With a growing demand for spectrum availability, spectrum sharing has become a high-profile solution to overcrowding. In order to enable spectrum sharing between incumbent/primary and secondary users, incumbents must have spectrum protection and privacy from malicious new entrants. In this Spectrum Access System (SAS) advancement, Primary Users (PUs) are obfuscated with the efforts of the SAS and the cooperation of obedient new entrants. Further, the necessary changes to the SAS to support this privacy scheme are exposed to suggest improvements in PU privacy, Citizens Broadband Radio Service Device (CBSD)-SAS relations, and punishment for unauthorized transmission. Results show the feasibility for PU obfuscation with respect to malicious spectrum sensing users. Simulation results indicate that the obfuscation scheme can deliver location and frequency occupation privacy with 75% and 66% effectiveness respectively in a 100% efficient spectrum utilization oriented obfuscation scheme. A scheme without spectrum utilization constraint shows up to 91% location privacy effectiveness. Experiment trials indicate that the privacy tactic can be implemented on an open-source SAS, however environmental factors may degrade the tactic's performance. / Master of Science / With a growing demand for spectrum availability, wireless spectrum sharing has become a high-profile solution to spectrum overcrowding. In order to enable spectrum sharing between incumbent/primary (e.g.,federal communications, naval radar, users already grandfathered into the band) and secondary users (e.g., commercial communications companies), incumbents must have spectrum protection and privacy from malicious new entrants. In this Spectrum Access System (SAS) advancement, Primary Users (PUs) are obfuscated with the efforts of the incumbent informed SAS and the cooperation of obedient new entrants. Further, the necessary changes to the SAS to support this privacy scheme are exposed to suggest improvements in PU privacy, Citizens Broadband Radio Service Device (CBSD)-SAS relations, and punishment for unauthorized transmission. Results show the feasibility of PU obfuscation with respect to malicious spectrum sensing users. Simulation results indicate that the obfuscation tactic can deliver location and frequency occupation privacy with 75% and 66% effectiveness respectively in a 100% efficient spectrum utilization oriented obfuscation scheme. A scheme without spectrum utilization constraint shows up to 91% location privacy effectiveness. Experiment trials indicate that the privacy tactic can be implemented on an open-source SAS, however environmental factors may degrade the tactic's performance.

Cloud Computing represents a recent paradigm shift that enables users to share and remotely access high-powered computing resources (both infrastructure and software/services) contained in off-site data centers thereby allowing a more efficient use of hardware and software infrastructures. This growing trend in cloud computing, combined with the demands for Big Data and Big Data analytics, is driving the rapid evolution of datacenter technologies towards more cost-effective, consumer-driven, more privacy conscious and technology agnostic solutions. This dissertation is dedicated to taking a systematic approach to develop system-level techniques and algorithms to tackle the challenges of large-scale data processing in the Cloud and scaling and delivering privacy-aware services with anytime-anywhere availability. We analyze the key challenges in effective provisioning of Cloud services in the context of MapReduce-based parallel data processing considering the concerns of cost-effectiveness, performance guarantees and user-privacy and we develop a suite of solution techniques, architectures and models to support cost-optimized and privacy-preserving service provisioning in the Cloud. At the cloud resource provisioning tier, we develop a utility-driven MapReduce Cloud resource planning and management system called Cura for cost-optimally allocating resources to jobs. While existing services require users to select a number of complex cluster and job parameters and use those potentially sub-optimal per-job configurations, the Cura resource management achieves global resource optimization in the cloud by minimizing cost and maximizing resource utilization. We also address the challenges of resource management and job scheduling for large-scale parallel data processing in the Cloud in the presence of networking and storage bottlenecks commonly experienced in Cloud data centers. We develop Purlieus, a self-configurable locality-based data and virtual machine management framework that enables MapReduce jobs to access their data either locally or from close-by nodes including all input, output and intermediate data achieving significant improvements in job response time. We then extend our cloud resource management framework to support privacy-preserving data access and efficient privacy-conscious query processing. Concretely, we propose and implement VNCache: an efficient solution for MapReduce analysis of cloud-archived log data for privacy-conscious enterprises. Through a seamless data streaming and prefetching model in VNCache, Hadoop jobs begin execution as soon as they are launched without requiring any apriori downloading. At the cloud consumer tier, we develop mix-zone based techniques for delivering anonymous cloud services to mobile users on the move through Mobimix, a novel road-network mix-zone based framework that enables real time, location based service delivery without disclosing content or location privacy of the consumers.