30,000 users. The major objective of this work is to study how the presence of multiple wireless devices affects the network usage behavior of users. Specifically, we characterize the usage pattern of the different device types in terms of total and intermittent usage, how the usage of different devices overlap over time, and uncarried device usage statistics. We also study user preferences of accessing sensitive content and device-specific factors that govern the choice of WiFi encryption type. The study reveals several interesting findings about MDUs. We see how the use of tablets and laptops are interchangeable and how the overall multidevice usage is additive instead of being shared among the devices. We also observe how current DHCP configurations are oblivious to multiple devices, which results in inefficient utilization of available IP address space. All findings about multidevice usage patterns have the potential to be utilized by different entities, such as app developers, network providers, security researchers, and analytics and advertisement systems, to provide more intelligent and informed services to users who have at least two devices among a smartphone, tablet, and laptop.

Mobile advertisements have become the dominant source of revenue for mobile application developers, advertisers and brokers. Using novel sensing techniques and the advanced sensors of mobile devices, it has become feasible to determine a user's fine-grained context such as her location, activity, and interests. This information can be used by the advertisement (ad) brokers to provide more relevant ads to the user based on her context. However, this has led to serious privacy risks, since a user can be tracked by the broker or an adversary based on her context. In this paper, we present AnonAd, an ad delivery scheme that allows users to protect their privacy when receiving micro-targeted ads from the broker. AnonAd utilizes the encryption of the user's context based on a split-secret scheme that guarantees that the broker can decrypt the context only when there exists k other users in the same context. This way, a user's privacy is protected with k-anonymity during the context report. We show that the split-secret scheme integrates seamlessly with existing homomorphic encryption-based schemes that can provide differential privacy for ad click reports. We implement AnonAd on Android smartphones and evaluate it with real users as well as simulated users that follow real mobility traces. Our results show that AnonAd achieves a balance between user's privacy and relevancy of advertisements without the requirement of any additional proxy servers.

There has been a huge growth in the number of wireless-enabled devices possessed by a user. Over two third of adults in United States currently own three devices - laptop, smartphone and tablet. In this paper, we provide a first look at the network usage behavior of today's multi-device users. Using the data collected from a large university campus, we provide a detailed measurement-based characterization study of over 30,000 users. Our objective is to understand how existence of multiple wireless devices affect the network usage behavior of users. Specifically, we study the usage pattern of devices, how the usage of difference devices overlap in time, user's preferences of accessing sensitive content and device-specific factors that govern their choice of WiFi encryption type. The study reveals numerous interesting findings such as how current DHCP configurations are oblivious to multiple devices which results in inefficient utilization of available IP address space.

The rise of location-based services has enabled many opportunities for content service providers to optimize the content delivery based on user's location. Since sharing precise location remains a major privacy concern among the users, many location-based services rely on contextual location (e.g. residence, cafe etc.) as opposed to acquiring user's exact physical location. In this paper, we present PACL (Privacy-Aware Contextual Localizer), which can learn user's contextual location just by passively monitoring user's network traffic. PACL can discern a set of vital attributes (statistical and application-based) from user's network traffic, and predict user's contextual location with a very high accuracy. We design and evaluate PACL using real-world network traces of over 1700 users with over 100 gigabytes of total data. Our results show that PACL (built using decision tree) can predict user's contextual location with the accuracy of around 87%.

We address the problem of controlling a team of robots subject to constraints on relative positions. We adopt the general framework of leader-follower control in which a network of controllers is used to control the position and orientation of the team and its shape. We propose two improvements to this scheme. First, we introduce cooperative leader-following where the motion of a robot is determined not only by its leader, but also by other robots including their followers. Second, we allow constraints that are induced by limitations on ranges of sensors and wireless network cards. Our approach is based on potential field controllers for each robot and the on-line modification of these controllers to accommodate motion constraints induced by other robots in the group. We present experimental results with a team of three car-like robots equipped with omnidirectional cameras and 802.11b network cards.

We describe a framework for cooperative control of a group of nonholonomic mobile robots that allows us to build complex systems from simple controllers and estimators. The resultant modular approach is attractive because of the potential for reusability. Our approach to composition also guarantees stability and convergence in a wide range of tasks. There are two key features in our approach: 1) a paradigm for switching between simple decentralized controllers that allows for changes in formation; 2) the use of information from a single type of sensor, an omnidirectional camera, for all our controllers. We describe estimators that abstract the sensory information at different levels, enabling both decentralized and centralized cooperative control. Our results include numerical simulations and experiments using a testbed consisting of three nonholonomic robots.

This paper considers the problem of vision-based control of a nonholonomic mobile robot. We describe the design and implementation of real-time estimation and control algorithms on a car-like robot platform using a single omni-directional camera as a sensor without explicit use of odometry. We provide experimental results for each of these vision-based control objects. The algorithms are packaged as control modes and can be combined hierarchically to perform higher level tasks involving multiple robots.

We describe a framework for controlling a group of nonholonomic mobile robots equipped with range sensors. The vehicles are required to follow a prescribed trajectory while maintaining a desired formation. By using the leader-following approach, we formulate the formation control problem as a hybrid (mode switching) control system. We then develop a decision module that allows the robots to automatically switch between continuous-state control laws to achieve a desired formation shape. The stability properties of the closed-loop hybrid system are studied using the Lyapunov theory. We do not use explicit communication between robots; instead we integrate optimal estimation techniques with nonlinear controllers. Simulation and experimental results verify the validity of our approach.

We describe a framework for coordinating multiple robots in cooperative manipulation tasks in which vision is used for establishing relative position and orientation and maintaining formation. The two key contributions are a cooperative scheme for localizing the robots based on visual imagery that is more robust than decentralized localization, and a set of control algorithms that allow the robots to maintain a prescribed formation (shape and size). The ability to maintain a prescribed formation allows the robots to "trap" objects in their midst, and to "flow" the formation to a desired position. We derive the cooperative localization and control algorithms and present experimental results that illustrate the implementation and the performance of these algorithms.

We propose a hierarchical structure for control of autonomous robots. We draw on ideas from the hybrid systems theory, which describe systems governed by a discrete set of continuous modes of operation and transitions between these modes. We aim to enable rapid development of test applications and to allow easy performance upgrades. Our structure is based on the creation of agents to process inputs and outputs at the highest level of abstraction in the system (the application domain). We describe our implementation of this structure in two systems: a nonholonomic car-like robot and a quadrupedal entertainment robot. Both examples demonstrate how abstraction of planning away from the implementation details of the robot enables rapid development.