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Keynote Lectures

Not ACID, not BASE, but SALT: A Transaction Processing Perspective on Blockchains
Stefan Tai, TU Berlin, Germany

Privacy-preserving Machine Learning over Sensitive Data
Jin Li, Guangzhou University, China

Big Data, Smart Data and Imbalanced Classification: Preprocessing, Models and Challenges
Francisco Herrera, University of Granada, Spain

 

Not ACID, not BASE, but SALT: A Transaction Processing Perspective on Blockchains

Stefan Tai
TU Berlin
Germany
 

Brief Bio
Stefan Tai is Full Professor and Head of Chair Information Systems Engineering at TU Berlin, Germany (2014-present). Prior to that, he was a Full Professor at the Karlsruhe Institute of Technology, Germany (2007-2014) and a Research Staff Member at the IBM Thomas J. Watson Research Center in New York, USA (1999-2007). Overall, Stefan has over 20 years of experience in cutting-edge IT research and development in both academia and in industry. Stefan works on creating next-generation IT software systems that provide a necessary foundation to address larger societal challenges. His research interests include distributed information systems, cloud services and systems, and the Web as a platform for technology, social, and business innovation. Currently, Stefan works passionately on blockchain-based solutions for a diversity of application domains. Stefan was involved in a number of industrial developments (mostly with IBM), holds several patents in the area of enterprise middleware and distributed systems, and has published over 100 peer-reviewed research articles. A new book on “Cloud Service Benchmarking” will appear in 2017, to be published by Springer. Stefan serves regularly as PC member of key conferences, as member of four editorial boards of relevant journals in the field, and as expert evaluator for scientific programs worldwide. He also acts as advisor to global IT companies and as mentor to start-up companies.


Abstract
Traditional ACID transactions, typically supported by relational database management systems, emphasize database consistency. BASE provides a model that trades some consistency for availability, and is typically favored by cloud systems and NoSQL data stores. With the increasing popularity of blockchain technology, another alternative to both ACID and BASE is introduced: SALT. Compromising scalability (for now), emphasis is set on symmetric, admin-free (but outcome-agreed), ledgered, and tamper-proof decentralized transaction processing. In this keynote, we discuss these three alternatives and describe current research activities and directions for building decentralized applications using blockchains.



 

 

Privacy-preserving Machine Learning over Sensitive Data

Jin Li
Guangzhou University
China
 

Brief Bio
Jin Li is a professor at Guangzhou University. His research interests include design of secure protocols in Cloud Computing, cryptography, and machine learning. He served as a senior research associate at Korea Advanced Institute of Technology (Korea), VirginiaTech (U.S.A.), and Illinois Institute of Technology. He has published more than 100 papers in international conferences and journals, including IEEE INFOCOM, IEEE Transaction on Parallel and Distributed Computation, IEEE Transactions on Computers, IEEE Transactions on Cloud Computing and ESORICS etc. His work has been cited more than 5000 times at Google Scholar and the H-Index is 28.
He also served as program chairs and committee for many international conferences such as CSE 2017, ISICA 2015, 3PGCIC20 14, ICCCN and CloudCom etc. He received two National Science Foundation of China (NSFC) Grants for his research on Security and Privacy in Cloud Computing. He is also panel of NSFC He is PI for more than 15 funding. He has been selected as one of science and technology new stars in Guangzhou and outstanding young scholar in Guangdong province.


Abstract
Machine learning has been applied widely for classifying and recognizing complex data. However, security and privacy issues arise when the data are sensitive or the computing and data storage services are outsourced in the cloud computing. When the data are sensitive and the data evaluators are not fully trusted, the data have to be encrypted and traditional methods cannot be utilized to process the data. In this talk, I will introduce some basic solutions and challenges in this topic. Finally, I will show our method to solve this problem.



 

 

Big Data, Smart Data and Imbalanced Classification: Preprocessing, Models and Challenges

Francisco Herrera
University of Granada
Spain
 

Brief Bio
Francisco Herrera is a Professor in the Department of Computer Science and Artificial Intelligence at the University of Granada, Spain. He has been the supervisor of 36 Ph.D. students. He has published more than 290 journal papers (H-index 99) that have received more than 35000 citations (Scholar Google). He is co-author of the books "Genetic Fuzzy Systems" (World Scientific, 2001) and "Data Preprocessing in Data Mining" (Springer, 2015). He currently acts as Editor in Chief of the international journals "Information Fusion" (Elsevier) and “Progress in Artificial Intelligence (Springer). He acts as editorial board member of a dozen of journals, among others: International Journal of Computational Intelligence Systems, IEEE Transactions on Fuzzy Systems, IEEE Transactions on Cybernetics, Information Sciences, Knowledge and Information Systems, Fuzzy Sets and Systems, Applied Intelligence, Knowledge-Based Systems, and Swarm and Evolutionary Computation. He is a Fellow of the European Coordinating Committee for Artificial Intelligence and the International Fuzzy Systems Association. He has been given many awards and honors for his personal work or for his publications in journals and conferences. His areas of interest include, among others, data science, data preprocessing, cloud computing and big data.


Abstract
Available Soon.



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