Seminar Series: Privacy Enhancing Technologies (PETS) – Dr. L. Jean Camp

Dr. L. Jean Camp

Director of Center for Security and Privacy in Informatics, Computing, and Engineering 
Professor of Informatics 
Indiana University 

The Center for Accelerating Operational Efficiency (CAOE) invites you to our latest seminar series Privacy Enhancing Technologies – Challenges, Opportunities, and Advancements. The next seminar features Dr. L. Jean Camp who will discuss an approach that combines human and machine intelligence by leveraging both local, private and global data.

About the Series

Privacy-enhancing technologies (PETs) under development promise the ability to control the sharing and use of sensitive information while minimizing the risk of unauthorized use. These technologies have been under development by researchers for nearly four decades but have been slow to migrate from the research lab into operational use. In this seminar series, Privacy Enhancing Technologies – Challenges, Opportunities, and Advancements we invite luminaries from across the globe to discuss the state-of-the-art in privacy enhancing technologies describing challenges, opportunities, and advancements with respect to technology development and uptake.

About the Seminar

Machine learning (ML) and artificial intelligence offer unparalleled capacity for formal processing of digital information, including in security. Simultaneously, human information processing capacities are capable of processing different types of data enabling contextual nuanced decisions. Machine learning systems that override, ignore, or are incapable of interacting with human decision-making can and have failed systematically and at scale. In addition, many security technologies rely upon large-scale data exfiltration to create their models; and are thus in conflict with human risk assessments with respect to privacy. 

In this seminar, Dr Camp will discuss an approach that combines human and machine intelligence by leveraging both local, private and global data.  Human capabilities, biases and decision-makers can be systematically included in system design. Dr. Camp describes design principals, architecture, and specific results from the combination of machine intelligence and human behavior. She presents this with empirical findings with respect to warning efficacy and provides proof of concept examples of human-centered machine learning for security that sets different thresholds for sensitivity for different devices and data types in the domains of phishing, routing, and IoT management.  

Contact [email protected] for event link. 

Event Location