Prof. Elisa Bertino, Department of Computer Science, Purdue University, USA
Topic: Data Security and Privacy in the IoT
Abstract: The Internet of Things (IoT) paradigm refers to the network of physical objects or "things" embedded with electronics, software, sensors, and connectivity to enable objects to exchange data with servers, centralized systems, and/or other connected devices based on a variety of communication infrastructures. IoT makes it possible to sense and control objects creating opportunities for more direct integration between the physical world and computer-based systems. IoT will usher automation in a large number of application domains, ranging from manufacturing and energy management (e.g. SmartGrid), to healthcare management and urban life (e.g. SmartCity). However, because of its fine-grained, continuous and pervasive data acquisition and control capabilities, IoT raises concerns about data security and privacy. Deploying existing security solutions to IoT is not straightforward because of device heterogeneity, highly dynamic and possibly unprotected environments, and large scale. In this talk, after outlining key challenges in IoT data security and privacy, we present initial approaches to securing IoT data, including recent edge-based security solutions for IoT security.
Bio: Elisa Bertino is professor of Computer Science at Purdue University. Prior to joining Purdue, she was a professor and department head at the Department of Computer Science and Communication of the University of Milan. She has been a visiting researcher at the IBM Research Laboratory (now Almaden) in San Jose, at the Microelectronics and Computer Technology Corporation, at Rutgers University, at Telcordia Technologies. Her main research interests include security, privacy, database systems, distributed systems, and sensor networks. Her recent research focuses on digital identity management, biometrics, IoT security, security of 4G and 5G cellular network protocols, and policy infrastructures for managing distributed systems. Prof. Bertino has published more than 700 papers in all major refereed journals, and in proceedings of international conferences and symposia. She has given keynotes, tutorials and invited presentations at conferences and other events. She is a Fellow member of ACM, IEEE, and AAAS. She received the 2002 IEEE Computer Society Technical Achievement Award for "For outstanding contributions to database systems and database security and advanced data management systems" and the 2005 IEEE Computer Society Tsutomu Kanai Award for “Pioneering and innovative research contributions to secure distributed systems”.
Prof. Latifur Khan, University of Texas at Dallas, USA
Topic: Big Data Stream Analytics and Its Applications
Abstract: Data streams are continuous flows of data. Examples of data streams include network traffic, sensor data, call center records and so on. Data streams demonstrate several unique properties that together conform to the characteristics of big data (i.e., volume, velocity, variety and veracity) and add challenges to data stream mining. In this talk we will present an organized picture on how to handle various data mining/machine learning techniques in data streams. In addition, we will present a number of stream classification applications such as adaptive website fingerprinting, textual stream analytics (political actor identification over textual stream), attack trace classification using good quality similarity metrics (metric learning) and domain adaptation. This research was funded in part by NSF, NASA, Air Force Office of Scientific Research (AFOSR), NSA, IBM Research, HPE and Raytheon.
Bio: Dr. Latifur Khan is currently a full Professor (tenured) in the Computer Science department at the University of Texas at Dallas, USA where he has been teaching and conducting research since September 2000. He received his Ph.D. degree in Computer Science from the University of Southern California (USC) in August of 2000. Dr. Khan is an ACM Distinguished Scientist and received Fellow of SIRI (Society of Information Reuse and Integration) award in Aug, 2018. He has received prestigious awards including the IEEE Technical Achievement Award for Intelligence and Security Informatics and IBM Faculty Award (research) 2016.
Dr. Latifur Khan has published over 250 papers in premier journals such as VLDB, Journal of Web Semantics, IEEE TDKE, IEEE TDSC, IEEE TSMC, and AI Research and in prestigious conferences such as AAAI, IJCAI, CIKM, ICDE, ACM GIS, IEEE ICDM, IEEE BigData, ECML/PKDD, PAKDD, ACM Multimedia, ACM WWW, ICWC, ACM SACMAT, IEEE ICSC, IEEE Cloud and INFOCOM. He has been invited to give keynotes and invited talks at a number of conferences hosted by IEEE and ACM. In addition, he has conducted tutorial sessions in prominent conferences such as SIGKDD 2017, 2016, IJCAI 2017, AAAI 2017, SDM 2017, PAKDD 2011 & 2012, DASFAA 2012, ACM WWW 2005, MIS2005, and DASFAA 2007. Currently, Dr. Khan’s research area focuses on big data management and analytics, data mining and its application over cyber security, complex data management including geo-spatial data and multimedia data. His research has been supported by grants from NSF, the Air Force Office of Scientific Research (AFOSR), DOE, NSA, IBM and HPE. More details can be found at: www.utdallas.edu/~lkhan/
Prof. Karuna Pande Joshi, Unversity of Maryland, USA
Topic:Title: Towards An Automated Semantically Rich Framework for Big Data Compliance
Abstract: Big data analytics related to consumer behavior, market analysis, opinions, and recommendation often deal with end user's derived and inferred data, along with the observed data. To ensure consumer data protection, there has been a spurt in regulations, like the European Union’s General Data Protection Regulation (EU GDPR), Payment Card Industry Data Security Standard (PCI DSS) etc. that must be adhered to by Big Data Practitioners. However, these Data protection regulations are currently available only in textual format and so require significant human time and effort to ensure compliance and thereby prevent data breaches. We envision that an integrated, semantically rich, machine processable approach that captures the various data compliance regulations, as they apply to Big Data on the Cloud, will significantly help in automating an organization’s data compliance processes. In addition to saving organizational resources dedicated to compliance adherence, it will also help in proactively identifying data breaches. In this talk, we will present our preliminary results and ongoing work.
Bio: Dr. Karuna Pande Joshi is an Assistant Professor in the Department of Information Systems at UMBC. She is the UMBC Site director for the Center of Accelerated Real Time Analytics (CARTA). She also directs the Knowledge, Analytics, Cognitive and Cloud Lab. Her primary research area is Data Science, Legal Text Analytics, Cloud Computing and Healthcare IT. She has published 50 scholarly papers. Dr. Joshi has been awarded research grants by NSF, ONR, DoD, Cisco and GE Research. She was also awarded the NSF I-Corps award and TEDCO MII grant to explore commercial opportunities for her research and created a start-up on Data Science and Cloud technologies. She received her MS and Ph.D. in Computer Science from UMBC, where she was twice awarded the IBM Ph.D. Fellowship. She did her Bachelor of Engineering (Computers) from University of Mumbai. Dr. Joshi has also worked for over 15 years in the Industry primarily as an IT Project Manager. She worked as a Senior Information Management Officer at the International Monetary Fund for nearly a decade. More details can be found at: http://karuna.informationsystems.umbc.edu