The first webinar of LUE IMPACT project DigiTrust will take place on Thursday, 25th March at 4pm.
Professor Alfredo Cuzzocrea, Excellence Chair in Computer engineering financed by DigiTrust, will give a presentation entitled “Privacy-Preserving Big Data Management and Analytics in Distributed Environments: Models, Issues, Proposals”
The webinar will take place on Teams.
Abstract:
Nowadays, big data management and analytics is gaining momentum within the research community. Basically, the main issue with big data management concerns with effectively and efficiently managing massive big data repositories for a wide variety of typical data management tasks, such as representation, querying, indexing, partitioning, and so forth. On the other hand, big data analytics concerns with extracting useful, actionable knowledge from big data repositories for decision making purposes, by extending classical approaches inherited from decades of data mining and machine learning research. In this so-delineated context, the issue of supporting privacy-preserving big data management and analytics plays a first-class role, especially with respect to the wide class of emerging big data application scenarios, which range from social networks to bio-informatics, from sensors networks to web recommendation tools, from e-science systems to e-government systems, and so forth. In all these applicative settings, protecting the privacy of sensitive information, for instance personal data or aggregate data, can be clearly intended as an enabling technology. Distributed environments are the natural humus for collocating privacy-preserving big data management and analytics tasks, including the astonishing raise of blockchain technology. Among others, Cloud systems play the major role, even stirred-up by recent technological advancements that have really enhanced the ICT industry at now. More and more today, real-life Cloud-based applications, such as smart cities, intelligent transportation systems, marketplace tools and so forth, are indeed posing new challenges to privacy-preserving big data research, thus contributing to improve the scientific area.
This seminar will explore the research challenge represented by supporting privacy-preserving big data management and analytics in distributed environments, by exploring models and issues, and describing some proposed solutions. In particular, the seminar will consider the special case of supporting privacy-preserving OLAP analytics in distributed environments. This kind of big data analytics tools predicates the definition of multidimensional metaphors to be embedded into the analytics phase, and supports the achievement of richer actionable knowledge (prone to decision making) to be extracted from distributed big data repositories. On the other hand, the issue of supporting privacy preservation within OLAP analytics is still an open research problem, whit many advancements still to be achieved. According to this consideration, an innovative privacy-preserving OLAP analytics in distributed environments approach is presented in this seminar, along with the proposal of some interesting extensions that are at now under evolution.
