Abstract
Technological advances and new applications, such as
sensors, cyber-physical systems, smart mobile devices, cloud systems, data analytics and social networks make it possible to capture and quickly process and analyze enormous amounts of data from which information crucial for security-related tasks can be extracted. In cybersecurity, such tasks include user authentication, access control, anomaly detection, user monitoring, and protection against insider threats. By analyzing and integrating data collected across the Internet and the Web, connections and relationships between individuals can be identified that can in turn help protect the homeland. By collecting and exploiting data on user journeys and disease outbreaks, one can predict the spread of diseases across geographic areas. And those are just a few examples; there are certainly many other areas where data technologies can play an important role in improving security. However, using data for security tasks raises major privacy concerns. Collected data, even if anonymized by removing identifiers such as names or social security numbers, when linked to other data, may have the effect of re-identifying the individuals to whom specific data items relate. Because organizations, such as government agencies, often have to collaborate on security tasks, datasets are exchanged between different organizations, making these datasets available to many different parties. In addition to using data for analytics, security tasks such as authentication and access control may require detailed information about users. An example is multi-factor authentication, which may also require biometric data from users in addition to a password or certificate. Recently proposed continuous authentication techniques extend the access control system. If this information is misused or stolen, it could lead to privacy breaches.
It seems then that to achieve security we must give up privacy. However, this does not necessarily have to be the case. Recent developments in the field of cryptography make it possible to work with encrypted data, for example to perform analyzes on encrypted data. However, much more needs to be done as the specific data privacy techniques to be used are highly dependent on the specific use of data and the security tasks to be performed. Also, current techniques still cannot meet the efficiency requirements for use with big data sets.
In this lecture we will discuss methods and techniques to enable this reconciliation and identify research directions.

About the Speaker
Elisa Bertino is a professor of computer science at Purdue University
and serves as research director of the Center for Information and
Information Assurance and Security Research (CERIAS). She is also an adjunct professor of Computer Science and Infotechnology at RMIT. Before joining Purdue in 2004, she was a professor and department head in the Department of Computer Science and Communications at the University of Milan. She was 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 recent research focuses on database security, digital identity management, policy systems, and web services security. She is a Fellow of ACM and of IEEE. She received the IEEE Computer Society 2002 Technical Achievement Award and the IEEE Computer Society 2005 Kanai Award. She currently serves as EiC of IEEE Transactions on Dependable and Secure Computing

http://www.cerias.purdue.edu

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