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Security with Noisy Data

Security with Noisy Data. Boris Škorić TU Eindhoven Ei/Ψ anniversary, 24 April 2009. OUTLINE Private biometrics Physical Unclonable Functions (PUFs) PUFs for anti-counterfeiting PUFs for secure key storage Fuzzy extractors General remarks. Private biometrics: intro. What's so private?

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Security with Noisy Data

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  1. Security with Noisy Data Boris Škorić TU Eindhoven Ei/Ψ anniversary, 24 April 2009

  2. OUTLINE • Private biometrics • Physical Unclonable Functions (PUFs) • PUFs for anti-counterfeiting • PUFs for secure key storage • Fuzzy extractors • General remarks

  3. Private biometrics: intro • What's so private? • fingerprints everywhere • easily photographed • no secrecy! • Biometrics database • access control • identification • How to abuse the database? • impersonation • identity theft • cross-db linking • detectable pathologies • ... yet undiscovered attacks • Insider attacks • db encryption not enough!

  4. Private biometrics: noisy biometrics • How to preserve privacy? • Don't store biometric itself • Store a one-way hash(like UNIX password file) • Attacker has to invert hash • Problem: noise • Measurement never the same twice • Any bit flip ⇒ hash totally changed • Need error correction • Redundancy data may leak! 00101101011110111001... one-wayfunction

  5. Private biometrics: secure error correction [Dodis et al., 2003] Recover SecureSketch Gen hash compare compare • "Fuzzy Extractor" • Uniform string: • Efficient storage • Quick db search • Efficient processing HelperData Reproduce Gen "extractedstring"

  6. OUTLINE • Private biometrics • Physical Unclonable Functions (PUFs) • PUFs for anti-counterfeiting • PUFs for secure key storage • Fuzzy extractors • General remarks

  7. Anti-counterfeiting: introduction The counterfeiting problem • Short history of paper money • 800 AD: China, first bills • 1450 AD: China abolishes paper money • 1601 AD: introduction in Sweden Frightening numbers: 10% of all medication 10% aircraft spare parts

  8. Anti-counterfeiting: think big

  9. Anti-counterfeiting, more voodoo than science Lots of obscurity [Source: Kirovski 2007]

  10. Anti-counterfeiting: a new approach • Traditional approach: • add authenticity mark to product • hard to forge • all marks are identical Er, ... WTF? • Alternative: [Bauder, Simmons < 1991] • unique marks • uncontrollable process • even manufacturer cannot clone • digitally signed • two-step verification • check sig., then check mark • forgery ← cloning / fake signature • allows "open" approach - product info - expiry date - mark details Digital signatureby Authority XYZ

  11. Anti-counterfeiting: PUFs • Physical Unclonable Function (PUF)[Pappu et al. 2001] • physical object • unpredictable challenge-response behaviour • hard to scrutinize without damaging • hard to model mathematically • hard ($) to clone physically, even for manufacturer Use PUF as anti-counterfeiting mark

  12. Anti-counterfeiting: PUF types Examples of anti-counterfeiting PUFs Kirovski et al. 2006 Microsoft research Škorić et al. 2008Philips research Pappu et al. 2001 Buchanan et al. 2005 MIT, Ingenia,Philips research

  13. Anti-counterfeiting: analogy with biometrics • Simplest case: • mark is not secret • use "distance" between measurements • no error correction • Without added mark: • mark is part of product • mark not really secret • but ... preserve "privacy" of product • noisy measurements Just like biometrics. Use fuzzy extractor!

  14. OUTLINE • Private biometrics • Physical Unclonable Functions (PUFs) • PUFs for anti-counterfeiting • PUFs for secure key storage • Fuzzy extractors • General remarks

  15. Secure key storage: intro • Problem: • Many devices need secret keys • authentication • encryption / decryption • signing • Digital key storage • 0/1 often distinguishable • invasive attacks • Alternative approach: Derive key from PUF • more opaque than digital memory • extract key when needed, then wipe from RAM • invasive attack ⇒ key destroyed

  16. Secure key storage: PUFs "Physically Obscured Key" (POK)[Gassend et al. 2003] • Physical Unclonable Function (PUF) • physical object • unpredictable challenge-response behaviour • hard to scrutinize without damaging • hard to model mathematically • hard ($) to clone physically, even for manufacturer PUF Sensor EEPROM Integrated - Helper data - EK[Device secrets] reproduce K Crypto processor

  17. Secure key storage: PUF types TiO2 TiN Silicon PUF[Gassend et al. 2002] Coating PUF [Posch 1998; Tuyls et al. 2006] Integrated optical PUF [Ophey et al. 2006] S-RAM PUF [Guajardo et al., Su et al. 2007] FPGA "butterfly" [Kumar et al. 2008]

  18. OUTLINE • Private biometrics • Physical Unclonable Functions (PUFs) • PUFs for anti-counterfeiting • PUFs for secure key storage • Fuzzy extractors • General remarks

  19. Fuzzy Extractors: intro • Required for e.g. • privacy preserving biometrics • anti-counterfeiting with "product privacy" • PUF-based key storage Dodis et al. 2003 Juels+Wattenberg 1999Linnartz+Tuyls 2003 • Properties • Secrecy and uniformity: Δ(WS; WU) ≤ ε. • "S given W is almost uniform" • Correctness: If X' sufficiently close to X, then S'=S. • Robustness [Boyen et al. 2005]:Detection of active attack against W noisy

  20. Fuzzy Extractors: high-level look at helper data Enrolment phase Gen(X) = {S, W} X W S X: measurementW: helper dataS: region index (extracted secret) X sufficiently "smooth" ⇒ W reveals little or nothing about S

  21. Fuzzy Extractors: high-level look at helper data Reproduction phase Rep(X',W) = S X' W S

  22. Fuzzy Extractors: necessity of helper data • Enrolments happen after fixing grid • Some X inevitably on boundary • noise can go either way • Helper data removes the ambiguity You need helper data. You really do.

  23. Fuzzy Extractors: active attacks • Active Attack: Modify W • accept wrong X' • accept key S' ≠ S • Defense: • TTP's signature on W. • But ... what if there's no PKI?Use secret S itself to authenticate W ! • hash(W||S). [Boyen 2005] • random oracle assumption • Sacrifice part of S as authentication key. • S = S1 || S2. • MAC(S1, W) (sort of) [Dodis et al. 2006] • information-theoretic security if X has sufficient entropy rate

  24. Fuzzy Extractors & PUFs: variety of disciplines information theory physics FUZZYEXTRACTIONFROM PUF crypto error-correcting codes security engineering

  25. OUTLINE • Private biometrics • Physical Unclonable Functions (PUFs) • PUFs for anti-counterfeiting • PUFs for secure key storage • Fuzzy extractors • General remarks

  26. General remarks: PUF proliferation optical PUF coating PUF Silicon PUF optical fiber PUF RF COA LC-PUF S-RAM PUF Arbiter PUF fluorescent PUF Delay PUF Butterfly PUF diode breakdown PUF reconfigurable PUF acoustic PUF controlled PUF phosphor PUF ...

  27. General remarks: PUF family tree MvD

  28. General remarks: after years of preaching the PUF gospel ...

  29. General remarks: ¥€££$ Making money from security with noisy data Philips spin-off Philips spin-off MIT spin-off Imperial College Londonspin-off

  30. Summary • Noisy sources of key material • privacy preserving storage of biometric data • anti-counterfeiting • secure key storage with PUFs • Fuzzy extractors • extract key from noisy source • reproducibility • secrecy of output • resilience against attacks on helper data • Subject becoming more popular • Not just theory, also $$$

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