University of Piraeus,Department of Informatics, E-Learning Program
Courses : Web Application Development with Joomla.
I was born in Kozani, Greece and I received the BSc degree in Business Planning and Information Systems from TEI of Patras, Greece in 2004 and the MSc in Advanced Computer Systems from University of Piraeus, Greece in 2008. I received my PhD diploma in October 2018 at University of Piraeus, titled "Content-based Information Retrieval & Anonymisation in Data & Multimedia Streams", under the supervision of Professor Constantinos Patsakis. Also, I was a member of Decision Support System Laboratory, Department of Informatics, University of Piraeus. Additionally, I held a scholarship from the Institute for the Management of Information Systems, Research Center "Athena" in the research area of "Privacy protection".
Content-based Information Retrieval & Anonymisation in Data & Multimedia Streams
University of Piraeus, Department of Informatics
One Year Programme of Pedagogical Training
School of Pedagogical and Technological Education
MSc in Advanced Computer Systems
Orientation Decision Support Systems
University of Piraeus
BSc in Informatics
University of Piraeus, Greece
BSc in Business Planning and Information Systems
Technological Educational Institute of Patra, Greece
My current research interests include Multimedia Retrieval and Metadata, Privacy Preserving Data Publishing, Anonymity, Digital Watermarking, Multicriteria Analysis and Digital Currencies.
The recent advent of Web 2.0 has placed individuals, their information and their social connections to the center of the Internet stage: the Internet has evolved from being an academic and business tool, to one of the prime means for casual everyday people to network, communicate, publish and exchange all sorts of different data, and to collaborate on projects and activities. GeoWeb 2.0 is the geographic embodiment of the Web 2.0 vision for the next generation of geographic information publishing, discovery and use on the Web. In this context, GEOSTREAM aims at providing novel techniques and tools for extracting, processing, and exploiting user-generated geospatial information on the Web.
Βασικός σκοπός είναι ο σχεδιασμός μοντέλου-εργαλείου για τη μέτρηση της αποδοτικότητας των τμημάτων οικονομικών σπουδών και συναφών επιστημών των δημοσίων ελληνικών πανεπιστημίων και διεθνώς με τη χρήση ποσοτικών μεθόδων και ιδιαιτέρως της Περιβάλλουσας Ανάλυσης Δεδομένων (ΠΑΔ, Data Envelopment Analysis – DEA). Η εφαρμογή της ΠΑΔ πρόκειται να βασιστεί σε δεδομένα τα οποία αντιστοιχούν στους διατιθέμενους παραγωγικούς συντελεστές των ΑΕΙ και αναφέρονται τόσο στους πόρους που καταναλώνονται για την παροχή εκπαιδευτικών υπηρεσιών (εισροές), όσο και στα ίδια τα αποτελέσματα της εκπαιδευτικής διαδικασίας (εκροές).
Regarding the alarming low turnout rate in European elections in some countries where voting is not compulsory and the democratic deficit it causes across Europe, the present project aims at restoring electoral faith among EU citizens by addressing the four main causes for low electoral participation found in collaboration with our partner towns: the lack of interest and of knowledge of the democratic workings of the EU, the mistrust towards European institutions and the lack of sense of belonging to Europe
Digital Security: Cybersecurity, Privacy and Trust H2020-DS-2014-1 Topic: DS-01-2014: Privacy. The OPERANDO project will develop a platform that will be used by independent Privacy Service Providers (PSPs) to provide comprehensive user privacy enforcement in the form of a dedicated online service, called “Privacy Authority”. The OPERANDO platform will support flexible and viable business models, including targeting of individual market segments such as public administrations, social networks and Internet of Things.
Recent advances in telecommunications and database systems have allowed the scientific community to efficiently mine vast amounts of information worldwide and to extract new knowledge by discovering hidden patterns and correlations. Nevertheless, all this shared information can be used to invade the privacy of individuals through the use of fusion and mining techniques. Simply removing direct identifiers such as name, SSN, or phone number is not anymore sufficient to prevent against these practices. In numerous cases, other fields, like gender, date of birth and/or zipcode, can be used to re-identify individuals and to expose their sensitive details, e.g. their medical conditions, financial statuses and transactions, or even their private connections. The scope of this work is to provide an in-depth overview of the current state of the art in Privacy-Preserving Data Publishing (PPDP) for relational data. To counter information leakage, a number of data anonymisation methods have been proposed during the past few years, including k-anonymity, l-diversity, t-closeness, to name a few. In this study we analyse these methods providing concrete examples not only to explain how each of them works, but also to facilitate the reader to understand the different usage scenarios in which each of them can be applied. Furthermore, we detail several attacks along with their possible countermeasures, and we discuss open questions and future research directions.
Presentation for the “User Empowerment Technologies for Privacy, Regulation and Compliance” panel at Computers, Privacy & Data Protection 2018 Conference (CPDP2018)
Providing users with a lot of information might sound ideal in many scenarios, nonetheless, this may often be very annoying for the end user. To limit the amount of information that has to be processed by the user, to a set that is more relevant for his needs, most service providers use recommender systems. Undoubtedly, to provide someone with proper recommendations, one needs some background knowledge about this individual. Nonetheless, depending on the nature of the query, the database and the means to acquire this background knowledge, a lot of sensitive user information can be leaked to the system, something that many companies try to monetize. To address such privacy issues, privacy preserving recommendation systems have been developed. In this work we introduce an efficient collaborative filtering system which uses state of the art encryption primitives to offer privacy to both the server and the client, while simultaneously providing quality recommendations to the latter.
In an attempt to make our cities more sustainable and resource efficient, we are transforming them into what we now call Smart Cities. This transformation is heavily dependent on ICT, where a huge network of interconnected sensors and other devices is deployed to monitor many aspects of urban life in real-time. Recently, Solanas et al.  introduced the concept of Smart Health (s-Health) in which the resources of a smart city are exploited to facilitate the provision of healthcare services to citizens. In this position paper we explore the potentials of s-Health in stress management, providing a conceptual framework which could be used not only to monitor the stress level, but to provide more advanced ICT intervention.
The Human Genome Project has generated a great wealth of information. Currently, almost all human genome has been sequenced and now it is time to identify the functionality of each gene. The sequence of base pairs accounts for approximately 3 billion elements. While there are many efficient algorithms and implementations to mine this information, doing it privately is a great challenge. Current state-of-the-art methods have improved their efficiency, but they are not practical yet. In this article, we introduce several protocols to drastically boost the performance of genome mining processes while guaranteeing privacy, thus, enabling practical implementations. We describe how to solve the private set intersection problem and a set of pattern matching queries with privacy. The proposed protocols are server-assisted and we prove that they are secure under the semi-honest model. We report the assessment of our solution using synthetic datasets and prove their efficiency.
Millions of people around the globe try to find their other half using Information and Communication Technologies. Although this goal could be partially sought in social networks, specialized applications have been developed for this very purpose. Dating applications and more precisely mobile dating applications are experiencing a continuous growth in the number of registered users worldwide. Thanks to the GPS and other sensors embedded in off-the-shelves mobile devices, dating mobile apps can provide location aware content, not only about the surroundings, but also about nearby users. Even if these applications have millions of registered users, it can hardly be said that they are using the best standards of security and privacy protection. In this work we study some of the major dating applications and we report some of the risks to which their users are exposed to. Our findings indicate that a malicious user could easily obtain significant amounts of fine-grained personal information about users.
Many organizations, enterprises or public services collect and manage personal data of individuals. These data contain knowledge that is of substantial value for scientists and market experts, but carelessly disseminating them can lead to significant privacy breaches, as they might reveal financial, medical or other personal information. Several anonymization methods have been proposed to allow the privacy preserving sharing of datasets with personal information. Anonymization techniques provide a trade-off between the strength of the privacy guarantee and the quality of the anonymized dataset. In this work we focus on the anonymization of sets of values from continuous domains, e.g., numerical data, and we provide a method for protecting the anonymized data from attacks against identity disclosure. The main novelty of our approach is that instead of using a fixed, given generalization hierarchy, we let the anonymization algorithm decide how different values will be generalized. The benefit of our approach is twofold: a) we are able to generalize datasets without requiring an expert to define the hierarchy and b) we limit the information loss, since the proposed algorithm is able to limit the scope of the generalization. We provide a series of experiments that demonstrate the gains in terms of information quality of our algorithm compared to the state-of-the-art.
In this paper we develop a Data Envelopment Analysis (DEA) assessment framework to evaluate the research activity of academic staff in comparable university departments. The selected factors (inputs and outputs) have a meaningful interpretation in the analysis and provide us with the ability to perform the assessments by taking into account both the extent as well as the quality of the research records. We take as inputs the duration of the research activity and the salary received. We consider as outputs the number of publications in journals ranked as A+ or A, the number of publications in journals ranked as B or C, the publications in unranked journals, publications in conferences and the number of citations (excluding self-citations). We draw the journal rankings from the Excellence in Research for Australia (ERA) 2010 journal classification system. The data are drawn from Scopus, Google Scholar, university personal records and CVs. In DEA, each DMU is free to select the weight variables that maximize its relative efficiency. However, the arbitrary trade off among the factors may not be in line with the decision maker. To facilitate the incorporation of a quality aspect in our assessments, i.e. to reward the quality research outcome while diminishing the contribution of extensive publications in low quality journals in the overall research performance, we restrict the weight space by imposing assurance region constraints. In particular, we utilize the Analytical Hierarchy Process (AHP) for enabling a group of experts to express their individual preferences with respect to the relative importance of the factors, and to aggregate them into group preferences that denote not only the ranking of the factors, but also the intense of the group preference over the factors. By translating this information into assurance region constraints, we incorporate into the DEA assessments the preferences of decision makers over the relative importance of the factors. To meet the above requirements, we developed a web-based group DSS which integrates AHP and DEA and enables the asynchronous cooperation of the experts. Particularly, the core of the system integrates AHP with an algorithm which encapsulates the CCR, the BCC and the additive DEA models and supports the incorporation of weight restrictions. In addition, the software provides cross efficiency as a post-analysis tool as well as it provides reports with efficiency scores, slacks and projections on the efficient frontier. Thus, we develop a robust framework to evaluate the research activity of academic staff. We illustrate both the proposed hybrid approach and the system with the aforementioned academic data set.
Online Social Networks (OSNs) are currently playing a crucial role in our everyday social life. Their great growth has sparked the interest of hackers and individual users that try to disclose as much information as possible, which in many cases unfortunately is possible. In most such events, the users’ privacy settings are bypassed by the leakage of their shared media content. To address this challenging but important research problem, we introduce a new distributed scheme for media content sharing on online social networks that may minimize users’ privacy exposure, through automated procedures. The novelty of the proposed scheme is the ability to enforce a user’s privacy policies across multiple online social networks, even if she is not subscribed to all of them, without using a trusted third party. Moreover, the proposed framework is a step towards enabling OSNs to interact, exchange information with equal rights, independently of their size, focus and underlying infrastructure.
A key aspect of online social networks (OSNs) is the user-generated multimedia content shared online. OSNs like Facebook have to deal with up to 300 million photos uploaded on a daily basis, both video- and audio-related social networks have also started to gain important shares of the market. Although the security and privacy mechanisms deployed by OSNs can cope with several risks and discourage inexperienced users from malicious behaviours, many issues still need to be addressed. Uploaded multimedia content carries information that could be transmitted virally and almost instantaneously within OSNs and beyond. OSNs could be seen as a multimedia heaven for users. However, in many cases they might end up being the user's personal hell with information disclosure or distortion, contrary to his/her will. In this article, we outline the most significant security and privacy issues related to the exposure of multimedia content in OSNs and we discuss possible countermeasures.
The quality of life has been significantly improved and one of the main reasons is the medical advances of the past decades. Nevertheless, to further advance the research and services in the field, practitioners, researchers and health organizations should share more information. While this need is indisputable, the sensitivity of the information demands that it is preprocessed, so that the published data are anonymized and individuals cannot be identified. The scope of this work is to highlight the difficulties in providing automated anonymization approaches for medical records without consulting experts in the field. One of the major problems that is going to be highlighted is that Quasi-Identifiers (QI) are not independent. It is well known that combinations of QIs can be used to infer other relevant information. Nevertheless, this work tries to exploit the other way of information flow, we show how sensitive attributes can be exploited to derive information about the QIs, leading to many privacy hazards for the patients whose records are shared. To this extent, we illustrate some relevant examples and discuss probable counter-measures.
Modern mobile and wearable devices are enabling the realization of so-called ubiquitous computing. This provides citizens the technological means to contribute to urban management by becoming sensors within a smart city. Notwithstanding, the health sector is a very crucial factor for city management, imposing restrictions to the decisions directly or indirectly. The question that arises is given the current technological advances, could we collect health related data from citizens without violating their privacy? In this work we propose a methodology that can be used to allow citizens to send their data without disclosing their identity, while simultaneously enabling almost real-time urban-scale virological and epidemiological data monitoring.
With the continuous adoption of the web and the increase of connection speeds, people are more and more sharing multimedia content. The main problem that is created by this approach is that the shared content become less and less search-friendly. The information that is shared, cannot be easily queried, so a big part of the web becomes inaccessible. To this end, there is a big shift towards adopting new metadata standards for image and video that can efficiently help with queries over image and videos. In this work we extend our proposed method of embedding metadata as QR codes in gray scale images, to color video files with a slightly modified algorithm to make the decoding faster. We then examine the experimental results regarding the compressed file size, using a lossless encoding and the distortion of the frames of the video files. Storing the metadata inside the multimedia stream with QR format has several advantages and possible new uses that are going to be discussed.
Users’ privacy is a key element on social networks. People are becoming aware of the dangers that emerge from thoughtless disclosure of information on Social Networks and demand more privacy-aware platforms. Several solutions have been proposed in order to achieve an acceptable level of privacy, however, almost all of them ignore one of the key aspects, multimedia content. Extended experiments show that major SNs do not apply any watermarking or steganographic scheme on their services. Based on this fact, this study illustrates a novel solution, using well-known techniques.
Due to the rise of social media, several new needs, problems and challenges have emerged in users' privacy and security policies. Two very serious problems that should be addressed are identity theft and unauthorized content sharing. In this work we propose a more secure scheme for privacy in social networks by the use of watermarking that manages to diminish these problems, at least inside current social media architectures, without the need for building them from scratch.
Image steganography has various usages due to the recent advances in technology, specially in the field of data communication. Even though steganography has been used so far mostly to secretly embed data, we make use of it for embedding cleartext data, which everyone can has access. This work proposes a new method for cross format metadata embedding in images that is based on QR codes, that seems to have several significant advantages over current method of storing metadata.
Customer satisfaction is an essential tool in the hands of the enterprises and organizations that are interested in the quality of their products or services. The measurement of customers satisfaction offers a useful feedback about client’s preferences and expectations. This thesis suggests that Clustering with data mining techniques and further use of clusters as an input into the multicriteria MUSA method, can be an effective solution to the problem of inhomogenous data acquired from questionnaires.
Η παρούσα εργασία έχει ως σκοπό να παρουσιάσει την πολυκριτήρια μέθοδο UTADIS που χρησιμοποιείται για την ταξινόμηση ενός συνόλου εναλλακτικών λύσεων σε προκαθορισμένες ομάδες. Κατόπιν θα παρουσιαστεί ένα παράδειγμα εφαρμογής της μεθόδου όπου θα καταταχτούν σε 3 προκαθορισμένες ομάδες μια σειρά καταστημάτων προς αξιολόγηση. Για την λύση των γραμμικών προγραμμάτων που προκύπτουν κατά την εφαρμογή της μεθόδου χρησιμοποιούμε το λογισμικό ανοικτού κώδικα (open source) LpSolve 5.5 ένα από τα καλύτερα στο είδος του και με δυνατότητες που συναγωνίζεται τα επαγγελματικά πακέτα εφαρμογών.
Η παρούσα εργασία έχει ως σκοπό να παρουσιάσει συνοπτικά την μέθοδο ιεραρχικής ανάλυσης αποφάσεων και να αποτελεί το εγχειρίδιο χρήσης της εφαρμογής που το συνοδεύει. Η εφαρμογή χρησιμοποιεί την μέθοδο της ιεραρχικής ανάλυσης αποφάσεων για να υποστηρίξει τον χρήστη ώστε να πάρει την ορθότερη απόφαση που θα είναι βασισμένη στις προτιμήσεις του
Η παρούσα εργασία μελετά το πρόβλημα του ελέγχου αυθεντικότητας και των πνευματικών δικαιωμάτων στα πολυμέσα. Η μέθοδος της υδατογράφησης είναι μια λύση που προτείνεται από πολλούς ερευνητές διότι ενσωματώνει την ασφάλεια πάνω στα πολυμέσα και παραμένει εκεί (σε αντίθεση για παράδειγμα με την κρυπτογραφία όπου η ασφάλεια χάνεται μετά την αποκρυπτογράφηση). Η εργασία πρέπει να αντιμετωπισθεί σαν εισαγωγή στα πολυμέσα και την ψηφιακή υδατογράφηση. Ειδικότερα αυτή η εργασία ασχολείται με τον έλεγχο αυθεντικότητας και τα πνευματικά δικαιώματα στις εικόνες.
Courses : Web Application Development with Joomla.
Courses : Computer Use, Word Processing, Internet.
Courses : Computer Use, Advanced Programming Techniques On the Internet.
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