Privacy-preserving systems around security, trust and identity

Data has proved to be the most valuable asset in a modern world of rapidly advancing technologies. Companies are trying to maximise their profits by getting valuable insights from collected data about people’s trends and behaviour which often can be considered personal and sensitive. Additionally, sophisticated adversaries often target organisations aiming to exfiltrate sensitive data to sell it to third parties or ask for ransom. Hence, the privacy assurance of the individual data producers is a matter of great importance who rely on simply trusting that the services they use took all the necessary countermeasures to protect them.Distributed ledger technology and its variants can securely store data and preserve its privacy with novel characteristics. Additionally, the concept of self-sovereign identity, which gives the control back to the data subjects, is an expected future step once these approaches mature further. Last but not least, big data analysis typically occurs through machine learning techniques. However, the security of these techniques is often questioned since adversaries aim to exploit them for their benefit.The aspect of security, privacy and trust is highlighted throughout this thesis which investigates several emerging technologies that aim to protect and analyse sensitive data compared to already existing systems, tools and approaches in terms of security guarantees and performance efficiency.The contributions of this thesis derive to i) the presentation of a novel distributed ledger infrastructure tailored to the domain name system, ii) the adaptation of this infrastructure to a critical healthcare use case, iii) the development of a novel self-sovereign identity healthcare scenario in which a data scientist analyses sensitive data stored in the premises of three hospitals, through a privacy-preserving machine learning approach, and iv) the thorough investigation of adversarial attacks that aim to exploit machine learning intrusion detection systems by “tricking” them to misclassify carefully crafted inputs such as malware identified as benign.A significant finding is that the security and privacy of data are often neglected since they do not directly impact people’s lives. It is common for the protection and confidentiality of systems, even of critical nature, to be an afterthought, which is considered merely after malicious intents occur. Further, emerging sets of technologies, tools, and approaches built with fundamental security and privacy principles, such as the distributed ledger technology, should be favoured by existing systems that can adopt them without significant changes and compromises. Additionally, it has been presented that the decentralisation of machine learning algorithms through self-sovereign identity technologies that provide novel end-to-end encrypted channels is possible without sacrificing the valuable utility of the original machine learning algorithms.However, a matter of great importance is that alongside technological advancements, adversaries are becoming more sophisticated in this area and are trying to exploit the aforementioned machine learning approaches and other similar ones for their benefit through various tools and approaches. Adversarial attacks pose a real threat to any machine learning algorithm and artificial intelligence technique, and their detection is challenging and often problematic. Hence, any security professional operating in this domain should consider the impact of these attacks and the protection countermeasures to combat or minimise them

On the Perturbation of the Three-Dimensional Stokes Flow of Micropolar Fluids by a Constant Uniform Magnetic Field in a Circular Cylinder

Modern engineering technology involves the micropolar magnetohydrodynamic flow of magnetic fluids. Here, we consider a colloidal suspension of non-conductive ferromagnetic material, which consists of small spherical particles that behave as rigid magnetic dipoles, in a carrier liquid of approximately zero conductivity and low-Reynolds number properties. The interaction of a 3D constant uniform magnetic field with the three-dimensional steady creeping motion (Stokes flow) of a viscous incompressible micropolar fluid in a circular cylinder is investigated, where the magnetization of the ferrofluid has been taken into account and the magnetic Stokes partial differential equations have been presented. Our goal is to apply the proper boundary conditions, so as to obtain the flow fields in a closed analytical form via the potential representation theory, and to study several characteristics of the flow. In view of this aim, we make use of an improved new complete and unique differential representation of magnetic Stokes flow, valid for non-axisymmetric geometries, which provides the velocity and total pressure fields in terms of easy-to-find potentials. We use these results to simulate the creeping flow of a magnetic fluid inside a circular duct and to obtain the flow fields associated with this kind of flow

RELINE: Point-of-Interest Recommendations using Multiple Network Embeddings

The rapid growth of users' involvement in Location-Based Social Networks (LBSNs) has led to the expeditious growth of the data on a global scale. The need of accessing and retrieving relevant information close to users' preferences is an open problem which continuously raises new challenges for recommendation systems. The exploitation of Points-of-Interest (POIs) recommendation by existing models is inadequate due to the sparsity and the cold start problems. To overcome these problems many models were proposed in the literature, but most of them ignore important factors such as: geographical proximity, social influence, or temporal and preference dynamics, which tackle their accuracy while personalize their recommendations. In this work, we investigate these problems and present a unified model that jointly learns users and POI dynamics. Our proposal is termed RELINE (REcommendations with muLtIple Network Embeddings). More specifically, RELINE captures: i) the social, ii) the geographical, iii) the temporal influence, and iv) the users' preference dynamics, by embedding eight relational graphs into one shared latent space. We have evaluated our approach against state-of-the-art methods with three large real-world datasets in terms of accuracy. Additionally, we have examined the effectiveness of our approach against the cold-start problem. Performance evaluation results demonstrate that significant performance improvement is achieved in comparison to existing state-of-the-art methods

Pathophysiology and Biomechanics of the Aging Spine

Aging of the spine is characterized by two parallel but independent processes: the reduction of bone mineral density and the development of degenerative changes. The combination of degeneration and bone mass reduction contribute, to a different degree, to the development of a variety of lesions. This results in a number of painful and often debilitating disorders. The present review constitutes a synopsis of the pathophysiological processes that take place in the aging spine as well as of the consequences these changes have on the biomechanics of the spine. The authors hope to present a thorough yet brief overview of the process of aging of the human spine

Αποτελεσματικότητα στον «παίκτη παραπάνω» μετά από time-out στην Υδατοσφαίριση

H λήψη time-out στην υδατοσφαίριση γίνεται συχνά από τους προπονητές πριν την πραγματοποίηση «παίκτη παραπάνω» με σκοπό να δοθούν κατάλληλες οδηγίες και να επιτευχθεί τέρμα.Σκοπός της παρούσας εργασίας ήταν να διερευνήσει την αποτελεσματικότητα στον «παίκτη παραπάνω» μετά από χρήση time-out στην υδατοσφαίριση. H μέθοδος που χρησιμοποιήσαμε για το σκοπό αυτό, ήταν η ανάλυση 121 αγώνων στην κορυφαία διασυλλογική διοργάνωση,το Champions League, σύμφωνα με την οποία βρέθηκε ότι είχαμε επιτυχία στον «παίκτη παραπάνω» μετά από time-out 42,56% και χωρίς χρήση time-out 38,9% ενώ η συνολική επιτυχία στον συγκεκριμένο τομέα ήταν 39,36%. Συμπερασματικά, η ανάλυση των αγώνων δεν επιβεβαίωσε προηγούμενα ευρήματα της βιβλιογραφίας και έδειξε ότι η αποτελεσματικότητα στον «παίκτη παραπάνω με από time-out κυμαίνεται στο 40% δείχνοντας όμως η λήψη time-out βοηθά στην αποτελεσματικότητα του «παίκτη παραπάνω» στην υδατοσφαίριση. Θα θέλαμε να αναφέρουμε πως μέσα από αυτή την εργασία θα επιδιώξουμε να αντιληφθούμε ποια είναι η καλύτερη στρατηγική στον τομέα του «παίκτη παραπάνω» και πως μπορούν να βοηθηθούν οι προπονητές μέσα από τα αποτελέσματα της συγκεκριμένης έρευνας.ΟΧ

Towards The Creation Of The Future Fish Farm

Aim: A fish farm is an area where fish raise and bred for food. Fish farm environments support the care and management of seafood within a controlled environment. Over the past few decades, there has been a remarkable increase in the calorie intake of protein attributed to seafood. Along with this, there are significant opportunities within the fish farming industry for economic development. Determining the fish diseases, monitoring the aquatic organisms, and examining the imbalance in the water element are some key factors that require precise observation to determine the accuracy of the acquired data. Similarly, due to the rapid expansion of aquaculture, new technologies are constantly being implemented in this sector to enhance efficiency. However, the existing approaches have often failed to provide an efficient method of farming fish. Methods: This work has kept aside the traditional approaches and opened up new dimensions to perform accurate analysis by adopting a distributed ledger technology. Our work analyses the current state-of-the-art of fish farming and proposes a fish farm ecosystem that relies on a private-by-design architecture based on the Hyperledger Fabric private-permissioned distributed ledger technology. Results: The proposed method puts forward accurate and secure storage of the retrieved data from multiple sensors across the ecosystem so that the adhering entities can exercise their decision based on the acquired data. Conclusion: This study demonstrates a proof-of-concept to signify the efficiency and usability of the future fish farm

A Privacy-Preserving Healthcare Framework Using Hyperledger Fabric

Electronic health record (EHR) management systems require the adoption of effective technologies when health information is being exchanged. Current management approaches often face risks that may expose medical record storage solutions to common security attack vectors. However, healthcare-oriented blockchain solutions can provide a decentralized, anonymous and secure EHR handling approach. This paper presents PREHEALTH, a privacy-preserving EHR management solution that uses distributed ledger technology and an Identity Mixer (Idemix). The paper describes a proof-of-concept implementation that uses the Hyperledger Fabric's permissioned blockchain framework. The proposed solution is able to store patient records effectively whilst providing anonymity and unlinkability. Experimental performance evaluation results demonstrate the scheme's efficiency and feasibility for real-world scale deployment

Privacy-Preserving Passive DNS

The Domain Name System (DNS) was created to resolve the IP addresses of web servers to easily remembered names. When it was initially created, security was not a major concern; nowadays, this lack of inherent security and trust has exposed the global DNS infrastructure to malicious actors. The passive DNS data collection process creates a database containing various DNS data elements, some of which are personal and need to be protected to preserve the privacy of the end users. To this end, we propose the use of distributed ledger technology. We use Hyperledger Fabric to create a permissioned blockchain, which only authorized entities can access. The proposed solution supports queries for storing and retrieving data from the blockchain ledger, allowing the use of the passive DNS database for further analysis, e.g., for the identification of malicious domain names. Additionally, it effectively protects the DNS personal data from unauthorized entities, including the administrators that can act as potential malicious insiders, and allows only the data owners to perform queries over these data. We evaluated our proposed solution by creating a proof-of-concept experimental setup that passively collects DNS data from a network and then uses the distributed ledger technology to store the data in an immutable ledger, thus providing a full historical overview of all the records