1. Fuzzy Commitment Ari Juels RSA Laboratories ajuels@rsasecurity.com DIMACS Workshop on Cryptography: Theory Meets Practice 15 October 2004

2. Part I: Data secrecy in biometric authentication systems

3. The Classical View of Biometric Authentication Is it Woody? Yes, it’s Woody!

4. The Classical View of Biometric Authentication Is it Woody? Yes, it’s Woody! Woody Allen = ?

5. The Classical View of Biometric Authentication Woody Allen = ? Hello, Mr. Woody Allen

6. In these scenarios, biometric data need not be kept secret Spoofing is difficult with human oversight Indeed, your face is public anyway (Assuming, of course, that passport is not a forgery) But what happens when…

7. A human-guided process Woody Allen = ?

8. Becomes automated? Woody Allen = ?

9. Secrecy of biometric data is now more important to security Reason 1: Automation will mean relaxation of human oversight –More opportunity for spoofing –Spoofing iris / face readers with printed images, “gummy” fingers, etc. Schiphol airport: Iris scanning

10. Secrecy of biometric data is now more important to security Reason 2: Spillover into remote / home authentication! Woody Allen Woody’s PC Server

11. And revocation is hard! First password Second password

12. Yet passports will transmit biometrics via RFID to any standard reader… Woody Allen Clandestine scanning 10cm range under legal conditions How much with a rogue reader? One meter? How much from eavesdropping on legitimate reader? Optical keys / Faraday cages? ICAO (International Civil Aviation Organization) standard – imminent adoption through DHS effort

13. But isn’t my face public anyway? Copying a biometric is somewhat like copying a painting… Facial images require special conditions for matching to work. In U.K., you’re not allowed to smile in passport photos any longer! Best for forger to have target image, i.e., one in passport serving as basis for authentication Iris and fingerprint are harder to capture than face Suppose you want to copy a painting… snapshot professional photo

14. Part II: Towards secrecy in biometric authentication systems

15. password Cryptographic tools for password secrecy

16. password Cryptographic tools for password secrecy h (password, salt) E password [key] Password-based key agreement

17. Cryptographic tools for biometric secrecy h (, salt) E [key] Finger-based key agreement? ?

18. Problem: Biometrics are variable, i.e., error-prone… Differing angles of presentation Differing amounts of pressure Chapped skin  and standard crypto does not tolerate errors! Woody Allen !

19. We want “fuzzy” cryptography Error-tolerant crypto primitives –E.g., E k [m] D k’ [ ] = m if k ≈ k’ Body of “fuzzy” crypto literature: –Davida, Frankel, & Matt ’98 –“Biometric encryption” (breakable) –Juels & Wattenberg ’99 (“fuzzy commitment”) Application of FJ ‘01 to “life questions” now in RSA product… –Monrose, Reiter, & Wetze l ’99 + follow-on –Juels & Sudan ’01 –Dodis, Rezyin, & Smith ’04 –Boyen in ten minutes… But no rigorous application to real biometrics yet!

20. Why everybody has nice eyes An iriscode has an estimated 250 bits of entropy! –Contrast 1/10,000 false acceptance for fingerprints… –Most people have two eyes! Hamming distance is the metric for iriscode similarity –E. g., fuzzy commitment applies directly… iris iriscode

21. Why it’s not so easy… An iriscode can be as long as 4096 bits –Where are those 250 bits of entropy hidden? –Bits are not independent… Signal processing data folded into iriscode Eyelids, eyelashes, and reflections can occlude much of iris We could get only 37 pairs of eyes for experiments…

22. A first attempt Tricks: 1.Use staggered samples: yields up to 75 independent bits 2.Use multiple scans to reduce error rate 3.Play some ad-hoc tricks with signal-processing data Result: Able to extract a 60-bit or so key from a pair of irises, but how much were methods fitted to data?

23. Conclusion Ongoing work (joint with Mike Szydlo & Brent Waters) –Trying to understand iriscode distribution –Need programming help! Other groups trying to apply fuzzy crypto to fingerprints Natural place where theory (crypto) meets practice (the human being) –… and error-prone devices too, e.g., POWFs, PUFs… With biometrics on the march, imminent surge of interest in these techniques?