Aris Gkoulalas-divanis  Aris Gkoulalas-divanis photo       

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Senior Data Privacy Research Scientist, IBM Master Inventor
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Professional Associations:  ACM  |  ACM SIGKDD  |  BDVA  |  British Computer Society  |  IEEE   |  IEEE Computer Society  |  Sigma Xi

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Aris Gkoulalas-Divanis received the BS degree from the University of Ioannina (2003), the MS from the University of Minnesota (2005) and the PhD (with honors) from the University of Thessaly (2009), all in Computer Science. His PhD dissertation was awarded the Certificate of Recognition and Honourable Mention in the 2009 ACM SIGKDD Dissertation Award. From March 2009 until February 2010, he was appointed as a postdoctoral research fellow in the Department of Biomedical Informatics of Vanderbilt University, working on privacy-preserving methods for medical data sharing. On March 2010, he joined IBM Research in Zurich, in the capacity of a Research Staff Member in the Information Analytics group and in 2011 he was nominated as a member of the IBM Business and Technical Leadership Resources.

From March 2012 until June 2016 he was working in the Smarter Cities Technology Center of IBM Research in Ireland, conducting research on data privacy for Smarter Cities. In this position he received three Invention Achievement Awards (Plateus) and was recognized in the 2014 Manager's Choice Award. In this capacity, he led the Identification of Privacy Vulnerabilities (IPV) and the Privacy Masking & Anonymization (PRIMA) projects for IBM, which involved methods for automatically discovering privacy vulnerabilities in datasets of various data types (e.g., relational, transaction, sequential, mobility), and applying data masking and anonymization to block them. In 2012 Aris served as the Global Technology Outlook (GTO) Advocate for IBM Research-Ireland. From 2012 until 2016, he also served as the Security and Privacy PIC co-Chair of IBM Research worldwide. Since 2015, he leads the Data Protection and Privacy Working Group (under Task Force 6) of the Big Data Value Association (www.bdva.eu), a non-for-profit organization consisting of 24 founding members from large and SME industry, and research.

In June 2016, Aris joined the IBM Watson Health lab in Cambridge, MA, in the capacity of a Senior Data Privacy Research Scientist, responsible for the design of data de-identification methods and technologies for protecting sensitive medical information.

Aris Gkoulalas-Divanis is a regular reviewer for Data and Knowledge Engineering (DKE), Transactions on Knowledge and Data Engineering (TKDE), Knowledge and Information Systems (KAIS), and Data Mining and Knowledge Discovery (DMKD). He serves as a Category Editor for ACM Computing Reviews as well as is appointed in the Editorial Board of the International Journal of Knowledge-Based Organizations (IJKBO). From January to August 2009 he served as an Associate Editor for ACM Crossroads. His research interests are in the areas of databases, data mining, privacy preserving data mining, privacy in trajectories and location-based services, privacy in medical data, and knowledge hiding. In these areas, he has given many seminars in universities and research institutes as well as two tutorials (ECML/PKDD 2011, SDM 2012), he has published more than 70 research works, including four Springer books, and he has filed 12 patents. He has received an IBM A-level accomplishment for his contribution to the Urban Data Innovation work for Smarter Cities. His recent paper in the Proceedings of the National Academy of Sciences (PNAS) proposed the first approach which anonymizes medical data in a way that it remains useful for validating Genome- Wide Association Studies (GWAS), and was reported by the National Human Genome Research Institute (NHGRI) among the important advances in the last 10 years of genomic research (Eric D. Green, et al. in Nature, vol. 470, 2011). Aris Gkoulalas-Divanis is a Professional member of ACM, IEEE, SIAM and AAAS, and an at-large member of UPE and Sigma Xi.

Recent projects: IPV: Identification of privacy vulnerabilities in datasets of different data types, PRIMA: Privacy Masking & Anonymization, design and development of anonymization methods for social housing data, participation to the SIMPLICITY and the PETRA EU projects on their data privacy needs.