Tree-Based Public Key Encryption with Conjunctive Keyword Search

Searchable public key encryption supporting conjunctive keyword search is an important technique in today’s cloud environment. Nowadays, previous schemes usually take advantage of forward index structure, which leads to a linear search complexity. In order to obtain better search efficiency, in this paper, we utilize a tree index structure instead of forward index to realize such schemes. To achieve the goal, we first give a set of keyword conversion methods that can convert the index and query keywords into a group of vectors and then present a novel algorithm for building index tree based on these vectors. Finally, by combining an efficient predicate encryption scheme to encrypt the index tree, a tree-based public key encryption with conjunctive keyword search scheme is proposed. The proposed scheme is proven to be secure against chosen plaintext attacks and achieves a sublinear search complexity. Moreover, both theoretical analysis and experimental result show that the proposed scheme is efficient and feasible for practical applications.

Se-PKSE: Secure Public-Key Searchable Encryption for Cloud-Assisted Lightweight Platforms

Since more and more data from lightweight platforms like IoT devices are being outsourced to the cloud, the need to ensure privacy while retaining data usability is important. Encrypting documents before uploading to the cloud, ensures privacy but reduces data usability. Searchable encryption, specially public-key searchable encryption (PKSE), allows secure keyword search in the cloud over encrypted documents uploaded from IoT devices. However, most existing PKSE schemes focus on returning all the files that match the queried keyword, which is not practical. To achieve a secure, practical, and efficient keyword search, we design a dynamic ranked PKSE framework over encrypted cloud data named \textit{Secure Public-Key Searchable Encryption} (Se-PKSE). We leverage a partially homomorphically encrypted index tree structure that provides sub-linear ranked search capability and allows dynamic insertion/deletion of documents without the owner storing any document details. An interactive search mechanism is introduced between the user and the cloud to eliminate trapdoors from the search request to ensure search keyword privacy and forward privacy. Finally, we implement a prototype of Se-PKSE and test it in the Amazon EC2 for practicality using the RFC dataset. The comprehensive evaluation demonstrates that Se-PKSE is efficient and secure for practical deployment.

The Tree Vandalism Model (Tvm): Quantifying urban tree vandalism status

The Tree Vandalism Model (TVM) was developed to assist decision-makers and tree managers to quantify the status of tree vandalism incidence in the urban area. The model quantifies tree vandalism incident influenced by the shortcoming of tree conditions, tree vandalism incident derived from human error and tree vandalism incident due to lack of urban tree concern; which that interpret the number of tree vandalism throughout the area; the tree vandalism composite index value throughout the area; and a tree vandalism classification. Keywords: Composite Index, Tree Vandalism, Urban Stresses, Urban Tree Care. eISSN: 2398-4287© 2021. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians/Africans/Arabians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v6iSI4.3031

Association between Green Space Structure and the Prevalence of Asthma: A Case Study of Toronto

Asthma is a chronic inflammatory disease that can be caused by various factors, such as asthma-related genes, lifestyle, and air pollution, and it can result in adverse impacts on asthmatics’ mental health and quality of life. Hence, asthma issues have been widely studied, mainly from demographic, socioeconomic, and genetic perspectives. Although it is becoming increasingly clear that asthma is likely influenced by green spaces, the underlying mechanisms are still unclear and inconsistent. Moreover, green space influences the prevalence of asthma concurrently in multiple ways, but most existing studies have explored only one pathway or a partial pathway, rather than the multi-pathways. Compared to greenness (measured by Normalized Difference Vegetation Index, tree density, etc.), green space structure—which has the potential to impact the concentration of air pollution and microbial diversity—is still less investigated in studies on the influence of green space on asthma. Given this research gap, this research took Toronto, Canada, as a case study to explore the two pathways between green space structure and the prevalence of asthma based on controlling the related covariates. Using regression analysis, it was found that green space structure can protect those aged 0–19 years from a high risk of developing asthma, and this direct protective effect can be enhanced by high tree diversity. For adults, green space structure does not influence the prevalence of asthma unless moderated by tree diversity (a measurement of the richness and diversity of trees). However, this impact was not found in adult females. Moreover, the hypothesis that green space structure influences the prevalence of asthma by reducing air pollution was not confirmed in this study, which can be attributed to a variety of causes.

e-SimNet: a visual similar product recommender system for E-commerce

<span>Visual similarity recommendations have an immense role in E-commerce portals. Fetching the appropriate similar products and suggesting to the buyers based on the product image's visual features is complex. Here in our research, we presented an efficient E-commerce similar product network model (e-SimNet) for visually similar recommendations. To achieve our objective, we have performed image feature extraction and generating embeddings using deep learning techniques and built an Index tree using the approximate nearest neighbor oh yeah (ANNOY) algorithm. Further, we have fetches top-N the near similar items using distance measure. We have benchmarked our model in terms of accuracy, error rate, and results show that better than other state-of-the-art approaches with 96.22% of accuracy.</span>

Towards Efficient Big Data Storage With MapReduce Deduplication System

In the big data era, there is a high requirement for data storage and processing. The conventional approach faces a great challenge, and de-duplication is an excellent approach to reduce the storage space and computational time. Many existing approaches take much time to pinpoint the similar data. MapReduce de-duplication system is proposed to attain high duplication ratio. MapReduce is the parallel processing approach that helps to process large number of files in less time. The proposed system uses two threshold two divisor with switch algorithm for chunking. Switch is the average parameter used by TTTD-S to minimize the chunk size variance. Hashing using SHA-3 and fractal tree indexing is used here. In fractal index tree, read and write takes place at the same time. Data size after de-duplication, de-duplication ratio, throughput, hash time, chunk time, and de-duplication time are the parameters used. The performance of the system is tested by college scorecard and ZCTA dataset. The experimental results show that the proposed system can lessen the duplicity and processing time.

Temporal association rules discovery algorithm based on improved index tree

With the rapid increase of information generated from all kinds of sources, temporal big data mining in business area has been paid more and more attention recently. A novel data mining algorithm for mining temporal association is proposed. Mining temporal association can not only provide better predictability for customer behaviour but also help organisations with better strategies and marketing decisions. To compare the proposed algorithm, two methods to mine temporal association are presented. One is improved based on a traditional mining algorithm, Apriori. The other is based on an Index-Tree. Moreover, the proposed method is extended to mine temporal association in multi-dimensional space. The experimental results show that the Index-Tree method outperforms the Apriori-modified method in all cases.

EFURMS: An Efficient Scheme for File Upload and Ranked Multi-Keyword Search over Encrypted Data in Cloud

Cloud based services provide scalable storage capacities and enormous computing capability to enterprises and individuals to support big data operations in different sectors like banking, scientific research and health care. Therefore many data owners are interested to outsource their data to cloud storage servers due to their huge advantage in data processing. However, as the banking and health records usually contain sensitive data, there are privacy concerns if the data gets leaked to un-trusted third parties in cloud storage. To protect data from leakage, the widely used technique is to encrypt the data before uploading into cloud storage servers. The traditional methods implemented by many authors consumes more time to outsource the data and searching for a document is also time consuming. Sometimes there may be chances of data leakage due to insufficient security. To resolve these issues, in the current VPSearch(VPS) scheme is implemented, which provides features like verifiability of search results and privacy preservation. With its features the current system consumes more time for file uploading and index generation, which slows down the searching process. In the existing VPS scheme time minimization to efficiently search for a particular document is a challenging task on the cloud. To resolve all the above drawbacks, we have designed an index generation scheme using a tree structure along with a search algorithm using Greedy Depth-first technique, that reduces the time for uploading files and file searching time. The newly implemented scheme minimizes the time required to form the index tree file for set of files in the document which are to be uploaded and helps in storing the files in a index tree format. These techniques result in reducing the document upload time and speeding up the process of accessing data efficiently using multi-keyword search with top-'K' value.

An Improved Energy-Aware Clustering Method for The Regional Queries in The Internet of Things

We will offer a method to improve energy efficient consumption for processing queries on the Internet of Things. We focused on an energy efficient hierarchical clustering index tree such that we can facilitate time-correlated region queries in the I.o.T (Internet of Things). We try to improve clustering and make a change on its proposed index tree. We try to do this by optimizing the query processing. We improve clustering to increase the accuracy of the Internet of Things and prevent the network from disconnecting. In the article that we have chosen, there is a heterogeneous cluster which means there exists a large data difference in the two ends of a cluster. Also, it often happens that the same information is sent to the base station by two overlapping clusters; therefore, we save energy by eliminating duplicated data.

A Study of Update Methods for BoND-Tree Index on Non-ordered Discrete Vector Data

There is an increasing demand from numerous applications such as bioinformatics and cybersecurity to efficiently process various types of queries on datasets in a multidimensional Non-ordered Discrete Data Space (NDDS). An NDDS consists of vectors with values coming from a non-ordered discrete domain for each dimension. The BoND-tree index was recently developed to efficiently process box queries on a large dataset from an NDDS on disk. The original work of the BoND-tree focused on developing the index construction and query algorithms. No work has been reported on exploring efficient and effective up- date strategies for the BoND-tree. In this paper, we study two update methods based on two different strategies for updating the index tree in an NDDS. Our study shows that using the bottom-up update method can provide improved efficiency, comparing to the traditional top-down update method, especially when the number of dimensions for a vector that need to be updated is small. On the other hand, our study also shows that the two update methods have a comparable effectiveness, which indicates that the bottom-up update method is generally more advantageous.