Multi Keyword Research Articles

Mechanisms of psychiatric disorders induced by amphetamines: A comprehensive review

Objectives: Amphetamines are stimulants and addictive drugs. Saudi officials announced in 2023 a huge campaign against abused drugs. Numerous studies have shown that amphetamines abuse may affect the functions and structures of the brain and result in damage and psychiatric disorders. Nothing evidence was gathered and summarized for all available mechanisms to induce psychiatric disorders. Therefore, we summarized the alterations that cause psychiatric disorders. Methods: We are accessed the studies included in our scope by conducting a literature review involves using engines of the health database such as APA, PubMed, Cochrane Review, Scopus, Google Scholar, Medline, and EMBASE to collect the data and identify the materials that describe the topic by using multi-key words including amphetamines, methamphetamine, ecstasy which known as (MDMA) 3,4-Methyl​enedioxy​methamphetamine , mechanisms of psychiatric disorders; substance-induced psychiatric disorders; psychosis; abuse mechanisms; cognitive-behavioral abnormalities. Results: Psychiatric Disorders induced by amphetamines is a result of direct mechanisms such as abnormalities in the levels of dopamine, glutamate, and GABA, in addition to high dopamine in the midbrain and forebrain, neuronal network dysregulation, and ultimately, cortical and subcortical damage. Indirect mechanisms are confirmed, such as long-term use, high doses, sleep deprivation, genetics, and reactive oxygen species. Dopamine functions and sensitization are confirmed in schizophrenia and drug-induced psychiatric disorders. Conclusion: Mechanisms that induce psychiatric disorders are complex and interrelated. Research focuses on alterations of dopaminergic, sensitization, glutamatergic, neuronal inflammation, cortical GABAergic neurons, apoptosis following neurotoxicity, and mediators of protein kinases. Amphetamine-induced psychiatric disorders could be avoided by early prevention and detoxification.

Efficient Secure Privacy Preserving Multi Keywords Rank Search over Encrypted Data in Cloud Computing

Verifiable multi keywords rank searchable encryption is an important technique to retrieve encrypted data in a cloud server and verify the validity of search results. The cloud server is malicious it may return incorrect search results or system fault. Thus, how to design verifiable secure multi keywords ranked search of outsourced encrypted data is an unsolved problem. Therefore, in this paper, we leverage the (T-SNE) techniques to reduce dimensionality and feathers transformation. Then adopt similar keywords clustering groups grouping strategy to construct an enhanced inverted index. Which links to coordinate matching techniques to improve accurate multi-keyword rank searches for practical user experience. Then Paillier Homorphic Encryption (P.H.E) and HMAC technique is adopted for search index encryption and keyword search verification to explore verifiable multi keywords rank search over encrypted data in the cloud server. The time cost of ranking score computation and search efficiency can be improved in two approaches, keywords grouping idea and no dummy keywords are added in query and documents vector. Thus, our scheme realizes verifiable multi keywords search exact ciphertext retrieval. We evaluated the performance of our proposed scheme using real-world datasets that ensure adequate security and privacy.

Optimal secure fuzzy based multi keyword search over ciphered e-mail in cloud

Cloud computing become increasingly more famous consistently, as numerous associations tend to outsource their information. With the outbreak of email message leakage, the protection and security of sensitive email data have become clients’ essential concerns. Encrypted email data is a superior method to ensure security, yet it will enormously restrict the searching. To take care of this difficulty keyword-based search over encrypted information is presented. The current search strategies permit the client to search utilizing just the specific keywords. There is no capacity to bear errors and format irregularities. In order to overcome those drawbacks, optimal secured fuzzy-based multi-keyword search over encrypted email data is proposed here. The email sender encrypts the email data before outsourcing the data to a cloud server. For encryption, the proposed method utilizes the optimal secure XOR (OSXOR) encryption algorithm. Here the key value is optimally selected by the mayfly optimization algorithm (MOA). After the encryption, the encrypted email is outsourced to the cloud server. The data owner creates an encrypted searchable index using an input file to enable querying across encrypted emails and then assigns either the index or the gathering of encrypted messages to a cloud server. The receiver receives them from the cloud server and is fed back information, but it is unable to comprehend the signal. The recipient of the encrypted email can decode it and create a search trapdoor in the encrypted email database. For searching, fuzzy-based multi-keyword search is proposed. The effectiveness of the proposed methodology is analyzed in terms of different metrics namely, Memory Usage, Execution time, Encryption and decryption time and search time. The experimental result shows that the proposed method takes a minimum search time is 0.51 s and it achieves maximum searching accuracy of 98%. The implementation is done in JAVA with a Cloud simulator.

Efficient processing of top-k frequent spatial keyword queries.

The rapid popularization of high-speed mobile communication technology and the continuous development of mobile network devices have given spatial textual big data (STBD) new dimensions due to their ability to record geographical objects from multiple sources and with complex attributes. Data mining from spatial textual datasets has become a meaningful study. As a popular topic for STBD, the top-k spatial keyword query has been developed in various forms to deal with different retrievals requirements. However, previous research focused mainly on indexing locational attributes and retrievals of few target attributes, and these correlations between large numbers of the textual attributes have not been fully studied and demonstrated. To further explore interrelated-knowledge in the textual attributes, this paper defines the top-k frequent spatial keyword query (tfSKQ) and proposes a novel hybrid index structure, named RCL-tree, based on the concept lattice theory. We also develop the tfSKQ algorithms to retrieve the most frequent and nearest spatial objects in STBD. One existing method and two baseline algorithms are implemented, and a series of experiments are carried out using real datasets to evaluate its performance. Results demonstrated the effectiveness and efficiency of the proposed RCL-tree in tfSKQ with the complex spatial multi keyword query conditions.

A secure searchable encryption scheme for cloud using hash-based indexing

Cloud service for data outsourcing has triggered security and privacy needs that can be satisfied by encrypting data prior to outsourcing to the cloud. Searchable Encryption that uses methods like homomorphic encryption is inefficient and impractical. In this paper, we have proposed a secure searchable encryption scheme using Hash based Indexing that consists of Elliptic curve based ElGamal additive homomorphic encryption. The use of indexing reduces computational load on the cloud server and users. The scheme obviates the need for generation and resource intensive processing on complex trapdoor system and binary query scheme. The security of this system relies on the Elliptic curve discrete logarithm problem assumption. Comparison analysis is performed and experimental results prove the suitability for the large data sets, while efficiently supporting the dynamic updates and effective ranking of precise files corresponding to the requested multi keywords.

Multi Keyword Search on Encrypted Data with Ranking

To maintain the anonymity of users, cloud storage owners often outsource encrypted documents. As a consequence, it is important to establish efficient and precise cypher text search techniques. One issue would be that the connection between documents is typically obscured during the encryption process, resulting in a significant deterioration of search accuracy efficiency. Additionally, the volume of data stored in data centers has exploded. This will make it significantly more difficult to create cipher text search schemes capable of providing efficient and reliable online information retrieval on large quantities of encrypted data. The paper proposes a hierarchical clustering approach in order to accommodate additional search semantics and to satisfy the demand for fast cipher text search in a big data environment. The proposed hierarchical approach clusters documents according to their minimum importance levels and then sub-clusters them until the maximum cluster size is reached. This approach can achieve linear computational complexity throughout the search process, spite of the fact that its size of the record set grows exponentially. The minimum hash sub-tree structure is used in this paper to check the validity of search results. The results demonstrate that as the number of documents in the dataset increases, the proposed method's search time increases linearly, while the conventional method's search time increases exponentially. Additionally, the suggested method outperforms the standard method in terms of rank privacy and document relevance.

Automatic Summarization on Restaurants Opinion Mining

Innovation has been a fundamental piece of our lives. When going bent another cafe/restaurants or bistro, individuals normally use sites or applications to question close by spots and afterward select one hooked in to normal rating. Notwithstanding, the traditional rating is now and again insufficient to foresee the character of the café as individuals have alternate points of view and wishes while assessing an eatery. During this paper, an overview framework for sentiments in eatery audits is proposed. The framework may be a useful device for clients during a hurry to assist them improve decisions about the nature of a restaurant while saving their time. This is often finished via naturally and rapidly furnishing the clients with a synopsis of the sentiments within the café's/restaurants surveys. The proposed synopsis framework has been actualized during a versatile area based application with KNN Algorithm and Multi keyword search it accomplished a high convenience score.

Enhanced Effective and Privacy Preserving Multi Keyword Search over Encrypted Data in Cloud Storage Using Blowfish Algorithm

The data owner administers their data to the public cloud due to regulatory. Even this data encoded once it easily transmitted to the cloud. In order to ensure the privacy and security, cloud subscribers need a very different type of online data. This specific information must be germane to the recipient's query. The user to inspect the query too in the cloud with umpteen keywords can accomplish this. With the intention to defend the privacy of data, confidential data encoded before subcontracting by the metadata owner, attempting to make the traditional and efficient plaintext keyword search tactic pointless. Therefore, it is crucial to explore a secure data search service for encrypted user data. Due to the growing of massive number of digital users and legal documents in the cloud, umpteen keywords forced in the search request and documents returned in the order of their relevance to these keywords.Cloud assistance end-user demands cloud information by various paternoster inquiry, whichlabelled as umpteenkeywordstratified exploration over scrambled information. In the cloud server, all the client inquiries exchanged. Server looks through the significant matter by utilizing the harmonic equivalency and sends the applicable outcomes to the client. Information got from cloud server is in the scrambled configuration. To get to control to the client,Information proprietor furnishes with key for unscrambling of information. Message Authentication Code(MAC) calculation is utilized to check and confirm trustworthiness of information. Thusly this paper depicts the method of giving security to redistribute information on cloud and checking the trustworthiness of information using blowfish algorithm. Comparison results shows the improvement in efficiency of Data Retrieval and Time efficiency.

Multi keyword searchable attribute based encryption for efficient retrieval of health Records in Cloud

Personal Health Record (PHR) is an online electronic application used by patients to store, retrieve and share their health information in a private and secure environment. While outsourcing the PHR into cloud environment, there exist issues in privacy while storing, searching and sharing of health information. To overcome these issues, an efficient retrieval of health records using Multi Keyword Searchable Attribute Based Encryption (MK-SABE) is proposed. To manage the increasing PHR data in the cloud, an Authorized File Level Deduplication technique is adopted. It eliminates redundant files, thereby reducing the communication overhead. Moreover, PHR data is encrypted before outsourcing and to perform searching over encrypted data, the proposed MK-SABE introduces Conjunctive Multi Keyword Searchable Attribute Based Encryption (CM-SABE). This maintains the searchable property after encryption for efficient retrieval of health files using range query. Further to ensure the trustworthiness while sharing the sensitive data, MK-SABE introduces the Location Based Encryption (LBE) and Dynamic Location Based ReEncryption (DLBRE) technique to provide additional security. From the experimental analysis, it is proved that the proposed MK-SABE reduces the storage complexity by 5%, keyword search time by 25% and improves the overall performance of PHR by 40% compared to the existing schemes.

An Efficient Privacy-Preserving Ranked Keyword Search Method

The keyword searching algorithm plays a vital role in cloud computing Architecture. Here, the searching technique is applied as a tool to encrypted data set which is outsourced. This technique works well, by either directed on multi keyword findings with exact match or one keyword search with fuzzy logic with a result of true or false. Though the existing techniques come across sensible difficultyin execution upon the encrypted data set, in the execution of multiple keyword based searching, keyword correction is difficult to achieve. To overcome with this problem, we construct a highly efficient, multiple corrections in keyword searching algorithm using ranking scheme. Multiple corrections like spell checks and errors. In the existing system of Author Wang and others scheme, they were unable to address the aforementioned problems, But in our method of multiple keyword searching technique, the Fuzzy Gramm algorithm helps to detect and handle the spelling errors by making a function call. Additionally the keywords with similar ancestry can be found by making use of stemming procedure. Alongside with thatrank keyword is used to achieve accurate checking with matched text file content. Thus the proposed approach enhances security and reliability to the maximum extent compared to the existing system.

A Correlation Coupling Prediction of Island Tourist Based on Multi Key Words of Web Search Index: A Case Study of Gulang Island in Xiamen

He, Y.; Huang, L.Y.; Ding, C.; Zou, Y., and Huang, P., 2020. A correlation coupling prediction of island tourist based on multi key words of web search index: A case study of Gulang Island in Xiamen. In: Bai, X. and Zhou, H. (eds.), Advances in Water Resources, Environmental Protection, and Sustainable Development. Journal of Coastal Research, Special Issue No. 115, pp. 373-378. Coconut Creek (Florida), ISSN 0749-0208.With the rapid rise of the Internet, people rely more on the Internet to collect tourism information when traveling. This paper will make a model by Baidu search index of keywords in scenic spot, use weighted calculation and composite index, take Gulang island in Xiamen as an example to verify the feasibility of the model, and conduct heat prediction analysis of scenic spot through prediction model. Through analysis and research, it is found that the Baidu search index has a strong positive correlation with the tourist volume of scenic spots, and the tourist volume and popularity of scenic spots can be predicted by the search volume of Baidu. This conclusion is conducive to the coordination and control between the government and the scenic spots, the market analysis of tourism enterprises, and the tourists' comfortable experience of peak travel.

Privacy Preserving Data Mining With Multi Keyword Ranked Search for Medical Data

Privacy Preserving Data Mining (PPDM) maintains the privacy of data stored in cloud. This work aims to protect outsourced data in cloud, and also permit multi keyword search over the encrypted data in a secure way by NLP process without downloading and decrypting all files. Different methods for privacy preservation were analyzed and randomization for multilevel trust is proposed along with an efficient method for keyword search in cloud.

Authorized Attribute-Based Encryption Multi-Keywords Search with Policy Updating

Attribute-based encryption is cryptographic techniques that provide flexible data access control to encrypted data content in cloud storage. Each trusted authority needs proper management and distribution of secret keys to the user’s to only authorized user’s attributes. However existing schemes cannot be applied multiple authority that supports only a single keywords search compare to multi keywords search high computational burden or inefficient attribute’s revocation. In this paper, a ciphertext policy attribute-based encryption (CP-ABE) scheme has been proposed which focuses on multi-keyword search and attribute revocation by new policy updating feathers under multiple authorities and central authority. The data owner encrypts the keywords index under the initial access policy. Moreover, this paper addresses further issues such as data access, search policy, and confidentiality against unauthorized users. Finally, we provide the correctness analysis, performance analysis and security proof for chosen keywords attack and search trapdoor in general group model using DBDH and DLIN assumption.

Protected and flexible multi-keyword search model over Encoded cloud data

Cloud computing is modern technology as a new computing model in number of business domains. Large numbers of large scale departments are starting to shift the data on to the cloud environment. With the benefit of storage as a service many enterprises are moving their valuable data to the cloud, since it costs less, easily scalable and can be accessed from anywhere any time. Improved dynamic multi-keyword ranking search scheme with top key via encrypted cloud data that simultaneously supports dynamic update operations as deleting and inserting documents. Greedy depth first search algorithm is provided for efficiency multi keywords on place and index structure. Cryptography is one of the establishing trust models. Searchable security is a cryptographic method to provide security. In number of researchers have been working on developing privacy and efficient searchable encryptiontypes. We take new effective cryptographic techniques based on data structures like CRSA and B-Tree to enhance the level of privacy. We propose new multi-keyword search query over encrypted cloud information in retrieving top k scored documents. The vector space model and TFIDF model are used to build index and query generation. This paper focuses on multi keyword search based on ranking over an encrypted cloud data. The search uses the feature of similarity and inner product similarity matching. We propose to support the top-k Multi-full-text search for security and performance analysis show that the proposed model guarantees a high safety and practicality and dynamic update operations, such as deleting and adding documents. The experimental results show that the overhead in computation and communication is low.

Secure Ranked Multi-Keyword Search based on Modified Blowfish Algorithm and AVL Tree in Untrusted Cloud Environment

Advancement in cloud services growing day by day motivates a huge number of data proprietors to store their valuable data in cloud servers. For privacy issue, datasets that contain sensitive information are encrypted first and then outsourced into the cloud server. In this paper, ranked multi keyword search based on AVL tree, especially for several data owners, has been proposed. To avoid unauthorized access, the proposed work encrypts the data using a Modified Blowfish (MB) algorithm before outsourcing. MB algorithm introduces # operation instead of normal OR operation in conventional blowfish algorithm. This MB algorithm provides robustness against any intruding whereas the conventional blowfish algorithm insecure for many applications. To achieve a proficient search, every data owner’s index based on AVL tree is encrypted by way of additive order and the privacy-preserving family is formed. The cloud server is then permitted to combine these indexes effectually without knowing the index content. An Iterative Deepening Depth First Search (IDDFS) procedure is used to discover the matching file for the data user request. Finally, our proposed work provides secure data outsourcing to cloud server compared to the other existing methods. The proposed work considers three metrics for the performance evaluation process, viz. precision, index construction time and search time.

Applying Multi Property Tree for Multi Keyword Rank Searching and Dynamic Update in Cloud

Various types of data structures are used for keyword searching like binary tee, KBB tree, inverted tree, inverted index, Multi Property Tree (MPT). These data structures are used for searching keywords in cloud space after getting instructions from data user. On the basis of MPT data structure the authors have introduced a search scheme called MPTsearch algorithm. Experiments show that the proposed scheme performs better than linear search. It also achieves lower time consumption and computing overhead for queries and trapdoor formation. Moreover this scheme not only fulfills the searching part but also plays vital role in dynamic update (insertion and deletion) of data provided by the data owner

Enhancement of the Homomorphism Encryption Based Mechanism by Solving the Integrity Problems Effectively on Multi Keyword Search in Cloud Computing

Cloud computing gives part of advantages to endeavors to offload their information and software services to cloud administrations by saving them lot of money that must be spent on foundation setup cost. Despite that, while offloading the information privacy and protection is a vital concern. Correspondingly for example when the emergency clinics and medicinal services association transfer their patient's subtleties to cloud and when the information is included, it will influence the protection of the patients. So privacy is a vital concern while offloading the information to cloud. Most of solutions for privacy depend on encryption and information to be offloaded is encoded and put away in cloud. Lots of encryption techniques like cryptography, symmetric key etc. methods are used for encrypting the information before uploading to cloud. However, the reaction in this encryption system is that, the encrypted information isn't structure preserving and it is not suitable for ranking or searching. To solve the complications of this searching and ranking due to disordering a homomorphism encryption based mechanism is proposed but this solution also suffers from the problem of integrity attacks on result. So this paper discussed the enhancement of the homomorphism encryption based mechanism by solving the integrity problems effectively.

Securing Cloud Data by Using Multi Keyword Search System

Securing Cloud Data by Using Multi Keyword Search System

Efficient Multi-Keyword Search Through Ciphertext Data in the Cloud

Cloud computing has become essential for storing sensitive data sets that are centralized in the cloud. The need for privacy and protection of files and documents has increased day by day. Data users typically dump the most powerful information in cloud storage to prevent third parties from accessing data in cloud storage. In legacy systems, end users safely retrieved encrypted information using keyword search. However, in existing systems, we recommend only individual keywords and Boolean keywords, which is not yet sufficient to ensure efficient data usage for a vast number of data users and the number of documents in the cloud repository. This work aims to develop a systematic approach to searching multi keywords in the cloud with ciphertext data. The cloud server carries out risk-free investigations with no clear information about keywords and trap doors. The client or user uses multiple keywords to perform data retrieval. When the client enters a question for many words, the server breaks the question into one word and retrieves the word from the index. In this task, the cipher text policy attribute-based encryption (CPABE) algorithm is used to perform encryption of files and documents. Experimental results show 95% accuracy with a data set size of 1000, for both single and multiple keyword searches. Because previous research were limited to single searches, this new work performs multiple keyword searches with unique security aspects to create a multi-keyword search system rather than the cryptographic data in the cloud.

Efficient Multi Keyword Search In Heterogeneous Environment Based On Ranking Technique

Efficient Multi Keyword Search In Heterogeneous Environment Based On Ranking Technique