@article {vassilakis2019HV,
	title = {Blockchain technologies for leveraging security and privacy},
	journal = {Homo Virtualis},
	volume = {2},
	number = {1},
	year = {2019},
	pages = {7{\textendash}14},
	abstract = {The contemporary internet has developed into a complex ecosystem involving humans, services, applications, machines and applications that interact exchanging information, ranging from e-mail messages and social media content to crowdsourcing data and videoconferencing. In this context, a number of security threats such as viruses and malware exist, while additionally the users{\textquoteright} privacy is jeopardized by threats such as personal data leakage, usage pattern monitoring, and so forth. The IoT trend renders the Internet ecosystem even more complex, by adding a rich set of services, applications and machines, many of them backed by new user roles; these elements are weaved into everyday life and industry alike. This increases both the number of opportunities available to threat agents for exploitation and the volume and value of the underlying infrastructure and data, increasing thus the user risk level. In this paper, we explore how the Blockchain technology can be used to leverage security and privacy in the modern Internet, both by providing underpinnings for preventive measures and by facilitating digital forensic evidence collection storage, safeguarding and controlled access.},
	keywords = {blockchain, Internet of Things, privacy, security},
	issn = {2585-3899},
	doi = {http://dx.doi.org/10.12681/homvir.20188},
	url = {https://ejournals.epublishing.ekt.gr/index.php/homvir/article/view/20188},
	author = {Costas Vassilakis}
}
@article {margaris-tldks,
	title = {Enhancing User Rating Database Consistency Through Pruning},
	journal = {Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXIV},
	volume = {LNCS, volume 10620},
	number = {Special issue on Consistency and Inconsistency in Data-centric Applications},
	year = {2017},
	pages = {33{\textendash}64},
	publisher = {Springer Berlin Heidelberg},
	abstract = {Recommender systems are based on information about users{\textquoteright} past behavior to formulate recommendations about their future actions. However, as time goes by the interests and likings of people may change: people listen to different singers or even different types of music, watch different types of movies, read different types of books and so on. Due to this type of changes, an amount of inconsistency is introduced in the database since a portion of it does not reflect the current preferences of the user, which is its intended purpose. In this paper, we present a pruning technique that removes old aged user behavior data from the ratings database, which are bound to correspond to invalidated preferences of the user. Through pruning (1) inconsistencies are removed and data quality is upgraded, (2) better rating prediction generation times are achieved and (3) the ratings database size is reduced. We also propose an algorithm for determining the amount of pruning that should be performed, allowing the tuning and operation of the pruning algorithm in an unsupervised fashion. The proposed technique is evaluated and compared against seven aging algorithms, which reduce the importance of aged ratings, and a state-of-the-art pruning algorithm, using datasets with varying characteristics. It is also validated using two distinct rating prediction computation strategies, namely collaborative filtering and matrix factorization. The proposed technique needs no extra information concerning the items{\textquoteright} characteristics (e.g. categories that they belong to or attributes{\textquoteright} values), can be used in all rating databases that include a timestamp and has been proved to be effective in any size of users-items database and under two rating prediction computation strategies. Note: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author{\textquoteright}s copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.},
	keywords = {Collaborative filtering, Evaluation, Internet of Things, performance, Recommendation Applications, Social Networks, Web services},
	doi = {10.1007/978-3-662-55947-5_3},
	url = {https://link.springer.com/article/10.1007\%2Fs11761-017-0216-y},
	author = {Dionisis Margaris and Costas Vassilakis}
}
@article {margaris-soca-17,
	title = {Exploiting Internet of Things information to enhance venues{\textquoteright} recommendation accuracy},
	journal = {Service Oriented Computing and Applications},
	volume = {todb},
	year = {2017},
	month = {aug},
	pages = {393{\textendash}409},
	publisher = {Springer Nature},
	abstract = {In this paper, we introduce a novel recommendation algorithm, which exploits data sourced from web services provided by the Internet of Things in order to produce more accurate venue recommendations. The proposed algorithm provides added value for the web services offered by the Internet of Things and enhances the state-of-the-art in this algorithm category by taking into account (a) web of things data regarding the contexts of the user and the context of the venues to be recommended (restaurants, movie theatres, etc.), such as the user{\textquoteright}s geographical position, road traffic and weather conditions, (b) qualitative aspects of the venues, such as price, atmosphere or service, (c) the semantic similarity of venues and (d) the influencing factors between social network users, derived from user participation in social networks. The combination of these features leads to more accurate and better user-targeted recommendations. We also present a framework which incorporates the above characteristics, and we evaluate the presented algorithm, both in terms of performance and recommendation quality.},
	keywords = {collaborative filtering, Evaluation, Internet of Things, performance, Recommendation Applications, Social Networks, Web services},
	doi = {10.1007/s11761-017-0216-y},
	url = {https://link.springer.com/article/10.1007/s11761-017-0216-y},
	author = {Dionisis Margaris and Costas Vassilakis}
}
