Traditionally, and up to this date, Land Mobile Radio (LMR) systems have been interconnected via leased lines and microwave links across circuit-switched networks. With the recent deployment of digital LMR standards such as the Association of Public and Communications Officials (APCO) Project 25 and the Terrestrial Trunked Radio (TETRA), traffic exchange has become more bursty and non-uniform, and as such, less suitable for circuit-switched networks. This thesis proposes a framework for a packet-switched Inter-System Interface (ISI) for LMR systems. Packet-switched networks have the advantage of supporting traffic integration, utilize capacity efficiently, scale easily and seamlessly, and eliminate single points of failure by providing a distributed architecture. Session Initiation Protocol (SIP) signaling messages are defined for setting up and tearing down unit-to-unit calls across the ISI. The Session Description Protocol (SDP) is used to describe how the voice calls are encoded. Voice packets are exchanged between LMR users using the Real-Time Transport Protocol (RTP). Based on the proposed framework, we develop a simulation model to investigate the performance of the ISI when different numbers of LMR users try to establish unit-to-unit calls across the packet-switched ISI. Three packet transport technologies providing Wide Area Network (WAN) connectivity are considered, IP, ATM, and Frame Relay. The results indicate that a packet-switched ISI can take advantage of statistical multiplexing techniques to distribute network resources more efficiently. Quantitative results are obtained for throughput and link utilization. When using an access link providing T1 service, we show that the End-To-End (ETE) delay, and delay variation can be controlled at levels capable of supporting the timely delivery of real-time voice packets. Assuming link utilization is maintained below 100%, the maximum ETE delay experienced in all three packet transport technologies considered is 58 ms and the maximum call setup time is less than 300 ms. An ATM WAN provides the best performance for all time-dependent metrics considered, i.e., ETE delay, delay variation, and call setup time. An IP WAN provides the highest bandwidth efficiency. Selecting the appropriate packet transport technology for the WAN is a tradeoff between the delay that can be tolerated by the voice packets traversing the LMR network and the cost of bandwidth on the access link. / Master of Science

Spurred by change in government regulations and to leverage lower-cost technology and services, many land mobile radio (LMR) operators have begun transitioning from circuit-switched to packet-switched backbone networks to handle their future communication needs. Due to the unique demands of packet-switched backbone networks for LMR, it may not be wise to carry over the previously implemented security methods used with circuit-switch systems or to treat an LMR backbone as a regular packet-switched network. This thesis investigates security in packet-switched LMR backbone networks to identify security issues in packet-switched LMR networks and provide possible solutions for them. Security solutions that are examined include different types of virtual private networks (VPNs), various encryption and keying procedures for safe communication, and logic behind how and where to implement security functions within the network. Specific schemes examined include IP Security (IPSec), OpenVPN, Virtual Tunnel (VTun), and Zebedee. I also present a quantitative analysis of the effects that the solutions have on packet-switched networks, in terms of link utilization, and on voice traffic, in terms of delay and delay jitter. In addition, I evaluate, in general terms, the additional cost or complexity that is introduced by the different security solutions. Simulation with OPNET Modeler was used to evaluate how the various security schemes affect voice communication and network performance as a whole. Since OPNET Modeler does not provide models of security functions, the source code of the transceiver system models was modified to introduce additional overhead that is representative of the various security solutions. Through experimentation, simulation, and analysis of the security schemes considered, it was found that the most effective security scheme overall for a packet-switched LMR backbone network would either be IPSec or OpenVPN implemented at the base stations and end-hosts. Both security schemes provide strong encryption, flexibility, and are actively supported. However, if bandwidth is scarce and flexibility is less important, then a security solution with less overhead, such as VTun, should be considered. Thus, one has to balance performance with security to choose the most effective security solution for a particular application. / Master of Science

The deployment of human-augmented machine learning (ML) systems has become a recommended organizational best practice. ML systems use algorithms that rely on training data labeled by human annotators. However, human involvement in reviewing and labeling consumers' voice data to train speech recognition systems for Amazon Alexa, Microsoft Cortana, and the like has raised privacy concerns among consumers and privacy advocates. We use the enhanced APCO model as the theoretical lens to investigate how the disclosure of human involvement during the supervised machine learning process affects consumers' privacy decision making. In a scenario-based experiment with 499 participants, we present various company privacy policies to participants to examine their trust and privacy considerations, then ask them to share reasons why they would or would not opt in to share their voice data to train a companies' voice recognition software. We find that the perception of human involvement in the ML training process significantly influences participants' privacy-related concerns, which thereby mediate their decisions to share their voice data. Furthermore, we manipulate four factors of a privacy policy to operationalize various cognitive biases actively present in the minds of consumers and find that default trust and salience biases significantly affect participants' privacy decision making. Our results provide a deeper contextualized understanding of privacy-related concerns that may arise in human-augmented ML system configurations and highlight the managerial importance of considering the role of human involvement in supervised machine learning settings. Importantly, we introduce perceived human involvement as a new construct to the information privacy discourse. Although ubiquitous data collection and increased privacy breaches have elevated the reported concerns of consumers, consumers' behaviors do not always match their stated privacy concerns. Researchers refer to this as the privacy paradox, and decades of information privacy research have identified a myriad of explanations why this paradox occurs. Yet the underlying crux of the explanations presumes privacy concern to be the appropriate proxy to measure privacy attitude and compare with actual privacy behavior. Often, privacy concerns are situational and can be elicited through the setup of boundary conditions and the framing of different privacy scenarios. Drawing on the cognitive model of empowerment and interest, we propose a multidimensional privacy interest construct that captures consumers' situational and dispositional attitudes toward privacy, which can serve as a more robust measure in conditions leading to the privacy paradox. We define privacy interest as a consumer's general feeling toward reengaging particular behaviors that increase their information privacy. This construct comprises four dimensions—impact, awareness, meaningfulness, and competence—and is conceptualized as a consumer's assessment of contextual factors affecting their privacy perceptions and their global predisposition to respond to those factors. Importantly, interest was originally included in the privacy calculus but is largely absent in privacy studies and theoretical conceptualizations. Following MacKenzie et al. (2011), we developed and empirically validated a privacy interest scale. This study contributes to privacy research and practice by reconceptualizing a construct in the original privacy calculus theory and offering a renewed theoretical lens through which to view consumers' privacy attitudes and behaviors. / Doctor of Philosophy / The deployment of human-augmented machine learning (ML) systems has become a recommended organizational best practice. ML systems use algorithms that rely on training data labeled by human annotators. However, human involvement in reviewing and labeling consumers' voice data to train speech recognition systems for Amazon Alexa, Microsoft Cortana, and the like has raised privacy concerns among consumers and privacy advocates. We investigate how the disclosure of human involvement during the supervised machine learning process affects consumers' privacy decision making and find that the perception of human involvement in the ML training process significantly influences participants' privacy-related concerns. This thereby influences their decisions to share their voice data. Our results highlight the importance of understanding consumers' willingness to contribute their data to generate complete and diverse data sets to help companies reduce algorithmic biases and systematic unfairness in the decisions and outputs rendered by ML systems. Although ubiquitous data collection and increased privacy breaches have elevated the reported concerns of consumers, consumers' behaviors do not always match their stated privacy concerns. This is referred to as the privacy paradox, and decades of information privacy research have identified a myriad of explanations why this paradox occurs. Yet the underlying crux of the explanations presumes privacy concern to be the appropriate proxy to measure privacy attitude and compare with actual privacy behavior. We propose privacy interest as an alternative to privacy concern and assert that it can serve as a more robust measure in conditions leading to the privacy paradox. We define privacy interest as a consumer's general feeling toward reengaging particular behaviors that increase their information privacy. We found that privacy interest was more effective than privacy concern in predicting consumers' mobilization behaviors, such as publicly complaining about privacy issues to companies and third-party organizations, requesting to remove their information from company databases, and reducing their self-disclosure behaviors. By contrast, privacy concern was more effective than privacy interest in predicting consumers' behaviors to misrepresent their identity. By developing and empirically validating the privacy interest scale, we offer interest in privacy as a renewed theoretical lens through which to view consumers' privacy attitudes and behaviors.