How people create and use information scraps, the small informal messages that people write to themselves to help them complete a task or remember something, has changed rapidly in the age of mobile computing. As recently as 2008, information scraps had continued to resist technological support. Since then, however, people have adopted mobile connected devices at a rate unimagined in the pre-smartphone era. Developers have, in turn, created a varied and growing body of smartphone software that supports many common information scrap use-cases. In this thesis, we describe our research into how and why people have adopted smartphone technology to serve their information scrap needs. The results of our survey show broad adoption of smartphones for many common information scrap tasks, particularly ones involving prospective memory. In addition, the results of our diary studies show that mobile contexts or locations are highly correlated with people's choosing to use smartphones to record information scraps. Our analysis of our diary study data also provides fresh understanding of the information scrap lifecycle and how mobile digital technology affects it. We find people's smartphone information scraps tend toward automatic archival, and we find their information scraps in general tend toward substantial role overlap regardless of medium. We use these findings to formulate a new information scrap lifecycle that is inclusive of mobile technology. These insights will help mobile technology creators to better support information scraps, which, in turn will allow users to enjoy the huge benefits of digital technology in their information scrap tasks. / Master of Science

Yes / A new generation of satellite systems that support regenerative on-board processors (OBPs) and multiple spot beam technology have opened new and efficient possibilities of implementing IP multicast communication over satellites. These new features have widened the scope of satellite-based applications and also enable satellite operators to efficiently utilize their allocated bandwidth resources. This makes it possible to provide cost effective satellite network services. IP multicast is a network layer protocol designed for group communication to save bandwidth resources and reduce processing overhead on the source side. The inherent broadcast nature of satellites, their global coverage (air, land, and sea), and direct access to a large number of subscribers imply satellites have unrivalled advantages in supporting IP multicast applications. IP mobility support in general and IP mobile multicast support in particular on mobile satellite terminals like the ones mounted on long haul flights, maritime vessels, continental trains, etc., still remain big challenges that have received very little attention from the research community. This paper proposes how Proxy Mobile IPv6 (PMIPv6)-based IP multicast mobility support defined for terrestrial networks can be adopted and used to support IP mobile multicast in future satellite networks, taking cognizance of the trend in the evolution of satellite communications.

<p>In the last decade, people have been surrounded by mobile devices such as smartphones, smartwatches, laptops, smart TVs, tablets, and IoT devices. As sensitive personal information such as photos, messages, contact information, schedules, and bank accounts are all stored on mobile devices today, the security and protection of such personal information are becoming more and more important. Today’s mobile devices are equipped with a variety of embedded sensors such as accelerometer, gyroscope, magnetometer, camera, GPS sensor, acoustic sensors, etc. that produce raw data on location, motion, and the environment around us. Based on these sensor data, we propose novel in-air signature authentication technologies on both smartphone and smartwatch in this dissertation. In-air signature authentication, as an essential behavioral biometric trait, has been adopted for identity verification and user authorization, as well as the development of deep neural networks, has vastly facilitated this field. This dissertation examines two challenging problems. One problem is how to deploy machine learning techniques to authenticate user in-air signatures in more convenient, intuitive, and secure ways by using smartphone and smartwatch in daily settings. Another problem is how to deal with the limited computational resources on today’s mobile devices which restrict to use machine learning models due to the substantial computational costs introduced by millions of parameters. </p> <p>To address the two above problems separately, we conduct the following research works. 1) The first work AirSign leverages both in-built acoustic and motion sensors on today’s smartphone for user authentication by signing signatures in the air without requiring any special hardware. This system actively transmits inaudible acoustic signals from the earpiece speaker, receives echoes back through both in-built microphones to “illuminate” signature and hand geometry, and authenticates users according to the unique features extracted from echoes and motion sensors. 2) The second work DeepWatchSign leverages in-built motion sensors on today’s smartwatch for user in-air signature authentication. The system adopts LSTM-AutoEncoder to generate negative signature data automatically from the enrolled signatures and authenticates each user by the deep neural network model. 3) We close this dissertation with an l0-based sparse group lasso approach called MobilePrune which can compress the deep learning models for both desktop and mobile platforms. This approach adopts group lasso penalty to enforce sparsity at the group level to benefit General Matrix Multiply (GEMM) and optimize the l0 norm in an exact manner. We observe the substantial reduction of compression ratio and computational costs for deep learning models. This method also achieves less response delay and battery consumption on mobile devices.</p>

In the past 10 years, the growth of machine learning technology has been significant, largely due to the availability of large datasets for training. However, gathering a sufficient amount of data on a central server can be challenging. Additionally, with the rise of mobile networking and the large amounts of data generated by IoT devices, privacy and security issues have become a concern, resulting in government regulations such as GDPR, HIPAA, CCPA, and ADPPA. Under these circumstances, traditional centralized machine learning methods face a problem in that sensitive data must be kept locally for privacy reasons, making it difficult to achieve the desired learning outcomes. Federated learning (FL) offers a solution to this by allowing for a global shared model to be trained by exchanging locally computed optimums instead of sharing the actual data. Despite its success as a natural solution for IoT machine learning implementation, Federated learning (FL) still faces challenges with regards to security and performance. These include high communication costs between IoT devices and the central server, the potential for sensitive information leakage and reduced model precision due to the aggregation process in the distributed IoT network, and performance concerns caused by the heterogeneity of data and devices in the network. In this dissertation, I present practical and effective techniques with strong theoretical supports to address these challenges. To optimize communication resources, I introduce a new multi-server FL framework called MS-FedAvg. To enhance security, I propose a robust defense algorithm called LoMar. To address data heterogeneity, I present FedLGA, and for device heterogeneity, I propose FedSAM.

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<p dir="ltr">The pervasive integration of mobile devices in today’s modern world, e.g., smartphones, IoT, and mixed-reality devices, has transformed various domains, enhancing user experiences, yet raising concerns about data security and privacy. Despite the implementation of various measures, such as permissions, to protect user privacy-sensitive data, vulnerabilities persist. These vulnerabilities pose significant threats to user privacy, including the risk of side-channel attacks targeting low-permission sensors. Additionally, the introduction of new permissions, such as the App Tracking Transparency framework in iOS, seeks to enhance user transparency and control over data sharing practices. However, these framework designs are accompanied by ambiguous developer guidelines, rendering them susceptible to deceptive patterns. These patterns can influence user perceptions and decisions, undermining the intended purpose of these permissions. Moreover, the emergence of new mobile technologies, e.g., mixed-reality devices, presents novel challenges in ensuring secure data sharing among multiple users in collaborative environments, while preserving usability.</p><p dir="ltr">In this dissertation, I focus on developing user-centered methods for enhancing the security and privacy of mobile system, navigating through the complexities of unsolicited data access strategies and exploring innovative approaches to secure device authentication and data sharing methodologies.</p><p dir="ltr">To achieve this, first, I introduce my work on the iStelan system, a three-stage side-channel attack. This method exploits the low-permission magnetometer sensor in smartphones to infer user sensitive touch data and application usage patterns. Through an extensive user study, I demonstrate the resilience of iStelan across different scenarios, surpassing the constraints and limitations of prior research efforts.</p><p dir="ltr">Second, I present my analysis and study on the App Tracking Transparency permission in iOS. Specifically, my work focuses on analyzing and detecting the dark patterns employed by app developers in the permission alerts to obtain user consent. I demonstrate my findings on the dark patterns observed in permission alerts on a large-scale of apps collected from Apple’s store, using both static and dynamic analysis methods. Additionally, I discuss the application of a between-subject user study to evaluate users’ perceptions and understanding when exposed to different alert patterns.</p><p dir="ltr">Lastly, I introduce StareToPair, a group pairing system that leverages multi-modal sensing technologies in mixed-reality devices to enable secure data sharing in collaborative settings. StareToPair employs a sophisticated threat model capable of addressing various real-world scenarios, all while ensuring high levels of scalability and usability.</p><p dir="ltr">Through rigorous investigation, theoretical analysis and user studies, my research endeavors enhance the field of security and privacy for mobile systems. The insights gained from these studies offer valuable guidance for future developments in mobile systems, ultimately contributing to the design of user-centered secure and privacy-aware mobile ecosystems.</p>

Machine learning has become one of the most emerging topics in a lot of research areas, such as pervasive and ubiquitous computing. Such computing applications always rely on the supervised learning approach to recognize user’s context before a suitable level of services are provided. However, since more and more users are involved in modern applications, the monitored data cannot be guaranteed to be always true due to wrong information. This may cause the mislabeling in machine learning and so affects the prediction. The goal of this Ph.D. thesis is to improve the data quality and solve the mislabeling problem caused by considering non-expert users. To achieve this goal, we propose a novel algorithm, called Skeptical Learning, aiming at interacting with the users and filtering out anomalies when an invalid input is monitored. This algorithm guarantees the machine to use the pre-known knowledge to check the availability of its own prediction as well as the label provided by the users. This thesis clarifies how we design this algorithm and makes three main contributions: (i.) we study the predictability of human behavior through the notion of personal context; (ii.)we design and develop Skeptical Learning as a paradigm to deal with the unreliability of users when providing non-confidential labels that describe their personal context; (iii.) we introduce an MCS platform where we implement Skeptical Learning on top of it to solve unreliable labels issue. Our evaluations have shown that Skeptical Learning could be widely used in pervasive and ubiquitous computing applications to better understand the quality of the data relying on the machine knowledge, and thus prevent mislabeling problem due to non-expert information.