Systematizing Decentralization and Privacy: Lessons from 15 Years of Research and Deployments

Decentralized systems are a subset of distributed systems where multiple authorities control different components and no authority is fully trusted by all. This implies that any component in a decentralized system is potentially adversarial. We revise fifteen years of research on decentralization and privacy, and provide an overview of key systems, as well as key insights for designers of future systems. We show that decentralized designs can enhance privacy, integrity, and availability but also require careful trade-offs in terms of system complexity, properties provided, and degree of decentralization. These trade-offs need to be understood and navigated by designers. We argue that a combination of insights from cryptography, distributed systems, and mechanism design, aligned with the development of adequate incentives, are necessary to build scalable and successful privacy-preserving decentralized systems

Reliable Inference from Unreliable Agents

Distributed inference using multiple sensors has been an active area of research since the emergence of wireless sensor networks (WSNs). Several researchers have addressed the design issues to ensure optimal inference performance in such networks. The central goal of this thesis is to analyze distributed inference systems with potentially unreliable components and design strategies to ensure reliable inference in such systems. The inference process can be that of detection or estimation or classification, and the components/agents in the system can be sensors and/or humans. The system components can be unreliable due to a variety of reasons: faulty sensors, security attacks causing sensors to send falsified information, or unskilled human workers sending imperfect information. This thesis first quantifies the effect of such unreliable agents on the inference performance of the network and then designs schemes that ensure a reliable overall inference. In the first part of this thesis, we study the case when only sensors are present in the system, referred to as sensor networks. For sensor networks, the presence of malicious sensors, referred to as Byzantines, are considered. Byzantines are sensors that inject false information into the system. In such systems, the effect of Byzantines on the overall inference performance is characterized in terms of the optimal attack strategies. Game-theoretic formulations are explored to analyze two-player interactions. Next, Byzantine mitigation schemes are designed that address the problem from the system\u27s perspective. These mitigation schemes are of two kinds: Byzantine identification schemes and Byzantine tolerant schemes. Using learning based techniques, Byzantine identification schemes are designed that learn the identity of Byzantines in the network and use this information to improve system performance. When such schemes are not possible, Byzantine tolerant schemes using error-correcting codes are developed that tolerate the effect of Byzantines and maintain good performance in the network. Error-correcting codes help in correcting the erroneous information from these Byzantines and thereby counter their attack. The second line of research in this thesis considers humans-only networks, referred to as human networks. A similar research strategy is adopted for human networks where, the effect of unskilled humans sharing beliefs with a central observer called \emph{CEO} is analyzed, and the loss in performance due to the presence of such unskilled humans is characterized. This problem falls under the family of problems in information theory literature referred to as the \emph{CEO Problem}, but for belief sharing. The asymptotic behavior of the minimum achievable mean squared error distortion at the CEO is studied in the limit when the number of agents $L$ and the sum rate $R$ tend to infinity. An intermediate regime of performance between the exponential behavior in discrete CEO problems and the $1/R$ behavior in Gaussian CEO problems is established. This result can be summarized as the fact that sharing beliefs (uniform) is fundamentally easier in terms of convergence rate than sharing measurements (Gaussian), but sharing decisions is even easier (discrete). Besides theoretical analysis, experimental results are reported for experiments designed in collaboration with cognitive psychologists to understand the behavior of humans in the network. The act of fusing decisions from multiple agents is observed for humans and the behavior is statistically modeled using hierarchical Bayesian models. The implications of such modeling on the design of large human-machine systems is discussed. Furthermore, an error-correcting codes based scheme is proposed to improve system performance in the presence of unreliable humans in the inference process. For a crowdsourcing system consisting of unskilled human workers providing unreliable responses, the scheme helps in designing easy-to-perform tasks and also mitigates the effect of erroneous data. The benefits of using the proposed approach in comparison to the majority voting based approach are highlighted using simulated and real datasets. In the final part of the thesis, a human-machine inference framework is developed where humans and machines interact to perform complex tasks in a faster and more efficient manner. A mathematical framework is built to understand the benefits of human-machine collaboration. Such a study is extremely important for current scenarios where humans and machines are constantly interacting with each other to perform even the simplest of tasks. While machines perform best in some tasks, humans still give better results in tasks such as identifying new patterns. By using humans and machines together, one can extract complete information about a phenomenon of interest. Such an architecture, referred to as Human-Machine Inference Networks (HuMaINs), provides promising results for the two cases of human-machine collaboration: \emph{machine as a coach} and \emph{machine as a colleague}. For simple systems, we demonstrate tangible performance gains by such a collaboration which provides design modules for larger, and more complex human-machine systems. However, the details of such larger systems needs to be further explored

Blockchain and smart contracts in health-related MyData scenario

Abstract. The MyData is concept framework that refers to human-centric ways of personal data management. Personal data gained significant attention recently. As the developing of Ubicomp technology, more and more particularly personal data are generating and collecting. Personal data own increasingly important economic, social, and practical value. However, individuals have little or no power to control when and how their data being created or processed by companies, organizations or governments. The MyData aim to provide individuals with practical methods to obtain, access, and utilize their personal datasets and to encourage organizations to give users control over their personal data. In this way, access and trade personal data can expect to build an open data market. Two challenges to achieve this goal is how to gain the individuals trust and permission and how to provide a more human-centric way to support personal data management and utilization. To explore a novel and reliable way to address the challenges in MyData, this thesis utilizes blockchain technology to support MyData framework. Blockchain is a decentralized transparent ledger with the transaction information that shared among all peer-to-peer network nodes. It has the potential to gain users trust and provide a solution to gain users permission in data trade. This thesis work focuses on studying blockchain and smart contract performance in MyData architecture. An Ethereum blockchain based MyData system that combined AWARE platform designed and implemented. The system deploys smart contract that provides users’ account management, personal data access, trade services, and information inquiry services in the Ethereum blockchain. Based on this system, two experiments designed to evaluate the performance of the integrated MyData system. The experiments results demonstrate how blockchain can facilitate MyData concept and how gas price influences the system performance. The thesis work shows that the blockchain and smart contract have the potential to provide the necessary technology support to solve the challenge in gain users’ trust and permission and support new business models and open data market to benefit both the data consumer and data producer. Additionally, blockchain and the smart contract can provide a more fine-grained and transparent way to help individuals to manage and utilize their personal data

Secure Data Aggregation Protocol with Byzantine Robustness for Wireless Sensor Networks

Sensor networks are dense wireless networks constituting of small and low-cost sensors that collect and disseminate sensory data. They have gained great attention in recent years due to their ability to offer economical and effective solutions in a variety of fields; and their profound suitability to address mission critical problems that are common in health, transportation, and military applications. “Sensor networks” is a technology that is seen to change the world, and as such their deployment is expected to see a rapid growth. Effective security strategy is essential for any sensor network in order to maintain trustful and reliable functionality, protect sensory information, and ensure network component authenticity. Security models and protocols that are typically used in other types of networks, such as wired networks, are not suitable for sensor networks due to their specific hardware specifications. This thesis highlights some of the research done so far in the area of security of wireless sensor networks and proposes a solution to detect Byzantine behaviour - a challenging security threat that many sensor networks face. The proposed solution’s use of cryptography is kept at a minimum to ensure maximum secure bandwidth. Under this solution, a sensor network continues to work normally until an attack is suspected. Once an attack is suspected, a cryptography scheme is enabled to authenticate suspected nodes and to allow the identification of potential external attacks. If an attack seems to persist after the cryptography scheme has been enabled, the same mechanism is used to identify and isolate potentially compromised nodes. The goal is to introduce a degree of intelligence into such networks and consequently improve reliability of data collection, accuracy of aggregated data, and prolong network lifetime

Quality of Context in Context-Aware Systems

Context-aware Systems (CASs) are becoming increasingly popular and can be found in the areas of wearable computing, mobile computing, robotics, adaptive and intelligent user interfaces. Sensors are the corner stone of context capturing however, sensed context data are commonly prone to imperfection due to the technical limitations of sensors, their availability, dysfunction, and highly dynamic nature of environment. Consequently, sensed context data might be imprecise, erroneous, conflicting, or simply missing. To limit the impact of context imperfection on the behavior of a context-aware system, a notion of Quality of Context (QoC) is used to measure quality of any information that is used as context information. Adaptation is performed only if the context data used in the decision-making has an appropriate quality level. This paper reports an analytical review for state of the art quality of context in context-aware systems and points to future research directions

New Distributed Byzantine Fault Detection & Data Integrity Scheme for WANET

Wireless ad-hoc networks (WANET) with multi-hop communication are subject to a variety of faults and attacks, and detecting the source of any fault is highly important to maintain the quality of service, confidentiality, and reliability of an entire network operation. Intermediate byzantine nodes in WANET could subvert the system by altering sensitive routed information unintentionally due to many reasons such as power depletion, software bug, malware, and environmental obstacles. This thesis highlights some of the research studies done in the area of distributed fault detection (DFD) and proposes a solution to detect Byzantine behavior cooperatively. The present research will focus on designing a scalable distributed fault detection (DFD) algorithm to detect byzantine nodes who permanently try to distort or reroute information while relaying a message from one node to another, complimentary to that, a symmetric distributed cryptography scheme will be employed to continuously validates the data integrity of a routed message. The main hypothesis of the research is that if a wireless ad-hoc network is been divided into N number of groups (classes) with relatively equal number of members, each group of nodes can cooperatively protect the network from every other group. Practically, each group of nodes will be assigned to a distinct shared key; nodes with similar group assignment shall guard the integrity of a routing path by incorporating their own secret message authentication code (MAC) that can be only validated by nodes belonging to the same group contributing to the same routing path. If a node from Group(i) detects a tampering event, it should either store and delay a fault report or embed a fault report to the same routed message and forward it to the Master Node (Destination) if applicable. Further report message overhead optimization has been devised to reduce the energy cost. Moreover, the empirical results have shown that the more reported evidence the master node can collect, the more accuracy of detection can be reached based on an incremental stream of evidence that contains information about both healthy and unhealthy nodes; so that every healthy report type can justify the unhealthy false report. The heuristic simulation based study considered many different aspects of the system for evaluation such as detection accuracy, fault model, the optimal number of classes, energy consumption, the impact of mobility, and network lifetime. The iGraph network simulation tool has been employed for visualization and graph manipulation, whereas, Python programming language has been utilized in conjunction to implement and simulate the DFD algorithm and generate the results