@inproceedings{1650,
abstract = {We consider the task of deriving a key with high HILL entropy (i.e., being computationally indistinguishable from a key with high min-entropy) from an unpredictable source.
Previous to this work, the only known way to transform unpredictability into a key that was ϵ indistinguishable from having min-entropy was via pseudorandomness, for example by Goldreich-Levin (GL) hardcore bits. This approach has the inherent limitation that from a source with k bits of unpredictability entropy one can derive a key of length (and thus HILL entropy) at most k−2log(1/ϵ) bits. In many settings, e.g. when dealing with biometric data, such a 2log(1/ϵ) bit entropy loss in not an option. Our main technical contribution is a theorem that states that in the high entropy regime, unpredictability implies HILL entropy. Concretely, any variable K with |K|−d bits of unpredictability entropy has the same amount of so called metric entropy (against real-valued, deterministic distinguishers), which is known to imply the same amount of HILL entropy. The loss in circuit size in this argument is exponential in the entropy gap d, and thus this result only applies for small d (i.e., where the size of distinguishers considered is exponential in d).
To overcome the above restriction, we investigate if it’s possible to first “condense” unpredictability entropy and make the entropy gap small. We show that any source with k bits of unpredictability can be condensed into a source of length k with k−3 bits of unpredictability entropy. Our condenser simply “abuses" the GL construction and derives a k bit key from a source with k bits of unpredicatibily. The original GL theorem implies nothing when extracting that many bits, but we show that in this regime, GL still behaves like a “condenser" for unpredictability. This result comes with two caveats (1) the loss in circuit size is exponential in k and (2) we require that the source we start with has no HILL entropy (equivalently, one can efficiently check if a guess is correct). We leave it as an intriguing open problem to overcome these restrictions or to prove they’re inherent.},
author = {Skórski, Maciej and Golovnev, Alexander and Pietrzak, Krzysztof Z},
location = {Kyoto, Japan},
pages = {1046 -- 1057},
publisher = {Springer},
title = {{Condensed unpredictability }},
doi = {10.1007/978-3-662-47672-7_85},
volume = {9134},
year = {2015},
}
@inproceedings{1651,
abstract = {Cryptographic e-cash allows off-line electronic transactions between a bank, users and merchants in a secure and anonymous fashion. A plethora of e-cash constructions has been proposed in the literature; however, these traditional e-cash schemes only allow coins to be transferred once between users and merchants. Ideally, we would like users to be able to transfer coins between each other multiple times before deposit, as happens with physical cash. “Transferable” e-cash schemes are the solution to this problem. Unfortunately, the currently proposed schemes are either completely impractical or do not achieve the desirable anonymity properties without compromises, such as assuming the existence of a trusted “judge” who can trace all coins and users in the system. This paper presents the first efficient and fully anonymous transferable e-cash scheme without any trusted third parties. We start by revising the security and anonymity properties of transferable e-cash to capture issues that were previously overlooked. For our construction we use the recently proposed malleable signatures by Chase et al. to allow the secure and anonymous transfer of coins, combined with a new efficient double-spending detection mechanism. Finally, we discuss an instantiation of our construction.},
author = {Baldimtsi, Foteini and Chase, Melissa and Fuchsbauer, Georg and Kohlweiss, Markulf},
location = {Gaithersburg, MD, USA},
pages = {101 -- 124},
publisher = {Springer},
title = {{Anonymous transferable e-cash}},
doi = {10.1007/978-3-662-46447-2_5},
volume = {9020},
year = {2015},
}
@inproceedings{1652,
abstract = {We develop new theoretical tools for proving lower-bounds on the (amortized) complexity of certain functions in models of parallel computation. We apply the tools to construct a class of functions with high amortized memory complexity in the parallel Random Oracle Model (pROM); a variant of the standard ROM allowing for batches of simultaneous queries. In particular we obtain a new, more robust, type of Memory-Hard Functions (MHF); a security primitive which has recently been gaining acceptance in practice as an effective means of countering brute-force attacks on security relevant functions. Along the way we also demonstrate an important shortcoming of previous definitions of MHFs and give a new definition addressing the problem. The tools we develop represent an adaptation of the powerful pebbling paradigm (initially introduced by Hewitt and Paterson [HP70] and Cook [Coo73]) to a simple and intuitive parallel setting. We define a simple pebbling game Gp over graphs which aims to abstract parallel computation in an intuitive way. As a conceptual contribution we define a measure of pebbling complexity for graphs called cumulative complexity (CC) and show how it overcomes a crucial shortcoming (in the parallel setting) exhibited by more traditional complexity measures used in the past. As a main technical contribution we give an explicit construction of a constant in-degree family of graphs whose CC in Gp approaches maximality to within a polylogarithmic factor for any graph of equal size (analogous to the graphs of Tarjan et. al. [PTC76, LT82] for sequential pebbling games). Finally, for a given graph G and related function fG, we derive a lower-bound on the amortized memory complexity of fG in the pROM in terms of the CC of G in the game Gp.},
author = {Alwen, Joel F and Serbinenko, Vladimir},
booktitle = {Proceedings of the 47th annual ACM symposium on Theory of computing},
location = {Portland, OR, United States},
pages = {595 -- 603},
publisher = {ACM},
title = {{High parallel complexity graphs and memory-hard functions}},
doi = {10.1145/2746539.2746622},
year = {2015},
}
@inproceedings{1654,
abstract = {HMAC and its variant NMAC are the most popular approaches to deriving a MAC (and more generally, a PRF) from a cryptographic hash function. Despite nearly two decades of research, their exact security still remains far from understood in many different contexts. Indeed, recent works have re-surfaced interest for {\em generic} attacks, i.e., attacks that treat the compression function of the underlying hash function as a black box.
Generic security can be proved in a model where the underlying compression function is modeled as a random function -- yet, to date, the question of proving tight, non-trivial bounds on the generic security of HMAC/NMAC even as a PRF remains a challenging open question.
In this paper, we ask the question of whether a small modification to HMAC and NMAC can allow us to exactly characterize the security of the resulting constructions, while only incurring little penalty with respect to efficiency. To this end, we present simple variants of NMAC and HMAC, for which we prove tight bounds on the generic PRF security, expressed in terms of numbers of construction and compression function queries necessary to break the construction. All of our constructions are obtained via a (near) {\em black-box} modification of NMAC and HMAC, which can be interpreted as an initial step of key-dependent message pre-processing.
While our focus is on PRF security, a further attractive feature of our new constructions is that they clearly defeat all recent generic attacks against properties such as state recovery and universal forgery. These exploit properties of the so-called ``functional graph'' which are not directly accessible in our new constructions. },
author = {Gazi, Peter and Pietrzak, Krzysztof Z and Tessaro, Stefano},
location = {Auckland, New Zealand},
pages = {85 -- 109},
publisher = {Springer},
title = {{Generic security of NMAC and HMAC with input whitening}},
doi = {10.1007/978-3-662-48800-3_4},
volume = {9453},
year = {2015},
}
@article{1655,
abstract = {Quantifying behaviors of robots which were generated autonomously from task-independent objective functions is an important prerequisite for objective comparisons of algorithms and movements of animals. The temporal sequence of such a behavior can be considered as a time series and hence complexity measures developed for time series are natural candidates for its quantification. The predictive information and the excess entropy are such complexity measures. They measure the amount of information the past contains about the future and thus quantify the nonrandom structure in the temporal sequence. However, when using these measures for systems with continuous states one has to deal with the fact that their values will depend on the resolution with which the systems states are observed. For deterministic systems both measures will diverge with increasing resolution. We therefore propose a new decomposition of the excess entropy in resolution dependent and resolution independent parts and discuss how they depend on the dimensionality of the dynamics, correlations and the noise level. For the practical estimation we propose to use estimates based on the correlation integral instead of the direct estimation of the mutual information based on next neighbor statistics because the latter allows less control of the scale dependencies. Using our algorithm we are able to show how autonomous learning generates behavior of increasing complexity with increasing learning duration.},
author = {Martius, Georg S and Olbrich, Eckehard},
journal = {Entropy},
number = {10},
pages = {7266 -- 7297},
publisher = {Multidisciplinary Digital Publishing Institute},
title = {{Quantifying emergent behavior of autonomous robots}},
doi = {10.3390/e17107266},
volume = {17},
year = {2015},
}
@inproceedings{1656,
abstract = {Recently there has been a significant effort to handle quantitative properties in formal verification and synthesis. While weighted automata over finite and infinite words provide a natural and flexible framework to express quantitative properties, perhaps surprisingly, some basic system properties such as average response time cannot be expressed using weighted automata, nor in any other know decidable formalism. In this work, we introduce nested weighted automata as a natural extension of weighted automata which makes it possible to express important quantitative properties such as average response time. In nested weighted automata, a master automaton spins off and collects results from weighted slave automata, each of which computes a quantity along a finite portion of an infinite word. Nested weighted automata can be viewed as the quantitative analogue of monitor automata, which are used in run-time verification. We establish an almost complete decidability picture for the basic decision problems about nested weighted automata, and illustrate their applicability in several domains. In particular, nested weighted automata can be used to decide average response time properties.},
author = {Chatterjee, Krishnendu and Henzinger, Thomas A and Otop, Jan},
booktitle = {Proceedings - Symposium on Logic in Computer Science},
location = {Kyoto, Japan},
publisher = {IEEE},
title = {{Nested weighted automata}},
doi = {10.1109/LICS.2015.72},
volume = {2015-July},
year = {2015},
}
@inproceedings{1657,
abstract = {We consider Markov decision processes (MDPs) with multiple limit-average (or mean-payoff) objectives. There exist two different views: (i) ~the expectation semantics, where the goal is to optimize the expected mean-payoff objective, and (ii) ~the satisfaction semantics, where the goal is to maximize the probability of runs such that the mean-payoff value stays above a given vector. We consider optimization with respect to both objectives at once, thus unifying the existing semantics. Precisely, the goal is to optimize the expectation while ensuring the satisfaction constraint. Our problem captures the notion of optimization with respect to strategies that are risk-averse (i.e., Ensure certain probabilistic guarantee). Our main results are as follows: First, we present algorithms for the decision problems, which are always polynomial in the size of the MDP. We also show that an approximation of the Pareto curve can be computed in time polynomial in the size of the MDP, and the approximation factor, but exponential in the number of dimensions. Second, we present a complete characterization of the strategy complexity (in terms of memory bounds and randomization) required to solve our problem. },
author = {Chatterjee, Krishnendu and Komárková, Zuzana and Kretinsky, Jan},
location = {Kyoto, Japan},
pages = {244 -- 256},
publisher = {IEEE},
title = {{Unifying two views on multiple mean-payoff objectives in Markov decision processes}},
doi = {10.1109/LICS.2015.32},
year = {2015},
}
@inproceedings{1658,
abstract = {Continuous-time Markov chain (CTMC) models have become a central tool for understanding the dynamics of complex reaction networks and the importance of stochasticity in the underlying biochemical processes. When such models are employed to answer questions in applications, in order to ensure that the model provides a sufficiently accurate representation of the real system, it is of vital importance that the model parameters are inferred from real measured data. This, however, is often a formidable task and all of the existing methods fail in one case or the other, usually because the underlying CTMC model is high-dimensional and computationally difficult to analyze. The parameter inference methods that tend to scale best in the dimension of the CTMC are based on so-called moment closure approximations. However, there exists a large number of different moment closure approximations and it is typically hard to say a priori which of the approximations is the most suitable for the inference procedure. Here, we propose a moment-based parameter inference method that automatically chooses the most appropriate moment closure method. Accordingly, contrary to existing methods, the user is not required to be experienced in moment closure techniques. In addition to that, our method adaptively changes the approximation during the parameter inference to ensure that always the best approximation is used, even in cases where different approximations are best in different regions of the parameter space.},
author = {Bogomolov, Sergiy and Henzinger, Thomas A and Podelski, Andreas and Ruess, Jakob and Schilling, Christian},
location = {Nantes, France},
pages = {77 -- 89},
publisher = {Springer},
title = {{Adaptive moment closure for parameter inference of biochemical reaction networks}},
doi = {10.1007/978-3-319-23401-4_8},
volume = {9308},
year = {2015},
}
@inproceedings{1659,
abstract = {The target discounted-sum problem is the following: Given a rational discount factor 0 < λ < 1 and three rational values a, b, and t, does there exist a finite or an infinite sequence w ε(a, b)∗ or w ε(a, b)w, such that Σ|w| i=0 w(i)λi equals t? The problem turns out to relate to many fields of mathematics and computer science, and its decidability question is surprisingly hard to solve. We solve the finite version of the problem, and show the hardness of the infinite version, linking it to various areas and open problems in mathematics and computer science: β-expansions, discounted-sum automata, piecewise affine maps, and generalizations of the Cantor set. We provide some partial results to the infinite version, among which are solutions to its restriction to eventually-periodic sequences and to the cases that λ λ 1/2 or λ = 1/n, for every n ε N. We use our results for solving some open problems on discounted-sum automata, among which are the exact-value problem for nondeterministic automata over finite words and the universality and inclusion problems for functional automata.},
author = {Boker, Udi and Henzinger, Thomas A and Otop, Jan},
booktitle = {LICS},
issn = {1043-6871 },
location = {Kyoto, Japan},
pages = {750 -- 761},
publisher = {IEEE},
title = {{The target discounted-sum problem}},
doi = {10.1109/LICS.2015.74},
year = {2015},
}
@inproceedings{1660,
abstract = {We study the pattern frequency vector for runs in probabilistic Vector Addition Systems with States (pVASS). Intuitively, each configuration of a given pVASS is assigned one of finitely many patterns, and every run can thus be seen as an infinite sequence of these patterns. The pattern frequency vector assigns to each run the limit of pattern frequencies computed for longer and longer prefixes of the run. If the limit does not exist, then the vector is undefined. We show that for one-counter pVASS, the pattern frequency vector is defined and takes one of finitely many values for almost all runs. Further, these values and their associated probabilities can be approximated up to an arbitrarily small relative error in polynomial time. For stable two-counter pVASS, we show the same result, but we do not provide any upper complexity bound. As a byproduct of our study, we discover counterexamples falsifying some classical results about stochastic Petri nets published in the 80s.},
author = {Brázdil, Tomáš and Kiefer, Stefan and Kučera, Antonín and Novotny, Petr},
location = {Kyoto, Japan},
pages = {44 -- 55},
publisher = {IEEE},
title = {{Long-run average behaviour of probabilistic vector addition systems}},
doi = {10.1109/LICS.2015.15},
year = {2015},
}