@conference {821,
	title = {RoG: A Pipeline for Automated Sensitive Data Identification and Anonymisation},
	booktitle = {Proceedings of the 2023 IEEE CSR Workshop on Data Science for Cyber Security (DS4CS)},
	year = {2023},
	abstract = {<p>Nowadays, the amount of data available online is constantly increasing. This data may contain sensitive or private information that can expose the person behind the data or be misused by malicious actors for identity theft, stalking, and other nefarious purposes. There is thus, a growing need to protect individuals{\textquoteright} privacy and prevent data breaches in several application domains. Protecting data privacy though, is a complex and multifaceted issue that involves a range of legal, ethical, and technical considerations. In this paper, we discuss the challenges associated with data protection, the role of automated tools, and the effectiveness of identifying and anonymising sensitive data. We then, propose a fully-automated process for sensitive data identification and anonymisation, based on Natural Language Processing (NLP) techniques, that can be applied both in big diverse datasets and to a wide range of domains.</p>
},
	keywords = {anonymisation, automated process, k-anonymity, NER, NLP, pipeline, sensitive/private data},
	author = {Sotiris Nikoletos and Stefanos Vlachos and Efstathios Zaragkas and Costas Vassilakis and Christos Tryfonopoulos and Paraskevi Raftopoulou}
}
@conference {575,
	title = {R-Susceptibility: An IR-Centric Approach to Assessing Privacy Risks for Users in Online Communities},
	booktitle = {Proceedings of the 39th Annual International ACM SIGIR Conference},
	year = {2016},
	month = {July},
	address = {Pisa, Italy},
	author = {Joanna Asia Biega and Krishna P. Gummadi and Ida Mele and Dragan Milchevski and Christos Tryfonopoulos and Gerhard Weikum}
}
@article {540,
	title = {Rewiring Strategies for Semantic Overlay Networks},
	journal = {Distributed and Parallel Databases (DAPD)},
	volume = {26},
	year = {2009},
	pages = {181{\textendash}205},
	abstract = {Semantic overlay networks cluster peers that are semantically, thematically or socially close into groups, by means of a rewiring procedure that is periodically executed by each peer. This procedure establishes new connections to similar peers and disregards connections to peers that are dissimilar. Retrieval effectiveness is then improved by exploiting this information at query time (as queries may address clusters of similar peers). Although all systems based on semantic overlay networks apply some rewiring technique, there is no comprehensive study showing the effect of rewiring on system{\textquoteright}s performance. In this work, a framework for studying the attribution of rewiring strategies in semantic overlay networks is proposed. A generic approach to rewiring is presented and several variants of this approach are reviewed and evaluated. We show how peer organisation is affected by the different design choices of the rewiring mechanism and how these choices affect the performance of the system overall (both in terms of communication overhead and retrieval effectiveness). Our experimental evaluation with real-word data and queries confirms the dependence between rewiring strategies and retrieval performance, and gives insights on the trade-offs involved in the selection of a rewiring strategy.},
	keywords = {Information retrieval, peer-to-peer networks, rewiring strategies, semantic overlay networks},
	author = {Paraskevi Raftopoulou and E.G.M. Petrakis and Christos Tryfonopoulos}
}
