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Loud and Clear: Human Verifiable Authentication Based on Audio

This paper presents a novel method for establishing a secure communication channel between devices lacking prior association, specifically targeting man-in-the-middle attacks. Utilizing human-assisted authentication, our solution involves generating vocalizable representations from a hash of a public key, allowing users to verify and pair devices securely. We explore various use cases, highlight the importance of auditorily robust sentences for user verification, and detail our system’s implementation and performance. This approach supports multiple communication mediums and is designed for devices with limited computational resources.

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Loud and Clear: Human Verifiable Authentication Based on Audio

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  1. Michael Goodrich Michael Sirivianos John Solis Gene Tsudik Ersin Uzun Computer Science Department University of California, Irvine July 5, 2006 @ ICDCS, Lisbon Loud and Clear: Human Verifiable Authentication Based on Audio

  2. Problem Wallet phone Goal:Establish a secure channel between devices that lack prior association

  3. Problem Eve can launch man-in-the-middle attack Goal:Establish a secure channel between devices that lack prior association

  4. Challenges • Human-assisted authentication • Involve human user in the process • No pre-established shared secrets • No on-line or off-line authority • no common PKI, TTP, etc • Support for multiple communication media • e.g., Bluetooth, Infrared, 802.11, etc • Limited computational resources on portable devices

  5. Outline • Related work and our motivation • Our Solution • System overview • Sample use scenarios • Use types • Vocalizable representations • Unidirectional authentication • Implementation and performance • Conclusions

  6. Related work-Secondary Channels • Stajano et. al. [Security Protocols ‘99] • Use a physical link a secondary authentication channel • Not all devices have suitable interfaces • Balfanz et. al. [NDSS ‘02] • Uses an infrared link as secondary channel • Still susceptible to man-in-the-middle attack

  7. Related work–Human verifiable channels • Maher [US Patent, ‘95] • Users compare 4 hex digit truncated hash of the shared key. Not enough bits for security. • Cagalj et. al. and Laur et. al. • Commitment-based short authenticated string schemes • 20 bit verification code is sufficient for security • Do not address verification code representation

  8. Related work–Textual representations • Haller [S/KEY, RFC 1760] • Textual representation of cryptographic strings • Pass-phrases not auditorially robust nor syntactically-correct • Juola and Zimmermann [PGPfone, ICSLP ‘96] • Uses auditorially robust word list. Not syntactically- correct, hard for human users to parse it

  9. Related work-SiB • Human readable visual hashes • Cumbersome task • High error rate • McCune et al [Oakland ‘05] • Seeing is Believing • Uses camera phones and bar codes to create a visual secondary channel • The visual channel is not always plausible

  10. Motivation • Many personal devices not equipped with cameras • Cameras unsuitable for visually-impaired users • Bar-code scanning requires ample light and sufficient proximity between devices • Camera-equipped devices typically prohibited in high-security areas

  11. Outline • Related work and our motivation • Our Solution • System overview • Sample use scenarios • Use types • Vocalizable representations • Unidirectional authentication • Implementation and performance • Conclusions

  12. Loud and Clear • Audio channel for human-assisted authentication of un-associated devices • Derive a robust-sounding, syntactically-correct sentence from a hash of a public key • Vocalize the sentence • L&C couples vocalization and/or display of the public authentication object on two devices • Suitable for secure device pairing

  13. Personal Device Target Device Printer or FAX: speaker & small display Cell phone: speaker & small display Handheld/PDA: speaker & display Base Station: no speaker & no display Smart Watch: tiny speaker & tiny display Mutual authentication possibly required MP3 player: audio out & no display Sample use scenarios

  14. L&C use types TYPE 2: Hear audible sequence from target device, compare to text displayed by personal device TYPE 1: Hear and compare two two audible sequences, one from each device TYPE 3: Hear audible sequence from personal device, compare it to text displayed by target device TYPE 4: Compare text displayed on each device

  15. Device requirements Device requirements for various use types

  16. Vocalizable representations Represent the authentication object as a syntactically- correct, auditorially robust sentence • Generate a non-sensical, English-like sentence (MadLib) from the output of a one-way hash • S/KEY-based word generation. • Divide truncated hash into 10-bit sections • Use each 10-bit section as index into a catalogue • One catalogue for each part of speech, e.g., verb, noun etc • Number of 10-bit sections = number of words contributing entropy in the MadLib sentence

  17. Vocalizable representations Within a catalogue, no two words sound the same • Create auditorially robust word lists for each catalogue, based on PGPfone’s phonetic distance Second pre-image resistance • For ephemeral Diffie-Hellman key agreement • 5 S/KEY-generated words needed • For one-year-term Diffie-Hellman public keys • 8 S/KEY-generated words needed

  18. Vocalizable representations Within a catalogue, no two words sound the same • Create auditorially robust word lists for each catalogue, based on PGPfone’s phonetic distance Second pre-image resistance • For ephemeral Diffie-Hellman key agreement • 5 S/KEY-generated words needed • For one-year-term Diffie-Hellman public keys • 8 S/KEY-generated words needed CALLIE FLEXIBLY ownsFLUFFYBINTURONGs that ABUSE.

  19. Auditorially robust word lists Using PGPfone’s phonetic distance, create auditorially- robust word lists, unique for each catalogue Construct a large set C of candidate words. Select a random subset W of 2k words from C, where k is the number of hash bits we wish to have this type of word represent. Repeatedly find the phonetically closest pair (p, q) of words in W and replace q with a word from C - W whose distance to any word in W is more than distance(p, q), if such word exists.

  20. Auditorially robust word lists (2) Order W so that each pair of consecutive words in W are as distant as possible. Assign integer values to words in W, so that consecutive integers differ in exactly one bit but their respective code words are distant.

  21. Unidirectional authentication • Step 1: • PDA and fax send to each other their Diffie-Hellman public keys

  22. Unidirectional authentication • Step 2: • PDA and fax compute the MadLib for fax’s public key

  23. Unidirectional authentication • Step 3: • Alice instructs PDA and fax to speak the MadLibs out

  24. Unidirectional authentication • Step 3: • Alice instructs PDA and fax to speak the MadLib out

  25. Unidirectional authentication • Step 4: • Alice compares the MadLibs

  26. Unidirectional authentication • Step 5: • Alice instructs the devices to compute the secret key

  27. Implementation Programming System • Built on the highly portable Ewe Java VM Text-to-Speech Engine • Can utilize a variety of portable TTS engines • Prototype uses Digit for PC and Pocket PC, which uses the Elan Speech Engine • Porting Sun’s clean Java FreeTTS and JSAPI to Ewe

  28. Implementation Crypto API • Ported Bouncy-Castle lightweight crypto package to implement DH- and RSA-based key agreement Memory utilization • Digit and Ewe program reside on ~10800 KB

  29. Performance Processing times (in milliseconds) of L&C operations • PC: 1.7 GHZ/2MB, 512MB RAM • iPAQ: 206 MHZ, 32 MB RAM • 10 word MadLib, 7 of whichS/Key generated

  30. Performance Excluding initialization and shared secret computation: • ~12 secs for TYPE 1 unidirectional session • ~7 secs for TYPE 2 unidirectional session With a commitment-based SAS protocol: • Number of S/Key generated words can be reduced to only 2! • ~6 secs for TYPE 1 unidirectional session

  31. Conclusions • Loud-and-Clear (L&C)for human-assisted device authentication • Light burden for human user • Based on the audio channel • Uses a TTS engine to vocalize a robust-sounding, syntactically-correct word sequence derived from some authentication object • Discussed some anticipated use cases • Provided experimental results for a prototype • Formal and comprehensive usability studies in progress

  32. In case you wonder … Binturong • a.k.a Bearcat • Leaves in the forest canopy, of southeast Asia, Borneo Vietnam, Malaysia, Indonesia • Belongs in the Viverridae family • Endangered 

  33. Thank you Latest paper version available at: www.ics.uci.edu/~msirivia/publications/icdcs.pdf Questions?

  34. Cagalj et al, DH-SC

  35. Performance Timings (in ms) for L&C sessions

  36. Loud and Clear Loud and Clear (L&C) system. • Light burden for the human user • Uses spoken natural language for human-assisted authentication • Suitable for secure device pairing • e.g., key exchange or similar tasks

  37. Outline • Related work and our motivation • Our Solution • System overview • Sample use scenarios • Use types • Vocalizable representations • Unidirectional authentication • Implementation and performance • Conclusions

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