Efficient Boolean Research Articles

Efficient boolean SSE: A novel encrypted database (EDB) for biometric authentication

Biometric authentication is up-and-coming to replace the traditional identity authentication method (e.g., passwords, PIN, identification cards) for its convenience and intelligence. With more and more users using this method, the database becomes more extensive, and the functions are seriously challenged. Data outsourcing has advantages in terms of convenience and cost savings, so it has attracted much research effort. However, due to the biometric's immutability of the whole life, it is extremely sensitive, and disclosing it to a third party is undesirable. In this paper, we address the issue of securely outsourcing biometric database. We propose a novel boolean searchable symmetric encryption (SSE) to construct a secure interactive protocol when outsourcing. A new encrypted database construction method was proposed, using the more efficient boolean vectors. Based on this, We suggest three kinds of expressive SSE, supporting disjunctive query, boolean query, and lightweight settings. We prove the schemes' correctness and security theoretically. Our constructions use simple cryptographic tools, such as symmetric cryptography and pseudo-random functions. They are straightforward to understand and easy to implement. The experiments show that all our schemes are practical and more efficient than the existing methods.

Efficient Boolean Keywords Search over Encrypted Cloud Data in Public Key Setting

Searchable public key encryption- (SPE-) supporting keyword search plays an important role in cloud computing for data confidentiality. The current SPE scheme mainly supports conjunctive or disjunctive keywords search which belongs to very basic query operations. In this paper, we propose an efficient and secure SPE scheme that supports Boolean keywords search, which is more advanced than the conjunctive and disjunctive keywords search. We first develop a keyword conversion method, which can change the index and Boolean keywords query into a group of vectors. Then, through applying a technique so-called dual pairing vector space to encrypt the obtained vectors, we propose a concrete scheme proven to be secure under chosen keyword attack. Finally, we put forward a detailed theoretical and experimental analysis to demonstrate the efficiency of our scheme.

Fast and Efficient Boolean Matrix Factorization by Geometric Segmentation

Boolean matrix has been used to represent digital information in many fields, including bank transaction, crime records, natural language processing, protein-protein interaction, etc. Boolean matrix factorization (BMF) aims to find an approximation of a binary matrix as the Boolean product of two low rank Boolean matrices, which could generate vast amount of information for the patterns of relationships between the features and samples. Inspired by binary matrix permutation theories and geometric segmentation, we developed a fast and efficient BMF approach, called MEBF (Median Expansion for Boolean Factorization). Overall, MEBF adopted a heuristic approach to locate binary patterns presented as submatrices that are dense in 1's. At each iteration, MEBF permutates the rows and columns such that the permutated matrix is approximately Upper Triangular-Like (UTL) with so-called Simultaneous Consecutive-ones Property (SC1P). The largest submatrix dense in 1 would lie on the upper triangular area of the permutated matrix, and its location was determined based on a geometric segmentation of a triangular. We compared MEBF with other state of the art approaches on data scenarios with different density and noise levels. MEBF demonstrated superior performances in lower reconstruction error, and higher computational efficiency, as well as more accurate density patterns than popular methods such as ASSO, PANDA and Message Passing. We demonstrated the application of MEBF on both binary and non-binary data sets, and revealed its further potential in knowledge retrieving and data denoising.

An efficient Boolean based multi-secret image sharing scheme

The purpose of this paper is to develop an algorithm for sharing k secret images to n participants in such a way that each participant gets a single share image by encoding all k images. Any qualified subgroup of t : t ≤ n of those n participants can reconstruct the kith secret image only by combining their share images if they are qualified to reconstruct the kith secret image. Most of the existing literature solves this problem for the cases where t = 2 or t = n making it a very restrictive scheme. In this article, we aim to design a multi-secret image sharing scheme based on XOR operation where t is not restricted to be 2 or n. We have used n random matrices of the same size as the secret image size as private share to generate r (where r is the number of qualified subgroups) share images as public share using XOR operations. The proposed scheme is computationally lightweight and lossless due to XOR operation only. It does not involve any pixel expansion. The experimental results with a very low correlation coefficient between share and secret images confirm that share image does not reveal anything about secret image. The scheme is secure against differential attack as a higher value of Number of Changing Pixel rate (NPCR) confirms that. The current proposal is based on a general access structure, and hence any secret image can be reconstructed by a qualified group of t or more shares where t need not be 2 or n only.

PS-tree-based efficient boolean expression matching for high-dimensional and dense workloads

Boolean expression matching is an important function for many applications. However, existing solutions still suffer from limitations when applied to high-dimensional and dense workloads. To overcome these limitations, in this paper, we design a data structure called PS-Tree that can efficiently index subscriptions in one dimension. By dividing predicates into disjoint predicate spaces, PS-Tree achieves high matching performance and good expressiveness. Based on PS-Tree, we first propose a Boolean expression matching algorithm PSTBloom. By efficiently filtering out a large proportion of unmatching subscriptions, PSTBloom achieves high matching performance, especially for high-dimensional workloads. PSTBloom also achieves fast index construction and a small memory footprint. Compared with state-of-the-art methods, comprehensive experiments show that PSTBloom reduces matching time, index construction time and memory usage by up to 84%, 78% and 94%, respectively. Although PSTBloom is effective for many workload distributions, dense workloads represent new challenges to PSTBloom and other algorithms. To effectively handle dense workloads, we further propose the PSTHash algorithm, which divides subscriptions into disjoint multidimensional predicate spaces. This organization prunes partially matching subscriptions efficiently. Comprehensive experiments on both synthetic and real-world datasets show that PSTHash improves the matching performance by up to 92% for dense workloads.

SAT-Based Control of Concurrent Software for Deadlock Avoidance

We present a highly efficient boolean satisfiability (SAT) formulation for deadlock detection and avoidance in concurrent programs modeled by Gadara nets, a class of Petri nets. The SAT formulation is used in an optimal control synthesis framework based on Discrete Control Theory. We compare our method with existing methods and show a significant increase in scalability. Stress tests show that our technique is capable of synthesizing deadlock avoidance control logic in programs modeled by Gadara nets with over $10^{9}$ unsafe states.

Parallel and efficient Boolean on polygonal solids

We present a novel framework which can efficiently evaluate approximate Boolean set operations for B-rep models by highly parallel algorithms. This is achieved by taking axis-aligned surfels of Layered Depth Images (LDI) as a bridge and performing Boolean operations on the structured points. As compared with prior surfel-based approaches, this paper has much improvement. Firstly, we adopt key-data pairs to store LDI more compactly. Secondly, robust depth peeling is investigated to overcome the bottleneck of layer-complexity. Thirdly, an out-of-core tiling technique is presented to overcome the limitation of memory. Real-time feedback is provided by streaming the proposed pipeline on the many-core graphics hardware.

The characteristics of biofilms in peri‐implant disease

To describe the microbiota associated with peri-implant disease, with a specific emphasis on the differential diagnosis of the condition. The potentially relevant literature was preliminarily assessed via scoping searches to find the most appropriate search terms and the most efficient Boolean search algorithm. We identified 29 reports on subjects with osseointegrated implants, with a pathological condition compatible with the definition of "peri-implant disease", and reporting microbiological data from samples taken in affected sites. In most studies bacterial samples were obtained by methods that destroy the three-dimensional structure of the biofilm. The samples therefore describe mixtures of bacteria from unspecified districts of biofilm associated with peri-implant diseases. Analyses of such samples with various methods indicate that peri-implant disease maybe viewed as a mixed anaerobic infection. In most cases the composition of the flora is similar to the subgingival flora of chronic periodontitis that is dominated by Gram-negative bacteria. Peri-implant infections may occasionally be linked to a different microbiota, including high numbers of peptostreptococci or staphylococci. Beneficial effects of mechanical and chemical interventions to disrupt the peri-implant biofilm demonstrate that microorganisms are involved in the disease process, even if they may not always be the origin of the condition.

Dealing with Satisfiability and n-ary CSPs in a Logical Framework

We investigate in this work a generalization of the known CNF representation that we call General Normal Form (GNF) and extend the resolution rule of Robinson to design a new sound and complete resolution proof system for the GNF. We show that by using a cardinality operator in the GNF we obtain the new representation CGNF that allows a natural and efficient Boolean encoding for n-ary finite discrete extensional CSPs. We prove that the space complexity of a CSP encoded in the CGNF is identical to its space complexity when it is expressed in the classical CSP representation. We prove that the generalized resolution rule applies for the CGNF encoding and introduce a new inference rule whose application until saturation achieves arc-consistency in a linear time complexity for n-ary CSP expressed in the CGNF encoding. Two enumerative methods for solving CSP instances encoded in this Boolean framework are studied: the first one (equivalent to MAC in CSPs) maintains full arc-consistency at each node of the search tree while the second (equivalent to FC in CSPs) performs partial arc-consistency on each node. Both methods are experimented and compared on some instances of the Ramsey problem, randomly generated 3/4-ary CSPs and promising results are obtained. We also adapted the notion of clause learning defined in SAT for the CGNF encoding. Finally, we show how the proposed inference rule can be used to achieve Path-consistency in the case of binary CSPs encoded in CGNF, and how it can be used to enforce strong path consistency when it is combined with the generalized resolution rule.

Efficient Boolean Operation on Manifold Mesh Surfaces

Computer-Aided Design and Applications is an international journal on the applications of CAD and CAM. It publishes papers in the general domain of CAD plus in emerging fields like bio-CAD, nano-CAD, soft-CAD, garment-CAD, PLM, PDM, CAD data mining, CAD and the internet, CAD education, genetic algorithms and CAD engines. The journal is aimed at all developers and users of CAD technology to ptovide CAD solutions for various stages of design and manufacturing. The journal publishes all about Computer-Aided Design and Computer-Aided technologies.

Modeling Biological Networks by Action Languages via Answer Set Programming

We describe an approach to modeling biological networks by action languages via answer set programming. To this end, we propose an action language for modeling biological networks, building on previous work by Baral et al. We introduce its syntax and semantics along with a translation into answer set programming, an efficient Boolean Constraint Programming Paradigm. Finally, we describe one of its applications, namely, the sulfur starvation response-pathway of the model plant Arabidopsis thaliana and sketch the functionality of our system and its usage.

A prototype implementation of a distributed satisfiability modulo theories solver in the ToolBus framework

An increasing number of verification tools (e.g., software model-checkers) require the use of Satisfiability Modulo Theories (SMT) solvers to implement the back-ends for the automatic analysis of specifications and properties. The most prominent approach to build SMT solvers consists in integrating an efficient Boolean solver with decision procedures capable of checking the satisfiability of sets of ground literals in selected theories. Although the problem of checking the satisfiability of arbitrary Boolean combinations of atoms modulo a background theory is NP-hard, there is a strong demand for high-performance SMT-solvers. In this paper, we describe the design and prototype implementation of-to the best of our knowledge-the first distributed SMT solver. The emphasis is on providing ways to reduce the implementation effort and to make the system easily extensible. This is achieved in two ways: (a) we re-use as much as possible the code of an available sequential SMT solver and (b) we adopt the TOOLBUS architecture for rapid prototyping. The behavior of the distributed SMT solver was tested on a set of problems which are representative of those generated by software verification techniques. The experiments show the possibility to obtain super-linear speed-ups of the distributed SMT solver with respect to its sequential version.

A prototype implementation of a distributed Satisfiability Modulo Theories solver in the ToolBus framework

Abstract An increasing number of verification tools (e.g., software model-checkers) require the use of Satisfiability Modulo Theories (SMT) solvers to implement the back-ends for the automatic analysis of specifications and properties. The most prominent approach to build SMT solvers consists in integrating an efficient Boolean solver with decision procedures capable of checking the satisfiability of sets of ground literals in selected theories. Although the problem of checking the satisfiability of arbitrary Boolean combinations of atoms modulo a background theory is NP-hard, there is a strong demand for high-performance SMT-solvers. In this paper, we describe the design and prototype implementation of—to the best of our knowledge—the first distributed SMT solver. The emphasis is on providing ways to reduce the implementation effort and to make the system easily extensible. This is achieved in two ways: (a) we re-use as much as possible the code of an available sequential SMT solver and (b) we adopt theToolBus architecture for rapid prototyping. The behavior of the distributed SMT solver was tested on a set of problems which are representative of those generated by software verification techniques. The experiments show the possibility to obtain super-linear speed-ups of the distributed SMT solver with respect to its sequential version.

A simple and efficient boolean solver for Constraint Logic Programming

We study in this paper the use of consistency techniques and local propagation methods, originally developed for constraints over finite domains, for solving boolean constraints in Constraint Logic Programming (CLP). To this aim, we first present a boolean CLP language clp(B/FD) built upon a CLP language over finite domains clp(FD) which uses a propagation-based constraint solver. It is based on a single primitive constraint which allows the boolean solver to be encoded at a low level. The boolean solver obtained in this way is both very simple and very efficient: on average it is eight times faster than the CHIP propagation-based boolean solver, i.e. nearly an order of magnitude faster, and infinitely better than the CHIP boolean unification solver. It also performs on average several times faster than special-purpose stand-alone boolean solvers. We then present in a second time several simplifications of the above approach, leading to the design of a very simple and compact dedicated boolean solver. This solver can be implemented in a WAM-based logical engine with a minimal extension limited to four new abstract instructions. This clp(B) system provides a further factor two speedup w.r.t. clp(B/FD).

Efficient Boolean Matrix and Graph Algorithms on a Bounded Parallel Computation Model

Two efficient boolean matrix multiplication algorithms on a classical k-bounded parallel computation model are proposed. Assuming that both the boolean matrices are of size (n × n) the time complexity of the first algorithm is O (max(n2/k, na/k)) whereas that for the second algorithm is O(max (n2/k, nβ/k)) for k ≦ n, where α (≦n2) and β ≦n⌈n2⌉) are integers. These algorithms are also applied for solving several important graph problems. The cost (i e parallel running time multiplied by the number of processors used) of each of these parallel algorithms is found better compared to their existing counterparts.