Photograph of me lecturing at the blackboard (credit: R. Benjamin Shapiro, 2002).
Activities and upcoming events that I'm involved with:
IEEE Security & Privacy (ongoing)
NSA SoS Best Paper Competition (annually, deadlines in April)
IEEE Cybersecurity Award for Practice (annually)
IEEE S&P (Oakland) 2023 (May 22–26, 2023)
CSET 2023 (August 2023)
NSPW 2023 (September/October 2023)
2023 NSF Cybersecurity Summit (Oct. 2023)
I3P Data SanitizationThis seed project looked at defining means for understanding what data can be sanitized, and how. Traditionally, techniques for sanitizing or anonymizing data have included masking, adding noise, or enforcing regularity. They typically also assume a "closed world." However, these techniques often either make data unusable for research or operational purposes or fail to completely sanitize the data. Thus, our data sanitization work builds on past techniques by also using an "open world" assumption. We also ask, what are the relationships between data fields that would need to be made (e.g., by making associations from external datasets) in order to reveal certain information? Alternatively, what associations need to be protected in order to conceal certain information? Finally, given policy constraints by the different stakeholders (e.g., the person who the data that describes, operational personnel, and research personnel), can dataset X be sanitized in a way that satisfies the policies of all of those people, or would certain compromises to one or more policies need to be made? If so, what?
Sponsor: Institute for Information Infrastructure Protection (I3P)
Publications resulting from this project:
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Last modified: Sunday, 30-Sep-2018 10:15:30 PDT