AISL researcher Chris Clifton of Purdue University will give a keynote talk at the The First ACM Workshop on AISec. This workshop is focused on bringing the AI and security research communities together to explore how AI tools and techniques can be applied to problems in information security.
Chris’ talk is titled Opportunities for Private and Secure Machine Learning and has the following abstract.
The privacy-preserving data mining literature provides numerous solutions to machine learning on sensitive data, while protecting the data from disclosure. Unfortunately, privacy has yet to provide the economic incentives for commercial development of this technology.
This talk will survey this work (and open challenges) in light of problems that may have greater incentives for development: collaborative machine learning by parties that do not fully trust each other. Opportunities include job brokerage (assigning jobs in ways that most efficiently utilize resources of competing companies), supply chain optimization, inter-agency data sharing, etc. Techniques similar to those in privacy-preserving data mining can enable such applications without the degree of information disclosure and trust currently required, providing a business model for development of the technology (and as a by-product, reducing the number of trusted systems that need to be secured.)