2018년 May 14일

Tutorial Talks

Tutorial Talk 1: Understanding and Solving the Privacy Challenges in the Smart City

Time: June 5th, 13:50 – 17:40

Speakers: Dr. David Eckho (TUMCREATE) and Isabel Wagner (De Montfort University)

Abstract:
Many modern cities strive to integrate information technology into every aspect of city life to create so-called smart cities. Smart cities rely on a large number of application areas and technologies to realize complex interactions between citizens, third parties, and city departments. This overwhelming complexity is one reason why holistic privacy protection only rarely enters the picture. A lack of privacy can result in discrimination and social sorting, creating a fundamentally unequal society. To prevent this, we believe that a better understanding of smart cities and their privacy implications is needed. In this tutorial, we therefore systematize the application areas, enabling technologies, privacy types, attackers and data sources for the attacks, giving structure to the fuzzy term “smart city”. Based on our taxonomies, we describe existing privacy-enhancing technologies, review the state of the art in real cities around the world, and discuss promising future research directions. Our tutorial can be seen as a guide to understanding and designing privacy-friendly smart city applications. This tutorial is particularly topical because ASIACCS is taking place in Songdo, which was designed from the ground up to be the world’s first truly smart city.

Biography:
Dr. David Eckho (david.eckho @tum-create.edu.sg) is a postdoctoral research fellow and one of the principal investigators at TUMCREATE, Singapore, a joint research institute by TU Munich and Nanyang Technological University, Singapore. David received his Ph.D. degree in engineering (Dr.-Ing., with distinction) and his M.Sc. degree in computer science (Dipl.-Inf. Univ., graduating top of his class) from the University of Erlangen in 2016 and 2009, respectively. In 2016 he was a visiting scholar with the group of Prof. Lars Kulik at the University of Melbourne, Australia. In October 2016, he joined TUMCREATE in Singapore in the group of Prof. Alois Knoll. His research interests include privacy protection, smart cities, vehicular networks, and intelligent transportation systems with a particular focus on modelling and simulation.
Dr. Isabel Wagner (isabel.wagner@dmu.ac.uk) is a Senior Lecturer in Computer Science (Cybersecurity) at De Montfort University in Leicester, UK. Dr. Wagner (previously Dietrich) received her Ph.D in Engineering (Dr.-Ing.) and M.Sc. in Computer Science (Dipl.- Inf. Univ.) from the Department of Computer Science, University of Erlangen in 2010 and 2005, respectively. In 2011 she was a JSPS Postdoctoral Fellow in the research group of Prof. Masayuki Murata at the University of Osaka, Japan. In 2017, she was recognized as a Senior Member of the ACM. Her research focuses on privacy and privacy-enhancing technologies, particularly on metrics to quantify the effectiveness of privacy protection mechanisms, as well as on privacy-enhancing technologies in smart cities, genomics, perceptual applications, vehicular networks, and smart grids. She is also interested in bio-inspired mechanisms for privacy.


Tutorial Talk 2: Recent Trends in Adversarial Machine Learning (AML)

Time: June 6th, 14:00 – 15:20

Speakers: Somesh Jha (Univ of Wisconsin, Madison)

Abstract:
Fueled by massive amounts of data, models produced by machine-learning (ML) algorithms, especially deep neural networks, are being used in diverse domains where trustworthiness is a concern, including automotive systems, finance, health care, natural language processing, and malware detection. Of particular concern is the use of ML algorithms in cyber-physical systems (CPS), such as self-driving cars and aviation, where an adversary can cause serious consequences. In this talk, we will address the following question: what happens to machine-learning algorithms in the presence of a malicious adversary? This area of machine learning is called adversarial ML (AML), and recently the interest in this area has simply exploded. We will survey recent results in the field and close with some open problems.

Biography:
Somesh Jha received his B.Tech from Indian Institute of Technology, New Delhi in Electrical Engineering. He received his Ph.D. in Computer Science from Carnegie Mellon University in 1996. Currently, Somesh Jha is the Grace Wahba Professor in the Computer Sciences Department at the University of Wisconsin (Madison), which he joined in 2000. His work focuses on analysis of security protocols, survivability analysis, intrusion detection, formal methods for security, and analyzing malicious code. Recently, he has also worked on privacy-preserving protocols and adversarial ML. Somesh Jha has published over 150 articles in highly-refereed conferences and prominent journals. He has won numerous best-paper awards. Somesh also received the NSF career award in 2005 and became an ACM fellow in 2017 and IEEE fellow in 2018.