Secure Human Identification Protocols

By Nicholas Hopper, Manuel Blum. International Conference on the Theory and Application of Cryptology and Information Security, 2001

583 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/5f58810e60296dacb378dc24048f34acf6e9323a

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Citations in the corpus

Fast Cryptographic Primitives and Circular-Secure Encryption Based on Hard Learning Problems (2009, methodology): “Indeed, previous works relied on these properties to obtain cryptography with low complexity [32,4,35,6], and to derive desirable cryptographic features such as random self-reducibility (with respect to the choice of a) and pseudorandomness [10].”
Decoding Random Binary Linear Codes in 2n/20: How 1+1=0 Improves Information Set Decoding (2012, background): “Also closely related to random linear codes is the learning parity with noise (LPN) problem that is frequently used in cryptography [1, 13, 16].”
On agnostic boosting and parity learning (2008, background): “Learning parity with noise is a longstanding open problem in computational learning theory, which has attracted attention in recent years [BKW03, FGKP06, Reg05, HB01 , JW05, MR05, Lyu05, GRS00], for several reasons.”
SASI: A New Ultralightweight RFID Authentication Protocol Providing Strong Authentication and Strong Integrity (2007, background): “Ç”
Pseudorandom Functions and Lattices (2012, background): “As evidence, consider that recent constructions of weaker primitives such as symmetric authentication protocols [18, 19, 20], randomized weak PRFs [4], and message-authentication codes [34, 21] from noisy-learning problems are all inherently randomized functions, where security relies on introducing”
A Generalized Birthday Problem (2002, background): “Others in learning theory have since used similar ideas [37], and the hardness of this problem has even been proposed as the basis for a human-computer authentication scheme [17].”
Trapdoors for hard lattices and new cryptographic constructions (2008, background): “LWE can seen as a “unique decoding” problem on a certain family of random lattices, and is a generalization (to larger moduli) of the well-studied learning parity with noise problem, which is believed to be hard and has seen many applications in cryptography (e.g., [BKW03, HB01 , JW05, KS06]).”
Guessing human-chosen secrets (2012, background): “The most secure schemes, proposed by Hopper and Blum in 2001 [144], can provide cryptographic security by reduction to a known NPhard problem (learning parity in the presence of noise), but are very difficult to execute and require over three minutes even for skilled users.”
Authenticating Pervasive Devices with Human Protocols (2005, methodology): “However, this paper will focus primarily on the the human authentication protocols of Hopper and Blum [16, 17].”
New Algorithms for Learning in Presence of Errors (2011, background): “All evidence suggests that this recovery problem is hard, and this conjectured hardness is the basis of some cryptosystems [5, 7, 12].”