Concordia University




February 27, 2020: Invited Speaker Seminar: Digital Infrastructure Security: From the Internet of Things to Cloud Analytics

Kalikinkar Mandal, Ph.D.
University of Waterloo

Thursday, February 27, 2020 at 10:30 am
Room EV002.184


In today’s networked, interconnected and distributed digital infrastructure, truly securing cyberspace requires securing each component from low-end tiny devices to large-scale data centers involving networks, devices, data, people, software, and hardware from cyberattacks. The Internet of Things (IoT), consisting of networks of physical objects, sensors, actuators, and computers, aims at integrating the physical world and the digital world for better operation, monitoring, and automation by collecting and intelligently processing data. As the IoT is quickly transforming from closed networks to the public Internet, protecting communications and real-time access of data in the IoT systems make it extremely challenging to implement security and privacy mechanisms. With recent advances in machine learning (ML), cloud and data centers deploy ML algorithms for processing large volumes of data collected from endpoint (IoT) devices to extract useful information. Data from multiple providers help to obtain better ML models by collaboratively training on a joint dataset. However, such collaborative training poses security and privacy risks of individual user data, especially when collaborative machine learning deals with sensitive data from healthcare, finance, and smartphone. Throughout this cyberspace, issues of security and privacy must be addressed.

In this talk, I will provide an overview of the security and privacy of the IoT ecosystems and the general approaches to perform privacy-preserving/secure computation for distributed systems. I will introduce two pieces of my research work: First, I will present two lightweight authenticated ciphers ACE and WAGE (NIST LWC round 2 candidates) for securing IoT applications. Then, I will present PrivFL, a privacy-preserving system for collaboratively training regression models and oblivious predictions over a highly dynamic mobile network, while guaranteeing data privacy, model privacy, and robustness to users dropping out.


Dr. Mandal is currently a Research Assistant Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, Canada. Prior to that, he was a Research Associate at the University of Washington, Seattle, USA from July 2014 - June 2016, and a Postdoctoral Fellow at the University of Waterloo from July 2016 - September 2017 and September 2013 – May 2014. He received a PhD degree in Electrical and Computer Engineering from the University of Waterloo in August 2013. His research interests include lightweight cryptography, IoT security, pseudorandom sequences, secure multiparty computation, privacy-preserving machine learning and data analytics, cloud and mobile data security, and high-speed cryptography and implementations. He has co-designed five authenticated encryption algorithms and two hash functions that are submitted to the ongoing NIST lightweight cryptography (LWC) standardization project, and four of them are now round 2 candidates.



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