Fuzzy Searches Research Articles

Encryption Scheme of Verifiable Search Based on Blockchain in Cloud Environment

While transferring data to cloud servers frees users from having to manage it, it eventually raises new problems, such as data privacy. The concept of searchable encryption has drawn more and more focus in research as a means of resolving the tension between data accessibility and data privacy. Due to the lack of integrity and correctness authentication in most searchable encryption techniques, malicious cloud servers may deliver false search results to users. Based on public key encryption with searching (PEKS), the study suggests a privacy-preserving method for verifiable fuzzy keyword searches based on the Ethernet blockchain in a cloud context to overcome the aforementioned security concerns. The search user can check the accuracy and integrity of the query document using the unalterability characteristics of the Ethernet blockchain system in this scheme to prevent the cloud server from giving incorrect query results. Furthermore, a fair transaction between the cloud server and the data user is achieved and can be tracked back to the malicious user using hash functions and Ethereum smart contracts, even if the user or the cloud is malicious. Finally, the security analysis shows that, under the random oracle model, our technique fulfils the adaptive selection keyword’s semantic security. The performance assessment demonstrates that the proposed scheme outperforms other related schemes in terms of computational efficiency.

Beacon v2 and Beacon networks: A "lingua franca" for federated data discovery in biomedical genomics, and beyond.

Beacon is a basic data discovery protocol issued by the Global Alliance for Genomics and Health (GA4GH). The main goal addressed by version 1 of the Beacon protocol was to test the feasibility of broadly sharing human genomic data, through providing simple “yes” or “no” responses to queries about the presence of a given variant in datasets hosted by Beacon providers. The popularity of this concept has fostered the design of a version 2, that better serves real‐world requirements and addresses the needs of clinical genomics research and healthcare, as assessed by several contributing projects and organizations. Particularly, rare disease genetics and cancer research will benefit from new case level and genomic variant level requests and the enabling of richer phenotype and clinical queries as well as support for fuzzy searches. Beacon is designed as a “lingua franca” to bridge data collections hosted in software solutions with different and rich interfaces. Beacon version 2 works alongside popular standards like Phenopackets, OMOP, or FHIR, allowing implementing consortia to return matches in beacon responses and provide a handover to their preferred data exchange format. The protocol is being explored by other research domains and is being tested in several international projects.

Secure Multi-keyword Fuzzy Searches With Enhanced Service Quality in Cloud Computing

With the ever-increasing amount of data resided in a cloud, how to provide users with secure and practical query services has become the key to improve the quality of cloud services. Fuzzy searchable encryption (FSE) is identified as one of the most promising approaches for enabling secure query services, since it allows searching encrypted data by using keywords with spelling errors. However, existing FSE schemes are far from the practical use for the following reasons: (1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Inflexibility.</i> It is hard for them to simultaneously support AND and OR semantics in a multi-keyword query. (2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Inefficiency.</i> They require sequentially scanning a whole dataset to find matched files, and thus are difficult to apply to a large-scale dataset. (3) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Limited robustness.</i> It is difficult for them to resist the linear analysis attack in the known-background model. To fix the above problems, this article proposes matrix-based multi-keyword fuzzy search (M2FS) schemes, which support approximate keyword matching by exploiting the indecomposable property of primes. Specifically, we first present a basic scheme, called M2FS-B, where multiple keywords in a query or a file are constructed as prime-related matrices such that the result of matrix multiplication can be employed to determine the level of matching for different query semantics. Then, we construct an advanced scheme, named M2FS-E, which builds a searchable index as a keyword balanced binary (KBB) tree for dynamic and parallel searches, while adding random noises into a query matrix for enhanced robustness. Extensive analyses and experiments demonstrate the validity of our M2FS schemes.

SE-PPFM: A Searchable Encryption Scheme Supporting Privacy-Preserving Fuzzy Multikeyword in Cloud Systems

Cloud computing provides an appearing application for compelling vision in managing big-data files and responding queries over a distributed cloud platform. To overcome privacy revealing risks, sensitive documents and private data are usually stored in the clouds in a cipher-based manner. However, it is inefficient to search the data in traditional encryption systems. Searchable encryption is a useful cryptographic primitive to enable users to retrieve data in ciphertexts. However, the traditional searchable encryptions provide lower search efficiency and cannot carry out fuzzy multikeyword queries. To solve this issue, in this article, we propose a searchable encryption that supports privacy-preserving fuzzy multikeyword search (SE-PPFM) in cloud systems, which is built by asymmetric scalar-product-preserving encryptions and Hadamard product operations. In order to realize the functionality of efficient fuzzy searches, we employ Word2vec as the primitive of machine learning to obtain a fuzzy correlation score between encrypted data and queries predicates. We analyze and evaluate the performance in terms of token of multikeyword, retrieval and match time, file retrieval time and matching accuracy, etc. The experimental results show that our scheme can achieve a higher efficiency in fuzzy multikeyword ciphertext search and provide a higher accuracy in retrieving and matching procedure.

АВТОМАТИЗАЦИЯ ПРОЦЕССА ТЕСТИРОВАНИЯ УЯЗВИМОСТЕЙ В КОРПОРАТИВНЫХ СИСТЕМАХ НА ОСНОВЕ НЕЙРО-НЕЧЁТКИХ АЛГОРИТМОВ

At the current stage of technology development, information systems on a corporate scale are becoming more and more widespread. Corporate systems include a hierarchical set of services and services that are constantly subject to internal changes in the configuration of the software and hardware component of the entire complex. Among other things, the system may represent a concept of Internet Things, combining not only the main devices, but also additional devices to organize the convenience of the end user interaction. Such a complex is subject to constant threats from the point of view of information security due to the changes made, since the presence of an error even in a separate component can lead to the presence of a vulnerability that allows to get an unauthorized access to the whole system, and as a result — unauthorized access to information and management of the target object. To study such systems for vulnerabilities, it is proposed to develop an information product that would allow to automate the process of searching and exploitation of vulnerabilities in the whole network infrastructure and subsequent reporting on various criteria. The product under development is intended for the operating system of the Unix family and is intended for use on specialized operating systems such as Kali Linux, Parrot Sec, Nethunter, etc. Considering a corporate network segment, it is assumed to organize a search for vulnerabilities by introducing neutrally fuzzy algorithms that search for several parameters and, if successful, nested search in a particular segment. The Rapid7 vulnerability database is supposed to be used as the input data to perform fuzzy searches. This approach will allow to identify vulnerabilities both in a separate system interaction proto col and in the operating system of a separate object, which may be a switch or server. As a result, it is planned to obtain an automated management system for the Metasploit Framework through a web interface, allowing the information security specialist to quickly eliminate the identified vulnerabilities.

Development of Mobile System for Medical Recommendations

This article considers the current problem of investigation mobile application and development of mobile system for medical recommendations. The article develops a method for the selection of constituent elements for constructing a text template. The text block pattern is described using Bukus-Naur notation. An algorithm for fuzzy duplicate searches in natural language texts was constructed. This paper presents the method of forming a pattern, which is absent in the pattern database and the method of allocating a pragmatic sign from a semi-structured natural language text is developed. Finally, authors present modelling and developing of the mobile system for medical recommendation.

Base-By-Base Version 3: New Comparative Tools for Large Virus Genomes.

Base-By-Base is a comprehensive tool for the creation and editing of multiple sequence alignments that is coded in Java and runs on multiple platforms. It can be used with gene and protein sequences as well as with large viral genomes, which themselves can contain gene annotations. This report describes new features added to Base-By-Base over the last 7 years. The two most significant additions are: (1) The recoding and inclusion of “consensus-degenerate hybrid oligonucleotide primers” (CODEHOP), a popular tool for the design of degenerate primers from a multiple sequence alignment of proteins; and (2) the ability to perform fuzzy searches within the columns of sequence data in multiple sequence alignments to determine the distribution of sequence variants among the sequences. The intuitive interface focuses on the presentation of results in easily understood visualizations and providing the ability to annotate the sequences in a multiple alignment with analytic and user data.

Development of a networked mass spectral database for veterinary drug residues

A networked and quickly searchable mass spectral database for veterinary drug residues with multiple retrieval modes was established for recording compound and mass information of more than 400 veterinary drugs. The multiple retrieval approaches include mass spectra retrieval (by ionization mode, and precursor and product ion peaks) and compound retrieval (advanced and fuzzy searches). A query can afford the compound and mass information of the veterinary drugs, in which the former includes the Chinese and English name, Chinese alias, abbreviation, chemical formula, molecular weight, molecular structure, CAS number, category, and residue markers of the compound, and a listing of isomers and compounds with the same molecular weight. The mass information comprises the mass spectrum and ion data. Mass spectra of the veterinary drugs were obtained under the optimized parameters of an API 5000 instrument using different ionization techniques in the [M+H]+, [M−H]−, [M+2H]2+, [M+3H]3+, [M+Na]+, [M+NH4]+, [M+K]+, and [M+HCOO]− modes. In addition, the search results could be used to compare the mass spectra of different compounds. The networked mass spectral database of veterinary drug residues will provide a reference for qualitative compound data.

Flexible and Fast Mapping of Peptides to a Proteome with ProteoMapper.

Bottom-up proteomics relies on the proteolytic or chemical cleavage of proteins into peptides, the identification of those peptides via mass spectrometry, and the mapping of the identified peptides back to the reference proteome to infer which possible proteins are identified. Reliable mapping of peptides to proteins still poses substantial challenges when considering similar proteins, protein families, splice isoforms, sequence variation, and possible residue mass modifications, combined with an imperfect and incomplete understanding of the proteome. The ProteoMapper tool enables a comprehensive and rapid mapping of peptides to a reference proteome. The indexer component creates a segmented index for an input proteome from a FASTA or PEFF file. The ProMaST component provides ultrafast mapping of one or more input peptides against the index. ProteoMapper allows searches that take into account known sequence variation encoded in PEFF files. It also enables fuzzy searches to find highly similar peptides with residue order changes or other isobaric or near-isobaric substitutions within a specified mass tolerance. We demonstrate an example of a one-hit-wonder identification in PeptideAtlas that may be better explained by a combination of catalogued and uncatalogued sequence variation in another highly observed protein. ProteoMapper is a free and open source, available for local use after downloading, embedding in other applications, as an online web tool at http://www.peptideatlas.org/map , and as a web service.

Efficient Multidimensional Fuzzy Search for Personal Information Management Systems

With the explosion in the amount of semistructured data users access and store in personal information management systems, there is a critical need for powerful search tools to retrieve often very heterogeneous data in a simple and efficient way. Existing tools typically support some IR-style ranking on the textual part of the query, but only consider structure (e.g., file directory) and metadata (e.g., date, file type) as filtering conditions. We propose a novel multidimensional search approach that allows users to perform fuzzy searches for structure and metadata conditions in addition to keyword conditions. Our techniques individually score each dimension and integrate the three dimension scores into a meaningful unified score. We also design indexes and algorithms to efficiently identify the most relevant files that match multidimensional queries. We perform a thorough experimental evaluation of our approach and show that our relaxation and scoring framework for fuzzy query conditions in noncontent dimensions can significantly improve ranking accuracy. We also show that our query processing strategies perform and scale well, making our fuzzy search approach practical for every day usage.

A Comparative Analysis of Queuing and Fuzzy Logic based Admission Control Schemes in CDMA Cellular Network

In CDMA cellular system, the coverage area of the cell is greatly influenced by soft handoff process. The variation in soft handoff coverage area can be taken as the parameter influencing the performance parameters of the CDMA cellular system. This paper is concerned with a comparative analysis of two queuing based call admission control schemes with a fuzzy based CAC (call admission controller) model. The analytical models developed are based on channel reservation scheme for handoff calls. Finite queuing scheme for handoff call is used in first model whereas finite queuing for both handoff as well as new call is considered in the second model. The fuzzy call admission controller is implemented on Mamdani inference scheme taking relative mobility of user and number of reserved channels for handoff calls as input parameters. The output parameter is taken as handoff queue capacity. The fuzzy CAC searches for optimum handoff queue capacity to meet the QoS requirements.

Efficient Multi Dimensional Nodal Fuzzy Search for Information Management System

We propose a novel multi-dimensional search approach that allows users to perform fuzzy searches for structure and metadata conditions in addition to keyword conditions. Our techniques individually score each dimension and integrate the three dimension scores into a meaningful unified score. We also design indexes and algorithms to efficiently identify the most relevant files that match multi-dimensional queries. We perform a thorough experimental evaluation of our approach and show that our relaxation and scoring framework for fuzzy query conditions in non content dimensions can significantly improve ranking accuracy. We also show that our query processing strategies perform and scale well, making our fuzzy search approach practical for every day usage.

Sequence Searcher: A Java tool to perform regular expression and fuzzy searches of multiple DNA and protein sequences.

BackgroundMany sequence-searching tools have limiting factors for their use. For example, they may be platform specific, enforce restrictive size limits and sequences to be searched, or only allow searches of one of DNA or protein.FindingsWe present an easy-to-use, fast, platform-independent tool to search for amino acid or nucleotide patterns within one or many protein or nucleic acid sequences. The user can choose to search for regular expressions or perform a fuzzy search in which a particular number of errors is accepted during matching of a sequence. Positions of mismatches in fuzzy searches are displayed graphically the user.ConclusionSeqS provides an improved feature set and functions as a stand-alone tool or could be integrated into other bioinformatics platforms.

BIOZON: a system for unification, management and analysis of heterogeneous biological data.

BackgroundIntegration of heterogeneous data types is a challenging problem, especially in biology, where the number of databases and data types increase rapidly. Amongst the problems that one has to face are integrity, consistency, redundancy, connectivity, expressiveness and updatability.DescriptionHere we present a system (Biozon) that addresses these problems, and offers biologists a new knowledge resource to navigate through and explore. Biozon unifies multiple biological databases consisting of a variety of data types (such as DNA sequences, proteins, interactions and cellular pathways). It is fundamentally different from previous efforts as it uses a single extensive and tightly connected graph schema wrapped with hierarchical ontology of documents and relations. Beyond warehousing existing data, Biozon computes and stores novel derived data, such as similarity relationships and functional predictions. The integration of similarity data allows propagation of knowledge through inference and fuzzy searches. Sophisticated methods of query that span multiple data types were implemented and first-of-a-kind biological ranking systems were explored and integrated.ConclusionThe Biozon system is an extensive knowledge resource of heterogeneous biological data. Currently, it holds more than 100 million biological documents and 6.5 billion relations between them. The database is accessible through an advanced web interface that supports complex queries, "fuzzy" searches, data materialization and more, online at .

A fuzzy ARTMAP nonparametric probability estimator for nonstationary pattern recognition problems

An incremental, nonparametric probability estimation procedure using the fuzzy ARTMAP (adaptive resonance theory-supervised predictive mapping) neural network is introduced. In the slow-learning mode, fuzzy ARTMAP searches for patterns of data on which to build ever more accurate estimates. In max-nodes mode, the network initially learns a fixed number of categories, and weights are then adjusted gradually.

Fuzzy linguistic data bases: An application

This paper discusses the empirical development and use of a fuzzy linguistic data base which is relational in character. Unlike conventional fuzzy searches for crisp data, we search a data base for fuzzy information. The data base supports another code which does fuzzy aggregation of data. It has been applied to support decision making connected with the construction of military budgets for command, control and communications (C3).