The Experts below are selected from a list of 9744 Experts worldwide ranked by ideXlab platform
Scott Yilek - One of the best experts on this subject based on the ideXlab platform.
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the mix and cut shuffle Small Domain encryption secure against n queries
International Cryptology Conference, 2013Co-Authors: Thomas Ristenpart, Scott YilekAbstract:We provide a new shuffling algorithm, called Mix-and-Cut, that provides a provably-secure block cipher even for adversaries that can observe the encryption of all N = 2 n Domain points. Such fully secure ciphers are useful for format-preserving encryption, where Small Domains (e.g., n = 30) are common and databases may well include examples of almost all ciphertexts. Mix-and-Cut derives from a general framework for building fully secure pseudorandom permutations (PRPs) from fully secure pseudorandom separators (PRSs). The latter is a new primitive that we treat for the first time. Our framework was inspired by, and uses ideas from, a particular cipher due to Granboulin and Pornin. To achieve full security for Mix-and-Cut using this framework, we give a simple proof that a PRP secure for (1 − e)N queries (recently achieved efficiently by Hoang, Morris, and Rogaway’s Swap-or-Not cipher) yields a PRS secure for N queries.
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CRYPTO (1) - The Mix-and-Cut Shuffle: Small-Domain Encryption Secure against N Queries
Advances in Cryptology – CRYPTO 2013, 2013Co-Authors: Thomas Ristenpart, Scott YilekAbstract:We provide a new shuffling algorithm, called Mix-and-Cut, that provides a provably-secure block cipher even for adversaries that can observe the encryption of all N = 2 n Domain points. Such fully secure ciphers are useful for format-preserving encryption, where Small Domains (e.g., n = 30) are common and databases may well include examples of almost all ciphertexts. Mix-and-Cut derives from a general framework for building fully secure pseudorandom permutations (PRPs) from fully secure pseudorandom separators (PRSs). The latter is a new primitive that we treat for the first time. Our framework was inspired by, and uses ideas from, a particular cipher due to Granboulin and Pornin. To achieve full security for Mix-and-Cut using this framework, we give a simple proof that a PRP secure for (1 − e)N queries (recently achieved efficiently by Hoang, Morris, and Rogaway’s Swap-or-Not cipher) yields a PRS secure for N queries.
Aarti Gupta - One of the best experts on this subject based on the ideXlab platform.
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SDSAT : Tight integration of Small Domain Encoding and Lazy approaches in a separation logic solver
Lecture Notes in Computer Science, 2020Co-Authors: Malay K. Ganai, Muralidhar Talupur, Aarti GuptaAbstract:Existing Separation Logic (a.ka Difference Logic, DL) solvers can be broadly classified as eager or lazy, each with its own merits and de-merits. We propose a novel Separation Logic Solver SDSAT that combines the strengths of both these approaches and provides a robust performance over a wide set of benchmarks. The solver SDSAT works in two phases: allocation and solve. In the allocation phase, it allocates non-uniform adequate ranges for variables appearing in separation predicates. This phase is similar to previous Small Domain encoding approaches, but uses a novel algorithm Nu-SMOD with 1-2 orders of magnitude improvement in performance and Smaller ranges for variables. Furthermore, the Separation Logic formula is not transformed into an equi-satisfiable Boolean formula in one step, but rather done lazily in the following phase. In the solve phase, SDSAT uses a lazy refinement approach to search for a satisfying model within the allocated ranges. Thus, any partially DL-theory consistent model can be discarded if it can not be satisfied within the allocated ranges. Note the crucial difference: in eager approaches, such a partially consistent model is not allowed in the first place, while in lazy approaches such a model is never discarded. Moreover, we dynamically refine the allocated ranges and search for a feasible solution within the updated ranges. This combined approach benefits from both the Smaller search space (as in eager approaches) and also from the theory-specific graph-based algorithms (characteristic of lazy approaches). Experimental results show that our method is robust and always better than or comparable to state-of-the art solvers.
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SDSAT: Tight Integration of Small Domain Encoding and Lazy Approaches in Solving Difference Logic
Journal on Satisfiability Boolean Modeling and Computation, 2007Co-Authors: Malay K. Ganai, Muralidhar Talupur, Aarti GuptaAbstract:Existing difference logic (DL) solvers can be broadly classified as eager or lazy, each with its own merits and de-merits. We propose a novel difference logic solver SDSAT that combines the strengths of both these approaches and provides a robust performance over a wide set of benchmarks. The solver SDSAT works in two phases: allocation and solve. In the allocation phase, it allocates non-uniform adequate ranges for variables appearing in difference predicates. This phase is similar to previous Small Domain encoding approaches, but uses a novel algorithm Nu-SMOD with 1-2 orders of magnitude improvement in performance and Smaller ranges for variables. Furthermore, the difference logic formula is not transformed into an equi-satisfiable Boolean formula in a single step, but rather done lazily in the following phase. In the solve phase, SDSAT uses a lazy refinement approach to search for a satisfying model within the allocated ranges. Thus, any partially DL-theory consistent model can be discarded if it cannot be satisfied within the allocated ranges. Note the crucial difference: in eager approaches, such a partially consistent model is not allowed in the first place, while in lazy approaches such a model is never discarded. Moreover, we dynamically refine the allocated ranges and search for a feasible solution within the updated ranges. This combined approach benefits from both the Smaller search space (as in eager approaches) and also from the theory-specific graph-based algorithms (characteristic of lazy approaches). Experimental results show that our method is robust and always better than or comparable to state of-the art solvers using similar eager or lazy techniques.
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TACAS - SDSAT : tight integration of Small Domain encoding and lazy approaches in a separation logic solver
Tools and Algorithms for the Construction and Analysis of Systems, 2006Co-Authors: Malay K. Ganai, Muralidhar Talupur, Aarti GuptaAbstract:Existing Separation Logic (a.k.a Difference Logic, DL) solvers can be broadly classified as eager or lazy, each with its own merits and de-merits. We propose a novel Separation Logic Solver SDSAT that combines the strengths of both these approaches and provides a robust performance over a wide set of benchmarks. The solver SDSAT works in two phases: allocation and solve. In the allocation phase, it allocates non-uniform adequate ranges for variables appearing in separation predicates. This phase is similar to previous Small Domain encoding approaches, but uses a novel algorithm Nu-SMOD with 1-2 orders of magnitude improvement in performance and Smaller ranges for variables. Furthermore, the Separation Logic formula is not transformed into an equi-satisfiable Boolean formula in one step, but rather done lazily in the following phase. In the solve phase, SDSAT uses a lazy refinement approach to search for a satisfying model within the allocated ranges. Thus, any partially DL-theory consistent model can be discarded if it can not be satisfied within the allocated ranges. Note the crucial difference: in eager approaches, such a partially consistent model is not allowed in the first place, while in lazy approaches such a model is never discarded. Moreover, we dynamically refine the allocated ranges and search for a feasible solution within the updated ranges. This combined approach benefits from both the Smaller search space (as in eager approaches) and also from the theory-specific graph-based algorithms (characteristic of lazy approaches). Experimental results show that our method is robust and always better than or comparable to state-of-the art solvers.
Thomas Ristenpart - One of the best experts on this subject based on the ideXlab platform.
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the mix and cut shuffle Small Domain encryption secure against n queries
International Cryptology Conference, 2013Co-Authors: Thomas Ristenpart, Scott YilekAbstract:We provide a new shuffling algorithm, called Mix-and-Cut, that provides a provably-secure block cipher even for adversaries that can observe the encryption of all N = 2 n Domain points. Such fully secure ciphers are useful for format-preserving encryption, where Small Domains (e.g., n = 30) are common and databases may well include examples of almost all ciphertexts. Mix-and-Cut derives from a general framework for building fully secure pseudorandom permutations (PRPs) from fully secure pseudorandom separators (PRSs). The latter is a new primitive that we treat for the first time. Our framework was inspired by, and uses ideas from, a particular cipher due to Granboulin and Pornin. To achieve full security for Mix-and-Cut using this framework, we give a simple proof that a PRP secure for (1 − e)N queries (recently achieved efficiently by Hoang, Morris, and Rogaway’s Swap-or-Not cipher) yields a PRS secure for N queries.
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CRYPTO (1) - The Mix-and-Cut Shuffle: Small-Domain Encryption Secure against N Queries
Advances in Cryptology – CRYPTO 2013, 2013Co-Authors: Thomas Ristenpart, Scott YilekAbstract:We provide a new shuffling algorithm, called Mix-and-Cut, that provides a provably-secure block cipher even for adversaries that can observe the encryption of all N = 2 n Domain points. Such fully secure ciphers are useful for format-preserving encryption, where Small Domains (e.g., n = 30) are common and databases may well include examples of almost all ciphertexts. Mix-and-Cut derives from a general framework for building fully secure pseudorandom permutations (PRPs) from fully secure pseudorandom separators (PRSs). The latter is a new primitive that we treat for the first time. Our framework was inspired by, and uses ideas from, a particular cipher due to Granboulin and Pornin. To achieve full security for Mix-and-Cut using this framework, we give a simple proof that a PRP secure for (1 − e)N queries (recently achieved efficiently by Hoang, Morris, and Rogaway’s Swap-or-Not cipher) yields a PRS secure for N queries.
Malay K. Ganai - One of the best experts on this subject based on the ideXlab platform.
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SDSAT : Tight integration of Small Domain Encoding and Lazy approaches in a separation logic solver
Lecture Notes in Computer Science, 2020Co-Authors: Malay K. Ganai, Muralidhar Talupur, Aarti GuptaAbstract:Existing Separation Logic (a.ka Difference Logic, DL) solvers can be broadly classified as eager or lazy, each with its own merits and de-merits. We propose a novel Separation Logic Solver SDSAT that combines the strengths of both these approaches and provides a robust performance over a wide set of benchmarks. The solver SDSAT works in two phases: allocation and solve. In the allocation phase, it allocates non-uniform adequate ranges for variables appearing in separation predicates. This phase is similar to previous Small Domain encoding approaches, but uses a novel algorithm Nu-SMOD with 1-2 orders of magnitude improvement in performance and Smaller ranges for variables. Furthermore, the Separation Logic formula is not transformed into an equi-satisfiable Boolean formula in one step, but rather done lazily in the following phase. In the solve phase, SDSAT uses a lazy refinement approach to search for a satisfying model within the allocated ranges. Thus, any partially DL-theory consistent model can be discarded if it can not be satisfied within the allocated ranges. Note the crucial difference: in eager approaches, such a partially consistent model is not allowed in the first place, while in lazy approaches such a model is never discarded. Moreover, we dynamically refine the allocated ranges and search for a feasible solution within the updated ranges. This combined approach benefits from both the Smaller search space (as in eager approaches) and also from the theory-specific graph-based algorithms (characteristic of lazy approaches). Experimental results show that our method is robust and always better than or comparable to state-of-the art solvers.
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SDSAT: Tight Integration of Small Domain Encoding and Lazy Approaches in Solving Difference Logic
Journal on Satisfiability Boolean Modeling and Computation, 2007Co-Authors: Malay K. Ganai, Muralidhar Talupur, Aarti GuptaAbstract:Existing difference logic (DL) solvers can be broadly classified as eager or lazy, each with its own merits and de-merits. We propose a novel difference logic solver SDSAT that combines the strengths of both these approaches and provides a robust performance over a wide set of benchmarks. The solver SDSAT works in two phases: allocation and solve. In the allocation phase, it allocates non-uniform adequate ranges for variables appearing in difference predicates. This phase is similar to previous Small Domain encoding approaches, but uses a novel algorithm Nu-SMOD with 1-2 orders of magnitude improvement in performance and Smaller ranges for variables. Furthermore, the difference logic formula is not transformed into an equi-satisfiable Boolean formula in a single step, but rather done lazily in the following phase. In the solve phase, SDSAT uses a lazy refinement approach to search for a satisfying model within the allocated ranges. Thus, any partially DL-theory consistent model can be discarded if it cannot be satisfied within the allocated ranges. Note the crucial difference: in eager approaches, such a partially consistent model is not allowed in the first place, while in lazy approaches such a model is never discarded. Moreover, we dynamically refine the allocated ranges and search for a feasible solution within the updated ranges. This combined approach benefits from both the Smaller search space (as in eager approaches) and also from the theory-specific graph-based algorithms (characteristic of lazy approaches). Experimental results show that our method is robust and always better than or comparable to state of-the art solvers using similar eager or lazy techniques.
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TACAS - SDSAT : tight integration of Small Domain encoding and lazy approaches in a separation logic solver
Tools and Algorithms for the Construction and Analysis of Systems, 2006Co-Authors: Malay K. Ganai, Muralidhar Talupur, Aarti GuptaAbstract:Existing Separation Logic (a.k.a Difference Logic, DL) solvers can be broadly classified as eager or lazy, each with its own merits and de-merits. We propose a novel Separation Logic Solver SDSAT that combines the strengths of both these approaches and provides a robust performance over a wide set of benchmarks. The solver SDSAT works in two phases: allocation and solve. In the allocation phase, it allocates non-uniform adequate ranges for variables appearing in separation predicates. This phase is similar to previous Small Domain encoding approaches, but uses a novel algorithm Nu-SMOD with 1-2 orders of magnitude improvement in performance and Smaller ranges for variables. Furthermore, the Separation Logic formula is not transformed into an equi-satisfiable Boolean formula in one step, but rather done lazily in the following phase. In the solve phase, SDSAT uses a lazy refinement approach to search for a satisfying model within the allocated ranges. Thus, any partially DL-theory consistent model can be discarded if it can not be satisfied within the allocated ranges. Note the crucial difference: in eager approaches, such a partially consistent model is not allowed in the first place, while in lazy approaches such a model is never discarded. Moreover, we dynamically refine the allocated ranges and search for a feasible solution within the updated ranges. This combined approach benefits from both the Smaller search space (as in eager approaches) and also from the theory-specific graph-based algorithms (characteristic of lazy approaches). Experimental results show that our method is robust and always better than or comparable to state-of-the art solvers.
Maria Rosaria Ferrante - One of the best experts on this subject based on the ideXlab platform.
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Small Domain estimation of business parameters using skew- normal models*
2020Co-Authors: Silvia Pacei, Maria Rosaria FerranteAbstract:Small area estimation has been till now applied esp ecially to poverty and employment parameters, however the demand for reliable information for res tricted Domains is growing also with reference to business parameters. The aim of this work is to provide estimates of business parameters for Small Domains defined by cross-classifying geograph ical areas, economic activities and firms' size. We use data on the Small and Medium Enterprises sample survey (1-99 employees) conducted by the Italian National Statistical Institute (Istat) in 2008. We focus on the manufacturing sector and o n two basic outcomes, value added and labor cost, whi ch are used to calculate important economic competitiveness indicators. As for many of the cons idered Domains the number of sampled firms is too low to obtain reliable estimates using the "dir ect" estimation strategy currently employed by Istat, a Small area method estimation is advisable. We use model-based Small area estimators relying on area level models, that may be represent ed as a sampling model, which accounts for the sampling variability of the direct survey estimates , and a model linking the Small Domain parameters of interest to area-specific auxiliary d ata. We adopt a Hierarchical Bayesian approach to estimation. Some peculiar issues arise in business surveys (Cox et al., 1995; Riviere, 2002). One of the most relevant is the asymmetry of outcomes' distribution s due to the presence, above all in Italy, of a majority of Small firms. To take into account of th is peculiarity, we relax the normality assumption of the classic Fay-Herriot model (Fay and Herriot, 1979) and consider the skew-normal distribution. The specification of a skew-normal distribution off ers some advantages with respect to other non- symmetric distribution, such as allowing for modeli ng zero and negative values. The skew-normal distribution has been already considered by Ferraz and Moura (2011) for the sampling error. We propose: i) to assign a skew-normal distribution also to the random area effects; ii ) a bivariate extension of such skew-normal models, which enables us to take into account of the high correlation between the target variables. Moreover, we control for firms' heterogeneity by including the information on the size (available from the ASI A administrative archive) as auxiliary variable. To estimate area level models direct estimates as w ell as their associated variance are necessary. Direct estimates are obtained by using a Horvitz-Th ompson estimator because the Domains of interest are collections of strata. Instead, we use a bootstrap strategy to estimate variances and covariance between direct estimates, because the un availability of some design information hinders the replication of the calibration procedure used b y Istat. Results highlight the importance to take into accou nt of the asymmetry of data, as the skew-normal specification allows for a considerable gain in eff iciency, about 30% on average with respect to the direct estimator. Moreover, we find that it is not useful to specify a non symmetric distribution for the random effects when it is specified for the sam pling errors, while it is more effective to specify a bivariate distribution for the random effects the n for the sampling errors.
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Small Domain estimation of business statistics by using multivariate skew normal models
Journal of The Royal Statistical Society Series A-statistics in Society, 2017Co-Authors: Maria Rosaria Ferrante, Silvia PaceiAbstract:Summary Small Domain business statistics are becoming important for better planning business policies. We focus on the estimation of the averages of value added and labour cost in Small Domains. To take into account the positive skewness in the distribution of outcomes and the correlation between them, we propose a bivariate skew normal Small area model. Estimates are obtained from real survey data. The performance of the estimator proposed is evaluated on the basis of both survey data and a synthetic firm population. Results show that the model proposed increases the estimates’ reliability and that the estimates obtained make it possible to perform detailed regional economic studies.