Single Keyword Research Articles

Determinant factors of antenatal care used by pregnant women in Indonesia: A systematic review

Pregnancy complications can basically be detected through an Integrated Antenatal Care (ANC). In Indonesia, the Integrated ANC is conducted comprehensively and with quality, targeting all pregnant women. This research employed a systematic review by deriving articles from three databases between 2013 and 2023 using a single keyword: "Antenatal Care" and "Pregnant Women" in Indonesia. Several inclusion and exclusion criteria were applied to determine the articles that can advance to data analysis. Initially, the three databases produced 25,582 studies. After completing the review and excluding duplicated articles, 15 met the criteria. These articles described the ANC behaviour of pregnant women in six major regions in Indonesia. The results presented factors that most influenced ANC behaviour among pregnant women in Indonesia. These factors were divided into three major groups: personal factors, external factor, and social support. In particular, the factors influencing participants’ ANC visits were knowledge, education, economic status, husband's support, family support, occupation, parity, and economic status.

Recent advances and current status of gene therapy for epilepsy.

Epilepsy is a common neurological disorder with complex pathogenic mechanisms, and refractory epilepsy often lacks effective treatments. Gene therapy is a promising therapeutic option, with various preclinical experiments achieving positive results, some of which have progressed to clinical studies. This narrative review was conducted by searching for papers published in PubMed/MEDLINE with the following single and/or combination keywords: epilepsy, children, neurodevelopmental disorders, genetics, gene therapy, vectors, transgenes, receptors, ion channels, micro RNAs (miRNAs), clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein (Cas)9 (CRISPR/Cas9), expression regulation, optogenetics, chemical genetics, mitochondrial epilepsy, challenges, ethics, and disease models. Currently, gene therapy research in epilepsy primarily focuses on symptoms attenuationmediated by viral vectors such as adeno-associated virus and other types. Advances in gene therapy technologies, such as CRISPR/Cas9, have provided a new direction for epilepsy treatment. However, the clinical application still faces several challenges, including issues related to vectors, models, expression controllability, and ethical considerations. Here, we summarize the relevant research and clinical advances in gene therapy for epilepsy and outline the challenges facing its clinical application. In addition to the shortcomings inherent in gene therapy components, the reconfiguration of excitatory and inhibitory properties in epilepsy treatment is a delicate process. On-demand, cell-autonomous treatments and multidisciplinary collaborations may be crucial in addressing these issues. Understanding gene therapy for epilepsy will help clinicians gain a clearer perception of the research progress and challenges, guiding the design of future clinical protocols and research decisions.

Verifiable attribute-based multi-keyword search scheme with sensitive information hiding for cloud-assisted e-healthcare sharing systems

Cloud-assisted e-healthcare sharing systems (EHSSs) play an increasingly pivotal role in the contemporary healthcare field. By outsourcing electronic medical records (EMRs) to the cloud, hospitals can alleviate local storage and management burdens while facilitating data sharing. Due to the highly sensitive nature of EMRs, encryption is necessary before storing them on the cloud. Attribute-based keyword search (ABKS) enables the privacy protection of EMRs with efficient search services. However, there remain some limitations in practical application. Firstly, most ABKS schemes only support single keyword queries, resulting in inaccurate results and wastage of computing and bandwidth resources. Secondly, since sensitive information within EMRs is encrypted as a whole, different data users (including internal doctors and external researchers) should have varying access rights to prevent leakage of this sensitive information. Thirdly, incorrect search results could lead to misdiagnosis or endanger patients' lives and affect researchers' decision-making processes. To effectively tackle these challenges, this paper proposes a verifiable attribute-based multi-keyword search scheme with sensitive information hiding (VABMKS-SIH) for cloud-assisted EHSSs, where we present a secure model for multi-keyword search with two-level access structure by incorporating an improved blindness filtering technique into ciphertext-policy attribute-based encryption (CP-ABE) within existing keyword search framework. Our scheme employs a super-increasing sequence to aggregate multiple filtered data blocks into one unified ciphertext, thereby greatly reducing communication overhead during the transmission phases of ciphertext. To check the correctness of returned results, we introduce a lightweight algebraic signature algorithm based on fundamental algebraic operations. A security analysis demonstrates that VABMKS-SIH is provably secure under the random oracle mode. Additionally, we also evaluate the proposed scheme's performance to demonstrate its utility in cloud-assisted EHSSs.

Secure and Efficient Multi-keyword Fuzzy Search Over Encrypted Data on Alliance Chain

Background:: Data regulation can effectively resist data privacy leakage and abuse in the process of external sharing of energy big data, but how to securely retrieve vast amounts of regulatory data is a challenge. Objective:: To securely retrieve vast amounts of regulatory data, a secure and efficient searchable encryption scheme that supports multi-keyword fuzzy retrieval on the alliance chain is proposed. Method:: This scheme encrypts and stores the regulatory data on the hyperledger fabric alliance chain. Energy big data files (EBDF) are encrypted and stored on a cloud server. Using the symmetric searchable encryption technology to achieve secure retrieval of regulatory data on the chain and secure access to EBDF. To improve search efficiency, we propose a SDPHashMap index structure and use the special manhatton distance matrix(SMDM) measurement to calculate the similarity of queried keywords and index keywords to locate the trapdoor retrieval hash address. Utilizing the cuckoo filter cluster to test the membership of queried keywords. Results:: This scheme stores EBDF off-chain, effectively relieving the storage and communication pressure of the blockchain, improving search speed, and providing more accurate retrieval services than single keyword fuzzy retrieval. By the simulation-based adversary- challenger game model, the security analysis demonstrates that the proposed scheme has adaptive selected keyword attack semantic security. Conclusion:: Experimental results show that the proposed scheme has high efficiency in trapdoor generation and multi-keyword search stages.

SDTA: Secure Decentralized Trading Alliance for Electronic Medical Data

Abstract Massive medical data are indispensable for training diagnostic models to provide high-quality health monitoring services. The methods for sharing data in existing works involve securely and essentially copying data but often overlook the integration and efficiency of data storage, exchange and application. In this paper, we propose a Secure Decentralized Trading Alliance (SDTA) to encompass the entire process holistically. With monetary incentives, we formulate a chain-net structure for recording data digests and authentic transactions, thereby transforming data sharing into data trading without duplicating data storage. Data privacy is promised by encryption. To manage and employ encrypted medical data, users can update and search their encrypted data using an index and keywords, subsequently retrieving data within the SDTA framework. It is realized by a novel dynamic searchable symmetric encryption (SSE) with an $l$-level access strategy, which confines users to data pertinent solely to them, thus circumventing unnecessary data leakage. We scrutinize the storage efficiency and prove the fairness and security of SDTA. Finally, we generate datasets of varying sizes, where the time required to search for a single keyword is approximately 0.04 s with 1 000 000 (keyword, identifier) pairs, showing it quite acceptable.

Exploring research trends and priorities of genus Melia

The genus Melia is known for its secondary metabolites and recently, this genus is being explored for its timber. There are vast differences among its species. For instance, Melia azedarach is reported to be invasive and while another species, M. dubia, has diverse utility with complex germination and regeneration characteristics. Researchers globally have been working on various aspects of this genus; In this study, using topic modelling and science mapping approach, we attempted to understand research facets on this genus. The literature corpus of the Web of Science database was explored using a single keyword—“Melia” which yielded 1523 publications (1946–2022) and after scrutiny metadata of 1263 publications were used in the study. Although nine individual species were cited in the publications, only three species are accepted viz., M. dubia, M. azedarach, and M. volkensii. This implies taxonomic uncertainty, with potential confusion in assigning scientific findings to particular species. Thus, a taxonomic relook on this genus is warranted for a better assessment of the economic utility in many countries. More importantly, our results indicate that the research interests have recently shifted from the secondary metabolite constituents towards growth, biomass, wood properties, germination, plantation, and green synthesis. The shift in research focus toward wood properties of Melia sp. can impact the wood demand–supply at a global scale owing to its fast growth and the possibility of cultivation over a wider geographical range.

Systematic Review (2003–2023): Exploring Technology-Supported Cross-Cultural Learning through Review Studies

Technology-supported cross-cultural learning (TSCCL) is essential for effective intercultural communication, fostering global understanding, and facilitating collaboration in an interconnected world. Although previous studies and review articles have explored this field, a comprehensive analysis of trends and key findings in these reviews is lacking. This study aims to fill this gap by conducting a thorough examination of existing review studies. We employed a narrative synthesis approach consisting of three phases: preparation, organization, and abstraction. We selected and systematically reviewed thirty-one review articles published in twenty-five journals between 2003 and 2023. Our findings highlight key aspects of the reviewed articles, such as keywords, databases, selection criteria, theoretical foundations, content analysis, research questions, findings, and limitations related to technology-supported cross-cultural learning. We found that the researchers used various strategies for literature searches, including single, double, or multiple keywords depending on the study’s focus. The most commonly used keywords were telecollaboration, intercultural, communication, competence, language learning, and technology, indicating their significance in current research and practice. The number of papers reviewed varied across studies, as did the timeframe coverage, ranging from a few years to several decades. Researchers employed different databases, with Web of Science, ERIC, Scopus, and Google Scholar being the most popular choices. Inclusion and exclusion criteria were established to ensure methodological rigor and relevance. Theoretical foundations, such as the telecollaboration model and intercultural communicative competence model, were frequently employed. Open coding was the dominant content analysis approach used. Research questions were present in some studies but not explicitly stated in others. These findings shed light on the strategies and considerations used by researchers in conducting review studies on technology-supported cross-cultural learning, providing guidance for future research. The study presents a reference table encompassing all previously published reviews, which can be valuable for future scholars. By gaining insights from past studies and identifying unexplored research avenues, researchers can benefit from this report. Additionally, the implications and suggestions derived from this review study are valuable for educators and researchers in the TSCCL field. This review addresses gaps in the existing literature and contributes to advancing knowledge in the field of TSCCL.

An improved method for extracting essay tangency features in intelligent scoring of English essays

With the continuous improvement of teaching quality in China's colleges and universities, English teaching has received more and more attention, and the automatic scoring system for English composition has begun to be gradually applied in English teaching in colleges and universities, but the system can only score compositions objectively, which is difficult to meet the actual teaching needs. Therefore, this study first proposes a topic richness based approach for analyzing composition cut points, and then proposes a combination based automatic grading method for English compositions. Based on the above, an intelligent grading improvement method based on composition tangent feature extraction is proposed to improve the efficiency of the English composition grading system and reduce the teaching burden on teachers. The traditional grading method is to grade grammar or essays by introducing Analytic Hierarchy Process, artificial neural networks, or combining machine learning theory with multi feature fusion. However, based on the characteristics of single semantic similarity and keyword alignment, the comprehensiveness of the grading is insufficient, making it difficult to analyze the correlation between essay sentences and the overall theme. The experimental results show that the topic decision-making model proposed in this study has better deep learning performance, and its scoring accuracy shows an upward trend with the increase of the sample data size of the essay, increasing from 40.05% to 91.46%. And the main content of the learned is the irrelevant information of the first sentence of a new topic and the truncation point, which can effectively distinguish whether a sentence is a topic segmentation point, and effectively identifies whether two sentences belong to the same topic; the proposed topic segmentation model has better practicality and its ability to segment topics is stronger; the combined method proposed in this study The overall performance of the model is better than the composition scoring methods based on RNN and BiLSTM models. Under the same semantic vectorization processing operation, the per values present in the composition sets are all different, and this result indicates that the writers' ability and the difficulty of writing on the topics are all different. It is hoped that this study can provide some solution ideas for the research of intelligent scoring feature extraction methods in English.

Blockchain for Achieving Accountable Outsourcing Computations in Edge Computing

Since the emergence of cloud computing, data owners are being driven to move their intricate data management systems from on-site locations to private cloud providers in order to take advantage of increased flexibility and cost savings. However, sensitive data must be encrypted before being outsourced in order to safeguard data privacy. This renders outdated the conventional method of using data, which relies on plaintext keyword searches. It is therefore crucial to activate an encrypted cloud data search service. In order to meet the effective data retrieval, need, search services must support multi-keyword queries and offer result similarity rating, given the volume of data users and documents stored in cloud storage. Similar works on searchable encryption seldom distinguish between search results and concentrate on single keyword or Boolean keyword search. I define and solve the difficult problem of privacy-preserving multi-keyword ranked search over encrypted cloud data (MRSE) for the first time in this work. I also outline a tight set of privacy requirements that must be met in order for such a safe cloud data utilization system to be implemented.

A Secure and Efficient Framework for Multi-User Encrypted Cloud Databases Supporting Single and Multiple Keyword Searches

A Secure and Efficient Framework for Multi-User Encrypted Cloud Databases Supporting Single and Multiple Keyword Searches

Certificate-based authenticated encryption with keyword search: Enhanced security model and a concrete construction for Internet of Things

The Internet of Things (IoT) devices produce a humongous amount of data frequently stored on cloud servers, and as a result, cryptographic techniques to guarantee the privacy of outsourced data while preserving search ability on servers are becoming indispensable research topics. A prominent example of such topics is the concept of public key authenticated encryption with keyword search (PAEKS). In spite of the large number of PAEKS schemes in the literature, most existing schemes exhibit issues in certificate management, key escrow, or even key distribution. To address these issues, recently, PAEKS schemes in the certificate-based setting have gained attention. To the best of our knowledge, there exist only two Certificate-Based Authenticated Encryption with Keyword Search (CBAEKS) schemes, both presented with rather weak security models based on single keyword challenges. In this paper, we propose an enhanced security model for CBAEKS which captures notions of multi-ciphertext and multi-trapdoor indistinguishability, then proceed to devise a concrete instantiation for a CBAEKS scheme and formally prove its security under our enhanced model. Furthermore, we prove that the existing CBAEKS schemes are insecure under the enhanced security model. Comparisons with related schemes in the literature are also provided to demonstrate that the enhanced security is achieved at some affordable costs.

Evaluation of the Needs and Experiences of Patients with Hypertriglyceridemia: Social Media Listening Infosurveillance Study.

Hypertriglyceridemia is a risk factor for cardiovascular diseases. Internet usage in China is increasing, giving rise to large-scale data sources, especially to access, disseminate, and discuss medical information. Social media listening (SML) is a new approach to analyze and monitor online discussions related to various health-related topics in diverse diseases, which can generate insights into users' experiences and expectations. However, to date, no studies have evaluated the utility of SML to understand patients' cognizance and expectations pertaining to the management of hypertriglyceridemia. The aim of this study was to utilize SML to explore the disease cognition level of patients with hypertriglyceridemia, choice of intervention measures, and the status quo of online consultations and question-and-answer (Q&A) search platforms. An infosurveillance study was conducted wherein a disease-specific comprehensive search was performed between 2004 and 2020 in Q&A search and online consultation platforms. Predefined single and combined keywords related to hypertriglyceridemia were used in the search, including disease, symptoms, diagnosis, and treatment indicators; lifestyle interventions; and therapeutic agents. The search output was aggregated using an aggregator tool and evaluated. Disease-specific consultation data (n=69,845) and corresponding response data (n=111,763) were analyzed from 20 data sources (6 Q&A search platforms and 14 online consultation platforms). Doctors from inland areas had relatively high voice volumes and appear to exert a substantial influence on these platforms. Patients with hypertriglyceridemia engaging on the internet have an average level of cognition about the disease and its intervention measures. However, a strong demand for the concept of the disease and "how to treat it" was observed. More emphasis on the persistence of the disease and the safety of medications was observed. Young patients have a lower willingness for drug interventions, whereas patients with severe hypertriglyceridemia have a clearer intention to use drug intervention and few patients have a strong willingness for the use of traditional Chinese medicine. Findings from this disease-specific SML study revealed that patients with hypertriglyceridemia in China actively seek information from both online Q&A search and consultation platforms. However, the integrity of internet doctors' suggestions on lifestyle interventions and the accuracy of drug intervention recommendations still need to be improved. Further, a combined prospective qualitative study with SML is required for added rigor and confirmation of the relevance of the findings.

Diagnosis of Severity of Alzheimer’s Disease from Clinical notes in the Veterans Affairs Healthcare System

AbstractBackgroundAlzheimer’s disease (AD) typically progresses in three stages: mild, moderate, and severe. Early detection improves patient care related to identification of suitable interventions and end‐of‐life planning. However, AD severity diagnosis is uncommon. The aim of this study is to identify Veterans in various stages of AD using clinical notes in the Veterans Affairs Healthcare System.MethodNotes containing “Alz*” from fiscal year 2019 were identified then screened for information indicating a clinical diagnosis of probable AD. Screened notes with AD severity keywords (mild, moderate, severe, early, late, advanced) within four words of “Alz*” were considered as a severity diagnosis. Notes containing a single AD severity diagnosis were included in the study.Result141,816 Veterans with 547,318 notes containing “Alz*” were identified as probable AD cases. Roughly 9% of notes contained at least one severity keyword and 6% of notes contained a single severity keyword. 34,181 notes with a single severity keyword from 14,148 Veterans were included in this study. The cohort comprised of 96% males and 78% identified as White with a mean age of 79. Ten percent of Veterans with an AD note included severity diagnosis. Approximately 19%, 12%, 18%, 20%, 3%, and 28% of severity notes contained mild, moderate, severe, early, late, and advanced, respectively. The mean age for Veterans with a mild, moderate, or severe AD diagnosis increased from 78 to 81. The mean age for early, late, and advanced AD patients increased from 74, 79, and 81, respectively. AD severity diagnosis was found in notes related to primary care (11%), internal medicine (10%), psychiatry (8%), mental health (7%), neurology (6%), geriatric (5%), neuropsychology (1%), and psychology (1%).ConclusionAD severity diagnosis is uncommon. Identification of patients in various stages of AD will lead to a better understanding of transition from one severity to another and facilitate the coordination of services to improve patient care. Specifically, identification of AD patients in the early stage may identify candidates for novel therapeutics focused on delaying AD progression.

A Survey on Searchable Symmetric Encryption

Outsourcing data to the cloud has become prevalent, so Searchable Symmetric Encryption (SSE), one of the methods for protecting outsourced data, has arisen widespread interest. Moreover, many novel technologies and theories have emerged, especially for the attacks on SSE and privacy-preserving. But most surveys related to SSE concentrate on one aspect (e.g., single keyword search, fuzzy keyword search) or lack in-depth analysis. Therefore, we revisit the existing work and conduct a comprehensive analysis and summary. We provide an overview of state-of-the-art in SSE and focus on the privacy it can protect. Generally, (1) we study the work of the past few decades and classify SSE based on query expressiveness. Meanwhile, we summarize the existing schemes and analyze their performance on efficiency, storage space, index structures, and so on.; (2) we complement the gap in the privacy of SSE and introduce in detail the attacks and the related defenses; (3) we discuss the open issues and challenges in existing schemes and future research directions. We desire that our work will help novices to grasp and understand SSE comprehensively. We expect it can inspire the SSE community to discover more crucial leakages and design more efficient and secure constructions.

Multi-Client Boolean File Retrieval With Adaptable Authorization Switching for Secure Cloud Search Services

Secure cloud search services provide a cost-effective way for resource-constrained clients to search encrypted files in the cloud, where data owners can customize search authorization. Despite providing fine-grained authorization, traditional attribute-based keyword search (ABKS) solutions generally support single keyword search. Towards expressive queries over encrypted data, multi-client searchable symmetric encryption (MC-SSE) was introduced. However, current search authorizations of existing MC-SSEs:(i) cannot support dynamic updating; (ii) are (semi-)black-box implementations of attribute-based encryption; (iii) incur significant cost during system initialization and file encryption. To address these limitations, we present AasBirch, an MC-SSE system with fast fine-grained authorization that supports adaptable authorization switching from one policy to any other one. AasBirch achieves constant-size storage and lightweight time cost for system initialization, file encryption and file searching. We conduct extensive experiments based on Enron dataset in real cloud environment. Compared to state-of-the-art MC-SSE with fine-grained authorization, AasBirch achieves 30∼200× smaller public parameter and secret key size, with the assumed least frequent keyword in a query ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</i> -term) as 21. Moreover, it runs 10∼20× faster for file encryption and >20× faster for file searching. In addition, AasBirch outperforms 80,000× (resp. 7,850×) faster with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</i> -term = 1 (resp. = 21), as compared to classic dynamic ABKS system.

Validity of Actigraph for Measuring Energy Expenditure in Healthy Adults: A Systematic Review and Meta-Analysis.

The objective of this systematic review and meta-analysis was to assess the validity of the Actigraph triaxial accelerometer device in measuring physical activity energy expenditure (PAEE) in healthy adults, with indirect calorimetry (IC) serving as the validity criterion. A comprehensive search was conducted using the PubMed, Web of Science, and sportdiscuss databases, in addition to manual searches for supplementary sources. Search strategies were employed that involved conducting single keyword searches using the terms "gt3x" and "Actigraph gt3x". The literature search encompassed the timeframe spanning from 1 January 2010 to 1 March 2023. The methodological quality of the studies included in the analysis was evaluated using both the Downs and Black checklist and the Consensus-Based Criteria for Selection of Measurement Instruments (COSMIN) checklist. The meta-analysis was conducted using the Review Manager 5.4 software. The standardized mean difference (SMD) was calculated and expressed as a 95% confidence interval (CI). The significance level was set at α = 0.05. A systematic assessment of the Actigraph's performance was conducted through the descriptive analysis of computed effect sizes. A total of 4738 articles were retrieved from the initial search. After eliminating duplicate articles and excluding those deemed irrelevant, a comprehensive analysis was conducted on a total of 20 studies, encompassing a combined sample size of 1247 participants. The scores on the Downs and Black checklist ranged from 10 to 14, with a mean score of 11.35. The scores on the COSMIN checklist varied from 50% to 100%, with an average score of 65.83%. The meta-analysis findings revealed a small effect size (SMD = 0.01, 95% CI = 0.50-0.52, p = 0.97), indicating no statistically significant difference (p > 0.05). The meta-analysis revealed a small effect size when comparing the Actigraph and IC, suggesting that the Actigraph can be utilized for assessing total PAEE. Descriptive analyses have indicated that the Actigraph device has limited validity in accurately measuring energy expenditure during specific physical activities, such as high-intensity and low-intensity activities. Therefore, caution should be exercised when utilizing this device for such purposes. Furthermore, there was a significant correlation between the activity counts measured by the Actigraph and the PAEE, indicating that activity counts can be utilized as a predictive variable for PAEE.

Adding Perspective to the Bibliometric Mapping Using Bidirected Graph

Abstract Bibliometric mapping offers easiness in analyzing the relationship between publications through the network visuals created. Several applications, such as VOSviewer, Bibliometrix, and CiteSpace, make conducting network analysis more convenient. Moreover, the relationship provided is usually in the form of an undirected graph, which negates the two-way relationship created. This study attempts to demonstrate the significance of considering two-way relationships by proposing a keyword network formed using bidirected graphs and association rules to examine the two-way relationship of two or more keywords. According to the proposed bidirected graph, a two-way graph can add value and insight by analyzing the correlation between a single keyword and several others. Two of the four metrics used, Confidence and Conviction, are sufficient to support directed graphs. In contrast, Support and Full Counting are related because they both see the occurrences of a keyword, so using undirected graphs is necessary.

Image Retrieval Through Free-Form Query using Intelligent Text Processing

Image Retrieval is the process of retrieving images from the image/multimedia databases. Retrieval of images are carried out with various types of queries, free-form query is a text-query that consists of single or multiple keywords and/or concepts or descriptions of images with or without the inclusion of wild-card characters and/or punctuations. This work aims to handle image retrieval based on free-form text queries. Simple &amp; complex queries of conceptual descriptions of images are explored and an intelligent processing system with free-form queries based on the Bag-of-Words model is modified and built for natural scene images and on Diverse Social Images using the Damerau-Levenshtein edit distance measure. The efficacy of the proposed system is evaluated by testing 1500 free-form text queries and has resulted in a recall accuracy of 91.3% on natural scene images (of Wang/Corel database) and 100% on Diverse Social Images (of DIV400 dataset). These results show that the system proposed has produced satisfactory performance compared to published results such as the harmonic mean of precision and recall (i.e. F1-Score) of 76.70% &amp; 63.32% at retrieval of 20 images etc in reported works.

Aggregate Functions in Categorical Data Skyline Search (CDSS) for Multi-keyword Document Search

Literature review is the first step in starting research for a deep understanding of the research interest. However, finding literature relevant to research interests is difficult and takes time. Skyline query is a method that can be used for filtering. An object p is said to dominate object q if p equals q on all of its attributes, and p is at least better than q on one attribute. Categorical Data Skyline Search (CDSS) is an algorithm that can filter skyline objects in categorical data types such as documents. CDSS uses Extended Distance Wu and Palmer (DEWP) to calculate the distance between the user query and document keywords. The document keywords and user queries are represented as nodes in the ACM CCS ontology, and documents are assumed to be represented by a single keyword. This study aims to use the CDSS algorithm to search for skyline documents represented by more than one keyword by adding an aggregate function (average, minimum, maximum) to the CDSS algorithm, especially in calculating DEWP. This study used the thesis documents from the IPB University computer science department. Document keywords will be extracted using the Term Frequency-Inverse Term Frequency (TF-IDF) method. The collected keywords will be mapped in a mixed ontology tree that refers to the Association of Computing Machinery Computing Classification System 2012 (ACM CCS 2012) and Computer Science Ontology (CSO) as ontology standards in computer science. The skyline query algorithm for determining skyline documents is Block Nested Loop (BNL). The evaluation method uses the skyline ratio of each aggregate function in the CDSS. Based on the ratio value, CDSS using the maximum DEWP has the most relevant skyline results compared to the average DEWP and minimum DEWP.

Attribute-Based Expressive and Ranked Keyword Search Over Encrypted Documents in Cloud Computing

Attribute-based keyword search (ABKS) has been proposed to realize fine-grained access control and provide search service in cloud computing. However, most ABKS schemes focus on single or conjunctive keyword search, while the recent Boolean keyword search schemes only support monotonic query formula mainly involving AND, OR and threshold operators. How to support more expressive Boolean query formulas and return the corresponding accurate search results to users have become challenges for practical ABKS over ciphertexts. In this paper, we introduce an attribute-based expressive and ranked keyword search scheme over encrypted documents named ABERKS, which allows authorized users to submit expressive Boolean query formulas involving AND, OR, NOT and threshold operators. ABERKS utilizes a non-monotonic access tree structure to construct the query formula, and further leverages extended Boolean model to rank the search results. Specifically, the users are able to define the weights in the query formula, and get the relevance score of each matched ciphertext if the attributes and keywords are both satisfied. We prove the security of ABERKS against chosen keyword attack under selective ciphertext policy model and against keyword guessing attack, and also conduct extensive experiments to show the efficiency and practicality of ABERKS.