Information-Theoretic Secure Outsourced Computation in Distributed Systems

Secure multi-party computation (secure MPC) has been established as the de facto paradigm for protecting privacy in distributed computation. One of the earliest secure MPC primitives is the Shamir\u27s secret sharing (SSS) scheme. SSS has many advantages over other popular secure MPC primitives like garbled circuits (GC) -- it provides information-theoretic security guarantee, requires no complex long-integer operations, and often leads to more efficient protocols. Nonetheless, SSS receives less attention in the signal processing community because SSS requires a larger number of honest participants, making it prone to collusion attacks. In this dissertation, I propose an agent-based computing framework using SSS to protect privacy in distributed signal processing. There are three main contributions to this dissertation. First, the proposed computing framework is shown to be significantly more efficient than GC. Second, a novel game-theoretical framework is proposed to analyze different types of collusion attacks. Third, using the proposed game-theoretical framework, specific mechanism designs are developed to deter collusion attacks in a fully distributed manner. Specifically, for a collusion attack with known detectors, I analyze it as games between secret owners and show that the attack can be effectively deterred by an explicit retaliation mechanism. For a general attack without detectors, I expand the scope of the game to include the computing agents and provide deterrence through deceptive collusion requests. The correctness and privacy of the protocols are proved under a covert adversarial model. Our experimental results demonstrate the efficiency of SSS-based protocols and the validity of our mechanism design

New Fundamental Technologies in Data Mining

The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining

Federated Learning in Mobile Edge Networks: A Comprehensive Survey

In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications. Traditional cloudbased Machine Learning (ML) approaches require the data to be centralized in a cloud server or data center. However, this results in critical issues related to unacceptable latency and communication inefficiency. To this end, Mobile Edge Computing (MEC) has been proposed to bring intelligence closer to the edge, where data is produced. However, conventional enabling technologies for ML at mobile edge networks still require personal data to be shared with external parties, e.g., edge servers. Recently, in light of increasingly stringent data privacy legislations and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. In FL, end devices use their local data to train an ML model required by the server. The end devices then send the model updates rather than raw data to the server for aggregation. FL can serve as an enabling technology in mobile edge networks since it enables the collaborative training of an ML model and also enables DL for mobile edge network optimization. However, in a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved. This raises challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale. In this survey, we begin with an introduction to the background and fundamentals of FL. Then, we highlight the aforementioned challenges of FL implementation and review existing solutions. Furthermore, we present the applications of FL for mobile edge network optimization. Finally, we discuss the important challenges and future research directions in F

Technology Pooling Licensing Agreements: Promoting Patent Access Through Collaborative IP Mechanisms

In patent communities several patentees cooperate contractually to licence the respective patented technologies to third parties. In consideration of the rising relevance of this business practice, this book discusses crucial courses and strategic considerations, which are the basis for the establishment of patent communities, both in legal and empirical regard, in order to identify the optimal conditions for successful conversion in a competitive surrounding. Thus the best conditions for the promotion of innovation are to be created

Evaluation of the new Design Summer Year weather data using parametrical buildings

The Charted Institution of Building Services Engineers (CIBSE) updated the near extreme weather (Design Summer Year – DSY) for all 14 locations in the UK in 2016. This new release attempts to address the underlying shortcomings of the previous definition where the averaged dry bulb temperature was the sole metric to choose DSY among source weather years. The aim of this research is to evaluate whether the new definition of the probabilistic DSYs can consistently represent near extreme condition. London historical weather data and their correspondent DSYs were used in this research. Dynamic thermal modelling using EnergyPlus was carried out on large number single zone offices (parametric study) which represent a large portion of cellular offices in the UK. The predicted indoor warmth from the sample building models show that these new definitions are not always able to represent near extreme conditions. Using multiple years as DSY is able to capture different types of summer warmth but how to use one or all of these DSYs to make informed judgement on overheating is rather challenging. The recommended practice from this research is to use more warm years for the evaluation of overheating and choose the near extreme weather from the predicted indoor warmt

Age composition and survival of public housing stock in Hong Kong

Emerging notably in more developed regions, building stock ageing which is characterised by shrinking new completions and falling “mortality” has been posing challenges to various stakeholders in built environment. To find way out of this transition, we need to know how long buildings will last these days and the factors leading to their “mortality”. By using data from 1950s till to date, a comprehensive investigation is conducted to analyse the age composition and life expectancy of public housing stock in Hong Kong. What comes after are survival analysis and empirical analysis of those demolished to identify the key factors leading to demolition. Presented in this paper are the preliminary findings as well as the research agenda on the theme to model age composition and survival of both private and public building stocks in Hong Kong and other similar cities in Asia Pacific Rim such as Adelaide and Singapore, together with research activities to formulate policies for sustainable urban management

Mobile Ad-Hoc Networks

Being infrastructure-less and without central administration control, wireless ad-hoc networking is playing a more and more important role in extending the coverage of traditional wireless infrastructure (cellular networks, wireless LAN, etc). This book includes state-of the-art techniques and solutions for wireless ad-hoc networks. It focuses on the following topics in ad-hoc networks: vehicular ad-hoc networks, security and caching, TCP in ad-hoc networks and emerging applications. It is targeted to provide network engineers and researchers with design guidelines for large scale wireless ad hoc networks