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Apache Kafka is a distributed, high throughput and fault-tolerant publish/subscribe messaging system in the Hadoop ecosystem. It is used as a distributed data streaming and processing platform. Kafka topics are the units of message feeds in the Kafka cluster. Kafka producer publishes messages into these topics and a Kafka consumer subscribes to topics to pull those messages. With the increased usage of Kafka in the data infrastructure of many companies, there are many Kafka clients that publish and consume messages to/from the Kafka topics. In fact, these client operations can be malicious. To mitigate this risk, clients must authenticate themselves and their operation must be authorized before they can access to a given topic. Nowadays, Kafka ships with a pluggable Authorizer interface to implement access control list (ACL) based authorization for client operation. Kafka users can implement the interface differently to satisfy their security requirements. SimpleACLAuthorizer is the out-of-box implementation of the interface and uses a Zookeeper for ACLs storage.HopsWorks, based on Hops a next generation Hadoop distribution, provides support for project-based multi-tenancy, where projects are fully isolated at the level of the Hadoop Filesystem and YARN. In this project, we added Kafka topicbased multi-tenancy in Hops projects. Kafka topic is created from inside Hops project and persisted both at the Zookeeper and the NDBCluster. Persisting a topic into a database enabled us for topic sharing across projects. ACLs are added to Kafka topics and are persisted only into the database. Client access to Kafka topics is authorized based on these ACLs. ACLs are added, updated, listed and/or removed from the HopsWorks WebUI. HopsACLAuthorizer, a Hops implementation of the Authorizer interface, authorizes Kafka client operations using the ACLs in the database. The Apache Avro schema registry for topics enabled the producer and consumer to better integrate by transferring a preestablished message format. The result of this project is the first Hadoop distribution that supports Kafka multi-tenancy.

In the contemporary digital landscape, ensuring secure communication amid widespread data exchange is imperative. This dissertation focuses on enhancing the security and privacy of end-to-end encryption (E2EE) applications while maintaining or improving usability. The dissertation first investigates and proposes improvements in two areas of existing E2EE applications: countering man-in-the-middle and impersonation attacks through automated key verification and studying user perceptions of cryptographic deniability. Insights from privacy-conscious users reveal concerns about the lack of E2EE support, app siloing, and data accessibility by client apps. To address these issues, we propose an innovative user-controlled encryption system, enabling encryption before data reaches the client app. Finally, the dissertation evaluates local threats in the FIDO2 protocol and devises defenses against these risks. Additionally, it explores streamlining FIDO2 authentication management across multiple websites for user convenience and security.

Nicotine, in the form of cigarette smoking, chewing tobacco, and most recently as vapor smoking, is one of the most heavily used addictive drugs in the world. Since smoking imposes a significant health-care and economic burden on the population, there have been sustained and significant efforts for the past several decades to control it. However, smoking epidemic is a complex and "policy-resistant" problem that has proven difficult to control. Despite the known importance of social networks in the smoking epidemic, there has been no network-centric intervention available for controlling the smoking epidemic yet. The long-term goal of this work is the development and implementation of an environment needed for developing network-centric interventions for controlling the smoking contagion. In order to develop such an environment we essentially need: an operationalized model of smoking that can be simulated, to determine the role of online social networks on smoking behavior, and actual methods to perform network-centric interventions. The objective of this thesis is to take first steps in all these categories. We perform Twitter-based surveillance of smoking-related tweets, and use mathematical modeling and simulation techniques to achieve our objective. Specifically, we use Twitter data to infer sentiments on smoking and electronic cigarettes, to estimate the proportion of user population that gets exposed to smoking-related messaging that is underage, and to identify statistically anomalous clusters of counties where people discuss about electronic cigarette a lot more than expected. In other work, we employ mathematical modeling and simulation approach to study how different factors such as addictiveness and peer-influence together contribute to smoking behavior diffusion, and also develop two methods to stymie social contagion. This lead to a total of four smoking contagion-related studies. These studies are just a first step towards the development of a network-centric intervention environment for controlling smoking contagion, and also to show that such an environment is realizable. / Ph. D.

This dissertation examines the predictability of user engagement on online social platforms by integrating theoretical perspectives from the literature on media and technology habits with principles of context-aware computing. It presents three studies, each targeting a different facet of technology-mediated communication, from social media use in general to more granular behaviors like active and passive use and instant messaging. The first chapter proposes a novel approach to the study of social media habits through predictive modeling of sequential smartphone user behaviors. Using longitudinal smartphone app log data, it examines the predictability of app engagement as a way to capture a critical yet previously neglected aspect of media and technology habits: their embeddedness in repetitive behavioral sequences. The study employs Long Short-Term Memory (LSTM) and transformer neural networks to demonstrate that social media use follows predictable patterns over time and that its predictability varies substantially across individuals. T he second chapter shifts focus to the potential of context-aware modeling as a holistic yet parsimonious and privacy-preserving approach to predicting user engagement on online social platforms. Analyzing over 100 million Snapchat sessions from nearly 80,000 users via deep LSTM neural networks, the study demonstrates the predictability of active and passive use based on past behavior and a notable improvement in predictive performance upon integrating momentary context information. Features related to connectivity status, location, temporal context, and weather were found to capture non-redundant variance in user engagement relative to features derived from histories of in-app behaviors. The findings are consistent with the idea of context-contingent, habit-driven patterns of active and passive use, highlighting the utility of contextualized representations of user behavior for predicting user engagement on online social platforms. The third chapter investigates the predictability of attentiveness and responsiveness in instant messaging on a large online social platform. Utilizing metadata from over 19 million messages, the study examines the predictive power of a wide range of predictor groups, including message attributes, user attributes, and momentary context, as well as historical communication patterns within ego networks and dyadic relationships. The findings echo the overarching theme that habitual behaviors and contextual factors shape user engagement. However, in this case, dyad-specific messaging histories account for the overwhelming share of explained variance, underlining the socially interdependent nature of user engagement in instant messaging. Collectively, the three studies presented in this dissertation make a theoretical contribution by establishing media and technology habits as a suitable framework for the study of user engagement and by introducing a novel perspective that emphasizes the repetitive, predictable, and context-dependent nature of media and technology habits. The research makes an important empirical contribution through the use of novel, large-scale, objective behavioral data, enhancing the ecological validity and real-world applicability of its findings. Methodologically, it pioneers the use of context-aware sequential machine learning techniques for the study of media and technology habits. The insights garnered from this research have the potential to inform the design of engaging and ethical online social platforms and mobile technologies, highlighting its practical implications for the billions of users navigating these digital environments on a daily basis.

Popularity of mobile devices along with the presence of ad hoc networks requiring no infrastructure has contributed to recent advances in the field of mobile computing in ad hoc networks. Mobile ad hoc networks have been mostly utilized in military environments. The recent advances in ad hoc network technology now introduce a new class of applications. In this thesis, we design, implement and analyze a multi-hop ad hoc messenger application using Pocket PCs and Microsoft .Net Compact Framework. Pocket PCs communicate wirelessly with each other using the IEEE 802.11b technology without the use of an infrastructure. The main protocol implemented in this application is based on Dynamic Source Routing (DSR), which consists of two important mechanisms, Route Discovery and Route Maintenance. We adopt DSR since DSR operates solely based on source routing and "on-demand" process, so each packet does not have to transmit any periodic advertisement packets or routing information. These characteristics are desirable for the ad hoc messenger application for which a conversation is source-initiated on-demand. To test our application easily, we have developed a testing strategy by which a mobility configuration file is pre-generated describing the mobility pattern of each node generated based on the random waypoint mobility model. A mobility configuration file thus defines topology changes at runtime and is used by all nodes to know whether they can communicate with others in a single-hop or multi-hops during an experimental run. We use five standard metrics to test the performance of the wireless ad hoc messenger application implemented based on DSR, namely, (1) average latency to find a new route, (2) average latency to deliver a data packet, (3) delivery ratio of data packets, (4) normalized control overhead, and (5) throughput. These metrics test the correctness and efficiency of the wireless ad hoc messenger application using the DSR protocol in an 802.11 ad hoc network that imposes limitations on bandwidth and resources of each mobile device. We test the effectiveness of certain design alternatives for implementing the ad hoc messenger application with these five metrics under various topology change conditions by manipulating the speed and pause-time parameters in the random waypoint model. The design alternatives evaluated include (1) Sliding Window Size (SWS) for end-to-end reliable communication control; (2) the use of per-hop acknowledgement packets (called receipt packets) deigned for rapid detection of route errors by intermediate nodes; and (3) the use of cache for path look-up during route discovery and maintenance. Our analysis results indicate that as the node speed increases, the system performance deteriorates because a higher node speed causes the network topology to change more frequently under the random waypoint mobility model, causing routes to be broken. On the other hand, as the pause time increases, the system performance improves due to a more stable network topology. For the design alternatives evaluated in our wireless ad hoc messenger, we discover that as SWS increases, the system performance also increases until it reaches an optimal SWS value that maximizes the performance due to a balance of a higher level of data parallelism introduced and a higher level of medium contention in 802.11 because of more packets being transmitted simultaneously as SWS increases. Beyond the optimal SWS, the system performance deteriorates as SWS increases because the heavy medium contention effect outweighs the benefit due to data parallelism. We also discover that the use of receipt packets is helpful in a rapidly changing network but is not beneficial in a stable network. There is a break-even point in the frequency of topology changes beyond which the use of receipt packets helps quickly detect route errors in a dynamic network and would improve the system performance. Lastly, the use of cache is rather harmful in a frequently changing network because stale information stored in the cache of a source node may adversely cause more route errors and generate a higher delay for the route discovery process. There exists a break-even point beyond which the use of cache is not beneficial. Our wireless ad hoc messenger application can be used in a real chatting setting allowing Pocket PC users to chat instantly in 802.11 environments. The design and development of the dynamic topology simulation tool to model movements of nodes and the automatic testing and data collection tool to facilitate input data selection and output data analysis using XML are also a major contribution. The experimental results obtained indicate that there exists an optimal operational setting in the use of SWS, receipt packets and cache, suggesting that the wireless ad hoc messenger should be implemented in an adaptive manner to fine-tune these design parameters based on the current network condition and performance data monitored to maximize the system performance. / Master of Science

It is crucial to understand how patient portals are used and adopted among different population groups. This dissertation follows a traditional 5-chapter format that includes three studies with the results of each study presented in an essay format. The first essay provides a systematic literature review of existing research on patient portal adoption, barriers, and predictors. This review reveals a gap in knowledge regarding emerging adults who are transitioning from adult care to self-care settings. The second essay presents an emerging adult patient portal behavioral model, which identifies the factors that impact patient portal usage. Finally, the third essay focuses on patient's trust in providers in secure messaging, which is one of the features available through patient portals. The results of Essay 1 revealed gaps in the literature, highlighting the need for understanding the perceptions of different subgroups of the population towards patient portals to promote their meaningful use. The findings from Essay 2 show that perceived risk and perceived usefulness are significant determinants affecting the behavioral intentions of emerging adults toward the usage of patient portals. Essay 3 describes how information reliability, structural assurance, persuasiveness, perceived ease-of-use, and patients' trusting beliefs in providers are related to the patient's intentions to use secure messaging and patient portals. In addition to addressing existing research gaps, the results of the research in this dissertation inform healthcare providers and developers on how to improve patient portal adoption. By identifying the factors that impact patient portal usage, healthcare providers can optimize the benefits of patient portals for patients and healthcare organizations. Additionally, understanding patients' trust in providers in secure messaging can help improve communication and further lead to better health outcomes.