TY - CONF
AB - Traditionally, symmetric-key message authentication codes (MACs) are easily built from pseudorandom functions (PRFs). In this work we propose a wide variety of other approaches to building efficient MACs, without going through a PRF first. In particular, unlike deterministic PRF-based MACs, where each message has a unique valid tag, we give a number of probabilistic MAC constructions from various other primitives/assumptions. Our main results are summarized as follows: We show several new probabilistic MAC constructions from a variety of general assumptions, including CCA-secure encryption, Hash Proof Systems and key-homomorphic weak PRFs. By instantiating these frameworks under concrete number theoretic assumptions, we get several schemes which are more efficient than just using a state-of-the-art PRF instantiation under the corresponding assumption. For probabilistic MACs, unlike deterministic ones, unforgeability against a chosen message attack (uf-cma ) alone does not imply security if the adversary can additionally make verification queries (uf-cmva ). We give an efficient generic transformation from any uf-cma secure MAC which is "message-hiding" into a uf-cmva secure MAC. This resolves the main open problem of Kiltz et al. from Eurocrypt'11; By using our transformation on their constructions, we get the first efficient MACs from the LPN assumption. While all our new MAC constructions immediately give efficient actively secure, two-round symmetric-key identification schemes, we also show a very simple, three-round actively secure identification protocol from any weak PRF. In particular, the resulting protocol is much more efficient than the trivial approach of building a regular PRF from a weak PRF. © 2012 International Association for Cryptologic Research.
AU - Dodis, Yevgeniy
AU - Pietrzak, Krzysztof Z
AU - Kiltz, Eike
AU - Wichs, Daniel
ID - 3282
TI - Message authentication, revisited
VL - 7237
ER -
TY - CONF
AB - We consider the problem of inference in a graphical model with binary variables. While in theory it is arguably preferable to compute marginal probabilities, in practice researchers often use MAP inference due to the availability of efficient discrete optimization algorithms. We bridge the gap between the two approaches by introducing the Discrete Marginals technique in which approximate marginals are obtained by minimizing an objective function with unary and pairwise terms over a discretized domain. This allows the use of techniques originally developed for MAP-MRF inference and learning. We explore two ways to set up the objective function - by discretizing the Bethe free energy and by learning it from training data. Experimental results show that for certain types of graphs a learned function can outperform the Bethe approximation. We also establish a link between the Bethe free energy and submodular functions.
AU - Korc, Filip
AU - Kolmogorov, Vladimir
AU - Lampert, Christoph
ID - 3124
TI - Approximating marginals using discrete energy minimization
ER -
TY - GEN
AB - We consider the problem of inference in agraphical model with binary variables. While in theory it is arguably preferable to compute marginal probabilities, in practice researchers often use MAP inference due to the availability of efficient discrete optimization algorithms. We bridge the gap between the two approaches by introducing the Discrete Marginals technique in which approximate marginals are obtained by minimizing an objective function with unary and pair-wise terms over a discretized domain. This allows the use of techniques originally devel-oped for MAP-MRF inference and learning. We explore two ways to set up the objective function - by discretizing the Bethe free energy and by learning it from training data. Experimental results show that for certain types of graphs a learned function can out-perform the Bethe approximation. We also establish a link between the Bethe free energy and submodular functions.
AU - Korc, Filip
AU - Kolmogorov, Vladimir
AU - Lampert, Christoph
ID - 5396
SN - 2664-1690
TI - Approximating marginals using discrete energy minimization
ER -
TY - CHAP
AU - Gupta, Ashutosh
ID - 5745
SN - 0302-9743
T2 - Automated Technology for Verification and Analysis
TI - Improved Single Pass Algorithms for Resolution Proof Reduction
VL - 7561
ER -
TY - JOUR
AB - Boolean notions of correctness are formalized by preorders on systems. Quantitative measures of correctness can be formalized by real-valued distance functions between systems, where the distance between implementation and specification provides a measure of "fit" or "desirability". We extend the simulation preorder to the quantitative setting by making each player of a simulation game pay a certain price for her choices. We use the resulting games with quantitative objectives to define three different simulation distances. The correctness distance measures how much the specification must be changed in order to be satisfied by the implementation. The coverage distance measures how much the implementation restricts the degrees of freedom offered by the specification. The robustness distance measures how much a system can deviate from the implementation description without violating the specification. We consider these distances for safety as well as liveness specifications. The distances can be computed in polynomial time for safety specifications, and for liveness specifications given by weak fairness constraints. We show that the distance functions satisfy the triangle inequality, that the distance between two systems does not increase under parallel composition with a third system, and that the distance between two systems can be bounded from above and below by distances between abstractions of the two systems. These properties suggest that our simulation distances provide an appropriate basis for a quantitative theory of discrete systems. We also demonstrate how the robustness distance can be used to measure how many transmission errors are tolerated by error correcting codes.
AU - Cerny, Pavol
AU - Henzinger, Thomas A
AU - Radhakrishna, Arjun
ID - 3249
IS - 1
JF - Theoretical Computer Science
TI - Simulation distances
VL - 413
ER -