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Cornell’s Trustworthiness Education Initiative By Fred B. Schneider TRUST TRUST:Team for Research in Ubiquitous Secure Technologies September 13th 2004.

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Presentation on theme: "Cornell’s Trustworthiness Education Initiative By Fred B. Schneider TRUST TRUST:Team for Research in Ubiquitous Secure Technologies September 13th 2004."— Presentation transcript:

1 Cornell’s Trustworthiness Education Initiative By Fred B. Schneider TRUST TRUST:Team for Research in Ubiquitous Secure Technologies September 13th 2004 NSF STC Review

2 Teaching Trustworthiness Traditional approach: –Specialized courses (upper class, MEng) Security, cryptography, fault-tolerant computing, … –Topics in Discipline-specific courses Op Sys, Databases, Computer Networks … specialists are well served by this approach. CU faculty [spring/summer 2004]: All computer scientists need to be sensitive to trustworthiness issues / solutions. –No room to add more required courses. –Different disciplines need to see different content. … must educate the whole CS / IS population TRUST September 13th 2004 NSF STC Review 2

3 Cornell’s Trustworthiness-Education Initiative Don’t add new courses---add relevant content to existing courses. –1-2 lectures worth of content per course –New content need not supplant existing topics: Well-chosen examples / applications could serve a dual role. Side effect: Mainstream trustworthiness courses can assume more of students and add depth/breadth. TRUST September 13th 2004 NSF STC Review 3

4 Progress on the Agenda Enthusiastic faculty response: –Each faculty member is reviewing course contents and proposing topics to cover. –Ultimately: We will produce notes + other teaching aids. (These will be debugged by TRUST members.) Students are excited about the new thread. Corporate partners are happy too. –Microsoft, NYC Financial Industry, … TRUST September 13th 2004 NSF STC Review 4

5 New Trustworthiness Content Examples Obvious examples: –quorums to discrete math course –program analysis tools to programming course –cryptography to computational complexity Non-obvious examples: –digital watermarking to graphics course –credit-card fraud detection to machine learning –data structures for big-number computation –canaries to compiler course TRUST September 13th 2004 NSF STC Review 5


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