ETSI Technical Specification TS 103757. SmartM2M; Asynchronous Contact Tracing System: Fighting pandemic disease with Internet of Things (IoT)

The present document defines properties and usage of IoT and M2M technology in Contact Tracing.It introduces the method of Asynchronous Contact Tracing (ACT). ACT registers the presence of SARS-CoV-2 virus on IoT connected objects (waste water, or air conditioning filters, or dirty objects, or dirty cleaning tools, etc.) or connected locations (such as a shops, restaurants, corridors in a supermarket, sanitary facilities in a shopping mall, railway stations, airports terminals and gates, etc.) using Group Test (sometime called in the literature Pooling Test).ACT identifies contacts with IoT connected objects that have been contaminated by the SARS-CoV-2 virus and works in synergy with solutions designed for manual and digital contact tracing to identify and alert people who may have been infected by the virus. In case the object is suspected to host or have hosted the SARS-CoV-2 virus, ACT allows users that have been in contact with the object or visited the connected location to be informed.This shifts the paradigm from synchronously tracing the contacts of the people infected by COVID-19 to asynchronously tracing of contacts of materials (such as infected surfaces, waste-water, air-conditioning filters, etc.) that are hosting the SARS-CoV-2 virus.This enables people who have come into contact asynchronously with those particular materials to be alerted of a potential COVID-19 contagion, and, at the same time, it signals that one or more persons have been in contact with the material which is now spreading the SARS-CoV-2 virus.Asynchronous Contact Tracing (ACT) traces the IoT connected object that may have been infected by the Covid-19 virus (or future pandemic viruses). This shifts the paradigm, from searching for a person in the process of infecting another to the tracing of both potential contamination and infections, and leveraging on the combination of the two information.The scope of this WI is to standardize the full support of Asynchronous Contact Tracing (ACT) by means of1) providing some examples of use and deployment of ACT by means of a few explanatory use cases.2) specifying the ACT method and its interaction with deployed contact tracing applications for human and systems. This includes the interaction with the different technologies used by non ACT contact tracing solutions.3) specifying the ACT system including application protocols and API.The new ACT method will require the use of existing ready-to-market IoT-based technology and well-established wireless network techniques, in particular the ones specified in the ETSI standards ecosystem. Moreover, it will preserve the user's privacy in accordance with GDPR and/or other regional requirements not requiring the transmission of any personal information by the user

Autonomy and Efficiency Trade-offs on an Ethereum-Based Real Estate Application

Siiani on jagamismajanduse vahendusplatvorme arendatud tsentraliseeritud andmebaaside abil. Plokiahela esiletõus on aga ilmutanud uusi võimalusi, et muuta valdkonda võltsimiskindlaks ning vähendada vajadust vahendajate järele. Käesolevas töös uuritakse plokiahela kasutusvõimalusi kinnisvara rentimise protsessi näitel. Täpsemalt, töös disainitakse lahendus Ethereumi abil ning teostatakse kolm järjestikust prototüüpi, et analüüsida andmete ning arvutuste tõstmist plokiahelasse. Tulemused näitavad, et detsentraliseerimisel tuleb teha kompromisse teostatavuse ning tõhususe vahel.Marketplaces in sharing economy have traditionally been organized as web applications running on top of centralized databases. The advent of blockchain technology brings new opportunities, with the promise of transforming the landscape with tamper-resilient storage and the potential of reduction in intermediaries. In this context, in this thesis we look at exploring the use of blockchain technologies in the domain of real estate rental process. More specifically, we designed a solution on top of Ethereum and implemented three consecutive prototypes to analyze the impact of moving data and processing to the blockchain. The results show a trade-off between efficacy versus efficiency when moving toward decentralization

Quality of Service Aware Data Stream Processing for Highly Dynamic and Scalable Applications

Huge amounts of georeferenced data streams are arriving daily to data stream management systems that are deployed for serving highly scalable and dynamic applications. There are innumerable ways at which those loads can be exploited to gain deep insights in various domains. Decision makers require an interactive visualization of such data in the form of maps and dashboards for decision making and strategic planning. Data streams normally exhibit fluctuation and oscillation in arrival rates and skewness. Those are the two predominant factors that greatly impact the overall quality of service. This requires data stream management systems to be attuned to those factors in addition to the spatial shape of the data that may exaggerate the negative impact of those factors. Current systems do not natively support services with quality guarantees for dynamic scenarios, leaving the handling of those logistics to the user which is challenging and cumbersome. Three workloads are predominant for any data stream, batch processing, scalable storage and stream processing. In this thesis, we have designed a quality of service aware system, SpatialDSMS, that constitutes several subsystems that are covering those loads and any mixed load that results from intermixing them. Most importantly, we natively have incorporated quality of service optimizations for processing avalanches of geo-referenced data streams in highly dynamic application scenarios. This has been achieved transparently on top of the codebases of emerging de facto standard best-in-class representatives, thus relieving the overburdened shoulders of the users in the presentation layer from having to reason about those services. Instead, users express their queries with quality goals and our system optimizers compiles that down into query plans with an embedded quality guarantee and leaves logistic handling to the underlying layers. We have developed standard compliant prototypes for all the subsystems that constitutes SpatialDSMS

Scalable big data systems: Architectures and optimizations

Big data analytics has become not just a popular buzzword but also a strategic direction in information technology for many enterprises and government organizations. Even though many new computing and storage systems have been developed for big data analytics, scalable big data processing has become more and more challenging as a result of the huge and rapidly growing size of real-world data. Dedicated to the development of architectures and optimization techniques for scaling big data processing systems, especially in the era of cloud computing, this dissertation makes three unique contributions. First, it introduces a suite of graph partitioning algorithms that can run much faster than existing data distribution methods and inherently scale to the growth of big data. The main idea of these approaches is to partition a big graph by preserving the core computational data structure as much as possible to maximize intra-server computation and minimize inter-server communication. In addition, it proposes a distributed iterative graph computation framework that effectively utilizes secondary storage to maximize access locality and speed up distributed iterative graph computations. The framework not only considerably reduces memory requirements for iterative graph algorithms but also significantly improves the performance of iterative graph computations. Last but not the least, it establishes a suite of optimization techniques for scalable spatial data processing along with three orthogonal dimensions: (i) scalable processing of spatial alarms for mobile users traveling on road networks, (ii) scalable location tagging for improving the quality of Twitter data analytics and prediction accuracy, and (iii) lightweight spatial indexing for enhancing the performance of big spatial data queries.Ph.D

Preserving Privacy in Mobile Environments

Technology is improving day-by-day and so is the usage of mobile devices. Every activity that would involve manual and paper transactions can now be completed in seconds using your ngertips. On one hand, life has become fairly convenient with the help of mobile devices, whereas on the other hand privacy of the data and the transactions occurring in the process have been under continuous threat. Mobile devices connect to a number of service providers for various reasons. These could include downloading data, online purchasing or could be just used to browse information which may be irrelevant at a later point. Access to critical and sensitive information may be available at a number of places. In case of a mobile device, the information may be available with the service provider. Service Provider could be in the form of any web portal. In all such scenarios, passing the information or data from the service provider into the mobile device is a major challenge, as the data/information cannot be sent in plain text format. The con dentiality and integrity of the data needs to be protected and hence, the service provider must convert the data into an encrypted format before passing it onto the mobile device, to prevent risks from sniffing and unauthorized disclosure of data. Preserving the location of the individual user of any mobile device has also been the concern for a number of researchers. Mobile devices have become an important tool in modern communication. Mobile and other handheld devices such as ipads and tablets have over taken laptops and desktops and hence there has been an increasing research interest in this area in recent years. This includes improving the quality of communication and the overall end-to-end data security in day-to-day transactions. Mobile devices continuously connect to di erent service providers for day-to-day needs such as online purchases, online banking and endless sur ng for information. In addition to this devices could be connecting to the service providers to receive or send sensitive information. At the Service Provider end, the data would be stored with the provider and Service Provider would only hand over the data if it con rms that the person requested it is authorized to receive the information. The exchange of data from one end of the network to the other is a major challenge due to malicious intruder mishandling of the data. Hence the con dentiality and integrity of the data needs to be protected either by transforming the sensitive information into a non-readable format or by converting into a cipher text. Privacy has been an open problem for research as more and more information is getting leaked on a day-to-day basis. Through this thesis, I have tried to address a number of areas within the privacy realm where information and data access and sharing is a key concern along side the key aspect of location privacy. I have also tried to address the problems in the space of access control wherein I have proposed policy based languages and extensions for ensuring appropriate access control methodologies. The main goal and focus in this work has been to enforce the importance of location privacy in mobile environments and to propose solutions that resolve the problems of where and when to enforce location security. Another key goal of this work has been to create new access control and trust based solutions to ensure the right level of access to the right receiver of information. Through my research, I have explored the various privacy related attacks and suggested appropriate countermeasures for the same. In addition to proposing and showcasing solutions using policy languages for access control, I have also introduced geospatial access control solutions to ensure that the right user is accessing or requesting for the right information from the right location. This helps the appropriate and the right use of the information by the right resource. Through my thesis I have also given equal importance to the trust aspects of sharing information. I have created new trust assessment models to show how fused information can be handled and how can trust be imposed on the information provider and the information itself. The main contribution of this thesis is to address the problems around protecting the data and individual's privacy and to propose solutions to mitigate these issues using new and novel techniques. They can be detailed as the following: In privacy, there is always a privacy versus utility tradeo and in order to make use of utility, trust in the location is essential. Through this research I have developed i) novel attestation models and access control methodologies including Privacy Preferences Platform (P3P) extensions, ii) Extensible Access Control Markup Language (XACML) extensions and iii) Geospatial access control through GeoXACML. iv)I have created new methodologies to enforce location privacy and shown where best to enforce privacy. v)I have also shown that global attestation is very crucial for privacy and needs accurate methods in place to attest user's location information for access. vi) Fusing of location information is very crucial as there could be a number of similar or con icting information produced about a common source and it is very important to assess and evaluate the trust level in the information. I have proposed, developed and implemented a new trust assessment framework. This framework looks at the incoming information and passes it on to the rule engine in the framework to make some inferences and then the trust assessment module computes the trust score based on forward chaining or background chaining scheme. The framework is used to evaluate the trust on the fused information in a streaming setup. vii) I have created new solutions to look at the similarity pro les and create identity enforcement through pro ling. I have shown methods of anonymisation for location privacy and identity privacy

Scalable visual analytics over voluminous spatiotemporal data

2018 Fall.Includes bibliographical references.Visualization is a critical part of modern data analytics. This is especially true of interactive and exploratory visual analytics, which encourages speedy discovery of trends, patterns, and connections in data by allowing analysts to rapidly change what data is displayed and how it is displayed. Unfortunately, the explosion of data production in recent years has led to problems of scale as storage, processing, querying, and visualization have struggled to keep pace with data volumes. Visualization of spatiotemporal data pose unique challenges, thanks in part to high-dimensionality in the input feature space, interactions between features, and the production of voluminous, high-resolution outputs. In this dissertation, we address challenges associated with supporting interactive, exploratory visualization of voluminous spatiotemporal datasets and underlying phenomena. This requires the visualization of millions of entities and changes to these entities as the spatiotemporal phenomena unfolds. The rendering and propagation of spatiotemporal phenomena must be both accurate and timely. Key contributions of this dissertation include: 1) the temporal and spatial coupling of spatially localized models to enable the visualization of phenomena at far greater geospatial scales; 2) the ability to directly compare and contrast diverging spatiotemporal outcomes that arise from multiple exploratory "what-if" queries; and 3) the computational framework required to support an interactive user experience in a heavily resource-constrained environment. We additionally provide support for collaborative and competitive exploration with multiple synchronized clients