Privacy in the era of big data, machine learning, IoT, and 5G

Elisa Bertino is the Samuel D. Conte Professor of Computer Science at Purdue University. She serves as Director of the Purdue Cyberspace Security Lab (Cyber2Slab). In her role as Director of Cyber2SLab she leads multi-disciplinary research in data security and privacy. 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 Telcordia Technologies, and visiting professor at the Singapore Management University and the National University of Singapore. Her recent research focuses on cybersecurity and privacy of cellular networks and IoT systems, and edge analytics and machine learning for cybersecurity.  Elisa Bertino is a Fellow member of IEEE, ACM, and AAAS. She received the 2002 IEEE Computer Society Technical Achievement Award for outstanding contributions to database systems and database security and advanced data management systems, the 2005 IEEE Computer Society Tsutomu Kanai Award for pioneering and innovative research contributions to secure distributed systems, the 2014 ACM SIGSAC Outstanding Contributions Award with citation for her seminal research contributions and outstanding leadership to data security and privacy for the past 25 years, and the 2019-2020 ACM Athena Lecturer Award. 

Abstract: 

Privacy in the Era of Big Data, Machine Learning, IoT, and 5G

Technological advances, such as IoT devices, cyber-physical systems, smart mobile devices, cloud systems, data analytics, social networks and increased communication capabilities, are making possible to capture, and to quickly process and analyze huge amounts of data from which to extract information critical for many critical tasks, such as healthcare security and cyber security. In the area of cyber security, such tasks include user authentication, access control, anomaly detection, user monitoring, and protection from insider threat. By analyzing and integrating data collected on the Internet and Web one can identify connections and relationships among individuals that may in turn help with homeland protection. By collecting and mining data concerning user travels, contacts and disease outbreaks one can predict disease spreading across geographical areas. And those are just a few examples. The use of data for those tasks raises however major privacy concerns. Collected data, even if anonymized by removing identifiers such as names or social security numbers, when linked with other data may lead to re-identify the individuals to which specific data items are related to. Also, as organizations, such as governmental agencies, often need to collaborate on security tasks, data sets are exchanged across different organizations, resulting in these data sets being available to many different parties. Privacy breaches may occur at many different layers and components in our interconnected systems. In this talk, I first present an interesting privacy attack that exploits paging occasion in 5G cellular networks and possible defenses.  Such attack shows that achieving privacy is challenging and there is no unique technique that one can use; rather one must combine different techniques depending also on the intended use of data. Examples of these techniques and their applications are presented. Finally, I discuss the notion of data transparency – critical when dealing with user sensitive data, and elaborate on the different dimensions of data transparency.