Secure OFDM System Design for Wireless Communications

Wireless communications is widely employed in modern society and plays an increasingly important role in people\u27s daily life. The broadcast nature of radio propagation, however, causes wireless communications particularly vulnerable to malicious attacks, and leads to critical challenges in securing the wireless transmission. Motivated by the insufficiency of traditional approaches to secure wireless communications, physical layer security that is emerging as a complement to the traditional upper-layer security mechanisms is investigated in this dissertation. Five novel techniques toward the physical layer security of wireless communications are proposed. The first two techniques focus on the security risk assessment in wireless networks to enable a situation-awareness based transmission protection. The third and fourth techniques utilize wireless medium characteristics to enhance the built-in security of wireless communication systems, so as to prevent passive eavesdropping. The last technique provides an embedded confidential signaling link for secure transmitter-receiver interaction in OFDM systems

Visual region understanding: unsupervised extraction and abstraction

The ability to gain a conceptual understanding of the world in uncontrolled environments is the ultimate goal of vision-based computer systems. Technological societies today are heavily reliant on surveillance and security infrastructure, robotics, medical image analysis, visual data categorisation and search, and smart device user interaction, to name a few. Out of all the complex problems tackled by computer vision today in context of these technologies, that which lies closest to the original goals of the field is the subarea of unsupervised scene analysis or scene modelling. However, its common use of low level features does not provide a good balance between generality and discriminative ability, both a result and a symptom of the sensory and semantic gaps existing between low level computer representations and high level human descriptions. In this research we explore a general framework that addresses the fundamental problem of universal unsupervised extraction of semantically meaningful visual regions and their behaviours. For this purpose we address issues related to (i) spatial and spatiotemporal segmentation for region extraction, (ii) region shape modelling, and (iii) the online categorisation of visual object classes and the spatiotemporal analysis of their behaviours. Under this framework we propose (a) a unified region merging method and spatiotemporal region reduction, (b) shape representation by the optimisation and novel simplication of contour-based growing neural gases, and (c) a foundation for the analysis of visual object motion properties using a shape and appearance based nearest-centroid classification algorithm and trajectory plots for the obtained region classes. 1 Specifically, we formulate a region merging spatial segmentation mechanism that combines and adapts features shown previously to be individually useful, namely parallel region growing, the best merge criterion, a time adaptive threshold, and region reduction techniques. For spatiotemporal region refinement we consider both scalar intensity differences and vector optical flow. To model the shapes of the visual regions thus obtained, we adapt the growing neural gas for rapid region contour representation and propose a contour simplication technique. A fast unsupervised nearest-centroid online learning technique next groups observed region instances into classes, for which we are then able to analyse spatial presence and spatiotemporal trajectories. The analysis results show semantic correlations to real world object behaviour. Performance evaluation of all steps across standard metrics and datasets validate their performance

Hybrid Caching for Chip Multiprocessors Using Compiler-Based Data Classification

The high performance delivered by modern computer system keeps scaling with an increasingnumber of processors connected using distributed network on-chip. As a result, memory accesslatency, largely dominated by remote data cache access and inter-processor communication, is becoming a critical performance bottleneck. To release this problem, it is necessary to localize data access as much as possible while keep efficient on-chip cache memory utilization. Achieving this however, is application dependent and needs a keen insight into the memory access characteristics of the applications. This thesis demonstrates how using fairly simple thus inexpensive compiler analysis memory accesses can be classified into private data access and shared data access. In addition, we introduce a third classification named probably private access and demonstrate the impact of this category compared to traditional private and shared memory classification. The memory access classification information from the compiler analysis is then provided to the runtime system through a modified memory allocator and page table to facilitate a hybrid private-shared caching technique. The hybrid cache mechanism is aware of different data access classification and adopts appropriate placement and search policies accordingly to improve performance. Our analysis demonstrates that many applications have a significant amount of both private and shared data and that compiler analysis can identify the private data effectively for many applications. Experimentsresults show that the implemented hybrid caching scheme achieves 4.03% performance improvement over state of the art NUCA-base caching

Automated Pilot Advisory System

An Automated Pilot Advisory System (APAS) was developed and operationally tested to demonstrate the concept that low cost automated systems can provide air traffic and aviation weather advisory information at high density uncontrolled airports. The system was designed to enhance the see and be seen rule of flight, and pilots who used the system preferred it over the self announcement system presently used at uncontrolled airports

Privacy preserving linkage and sharing of sensitive data

2018 Summer.Includes bibliographical references.Sensitive data, such as personal and business information, is collected by many service providers nowadays. This data is considered as a rich source of information for research purposes that could benet individuals, researchers and service providers. However, because of the sensitivity of such data, privacy concerns, legislations, and con ict of interests, data holders are reluctant to share their data with others. Data holders typically lter out or obliterate privacy related sensitive information from their data before sharing it, which limits the utility of this data and aects the accuracy of research. Such practice will protect individuals' privacy; however it prevents researchers from linking records belonging to the same individual across dierent sources. This is commonly referred to as record linkage problem by the healthcare industry. In this dissertation, our main focus is on designing and implementing ecient privacy preserving methods that will encourage sensitive information sources to share their data with researchers without compromising the privacy of the clients or aecting the quality of the research data. The proposed solution should be scalable and ecient for real-world deploy- ments and provide good privacy assurance. While this problem has been investigated before, most of the proposed solutions were either considered as partial solutions, not accurate, or impractical, and therefore subject to further improvements. We have identied several issues and limitations in the state of the art solutions and provided a number of contributions that improve upon existing solutions. Our rst contribution is the design of privacy preserving record linkage protocol using semi-trusted third party. The protocol allows a set of data publishers (data holders) who compete with each other, to share sensitive information with subscribers (researchers) while preserving the privacy of their clients and without sharing encryption keys. Our second contribution is the design and implementation of a probabilistic privacy preserving record linkage protocol, that accommodates discrepancies and errors in the data such as typos. This work builds upon the previous work by linking the records that are similar, where the similarity range is formally dened. Our third contribution is a protocol that performs information integration and sharing without third party services. We use garbled circuits secure computation to design and build a system to perform the record linkages between two parties without sharing their data. Our design uses Bloom lters as inputs to the garbled circuits and performs a probabilistic record linkage using the Dice coecient similarity measure. As garbled circuits are known for their expensive computations, we propose new approaches that reduce the computation overhead needed, to achieve a given level of privacy. We built a scalable record linkage system using garbled circuits, that could be deployed in a distributed computation environment like the cloud, and evaluated its security and performance. One of the performance issues for linking large datasets is the amount of secure computation to compare every pair of records across the linked datasets to nd all possible record matches. To reduce the amount of computations a method, known as blocking, is used to lter out as much as possible of the record pairs that will not match, and limit the comparison to a subset of the record pairs (called can- didate pairs) that possibly match. Most of the current blocking methods either require the parties to share blocking keys (called blocks identiers), extracted from the domain of some record attributes (termed blocking variables), or share reference data points to group their records around these points using some similarity measures. Though these methods reduce the computation substantially, they leak too much information about the records within each block. Toward this end, we proposed a novel privacy preserving approximate blocking scheme that allows parties to generate the list of candidate pairs with high accuracy, while protecting the privacy of the records in each block. Our scheme is congurable such that the level of performance and accuracy could be achieved according to the required level of privacy. We analyzed the accuracy and privacy of our scheme, implemented a prototype of the scheme, and experimentally evaluated its accuracy and performance against dierent levels of privacy