scholarly journals A Multibranch Search Tree-Based Multi-Keyword Ranked Search Scheme over Encrypted Cloud Data

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Hua Dai ◽  
Xuelong Dai ◽  
Xiao Li ◽  
Xun Yi ◽  
Fu Xiao ◽  
...  
Keyword(s):  
Large Scale ◽  
Personal Data ◽  
Cloud Service ◽  
Search Tree ◽  
The Novel ◽  
Privacy Concerns ◽  
Cloud Data ◽  
Document Cluster ◽  
Ranked Search ◽  
Index Tree

In the interest of privacy concerns, cloud service users choose to encrypt their personal data before outsourcing them to cloud. However, it is difficult to achieve efficient search over encrypted cloud data. Therefore, how to design an efficient and accurate search scheme over large-scale encrypted cloud data is a challenge. In this paper, we integrate bisecting k-means algorithm and multibranch tree structure and propose the α-filtering tree search scheme based on bisecting k-means clusters. The novel index tree is built from bottom-up, and a greedy depth first algorithm is used for filtering the nonrelevant document cluster by calculating the relevance score between the filtering vector and the query vector. The α-filtering tree can improve the efficiency without the loss of search accuracy. The experiment on a real-world dataset demonstrates the effectiveness of our scheme.

scholarly journals EMSRSE: Efficient Multi-Keyword Synonym Based Ranked Search Technique Over Outsourced Encrypted Cloud Data

2018 ◽  
Vol 7 (4.36) ◽  
pp. 736
Author(s):  
Veerraju Gampala ◽  
Sreelatha Malempati
Keyword(s):  
Data Privacy ◽  
Cloud Service ◽  
Search Tree ◽  
Maintenance Cost ◽  
Data Outsourcing ◽  
Sensitive Data ◽  
Cloud Data ◽  
Insertion And Deletion ◽  
Ease Of Access ◽  
Result Analysis

Recently, searching over encrypted cloud-data outsourcing has attracted the current researcher. Using cloud computing (CC), individuals and organizations are motivated to outsource their private and sensitive data onto the cloud service provider (CSP) due to less maintenance cost, great flexibility, and ease of access.  However, the data should be encrypted using encryption techniques such as DES and AES before uploading to the CSP in order to provide data privacy and protection, which obsolete plaintext searching techniques over encrypted cloud data. Thus, this article proposes an efficient multi-keyword synonym-based ranked searching technique over encrypted cloud data (EMSRSE), which supports dynamic insertion and deletion of documents. The main objectives of EMSRSE are 1. To build an index search tree in order to store encrypted index vectors of documents and 2. To achieve better searching efficiency, a searching technique over the encrypted index tree is proposed. An extensive research and empirical result analysis show that the proposed EMSRSE scheme achieves better efficiency in comparison with other existing methods.  

scholarly journals GLOVE: Towards Privacy-Preserving Publishing of Record-Level-Truthful Mobile Phone Trajectories

2021 ◽  
Vol 2 (3) ◽  
pp. 1-36
Author(s):  
Marco Gramaglia ◽  
Marco Fiore ◽  
Angelo Furno ◽  
Razvan Stanica
Keyword(s):  
Mobile Phone ◽  
Large Scale ◽  
Real Life ◽  
Personal Data ◽  
Privacy Preserving ◽  
Proof Of Concept ◽  
Privacy Concerns ◽  
Large Populations ◽  
Real World Datasets ◽  
Privacy Risks

Datasets of mobile phone trajectories collected by network operators offer an unprecedented opportunity to discover new knowledge from the activity of large populations of millions. However, publishing such trajectories also raises significant privacy concerns, as they contain personal data in the form of individual movement patterns. Privacy risks induce network operators to enforce restrictive confidential agreements in the rare occasions when they grant access to collected trajectories, whereas a less involved circulation of these data would fuel research and enable reproducibility in many disciplines. In this work, we contribute a building block toward the design of privacy-preserving datasets of mobile phone trajectories that are truthful at the record level. We present GLOVE, an algorithm that implements k -anonymity, hence solving the crucial unicity problem that affects this type of data while ensuring that the anonymized trajectories correspond to real-life users. GLOVE builds on original insights about the root causes behind the undesirable unicity of mobile phone trajectories, and leverages generalization and suppression to remove them. Proof-of-concept validations with large-scale real-world datasets demonstrate that the approach adopted by GLOVE allows preserving a substantial level of accuracy in the data, higher than that granted by previous methodologies.

Control Cloud Data Access Privilge Anonymity with Attributed Based Encryption

2017 ◽  
Vol 7 (8) ◽  
pp. 279
Author(s):  
P. Sudheer ◽  
T. Lakshmi Surekha
Keyword(s):  
Data Privacy ◽  
Low Cost ◽  
Data Access ◽  
Privacy Concerns ◽  
Cloud Data ◽  
Computing Paradigm ◽  
Attribute Based Encryption ◽  
Data Content ◽  
Identity Privacy ◽  
Cloud Servers

Cloud computing is a revolutionary computing paradigm, which enables flexible, on-demand, and low-cost usage of computing resources, but the data is outsourced to some cloud servers, and various privacy concerns emerge from it. Various schemes based on the attribute-based encryption have been to secure the cloud storage. Data content privacy. A semi anonymous privilege control scheme AnonyControl to address not only the data privacy. But also the user identity privacy. AnonyControl decentralizes the central authority to limit the identity leakage and thus achieves semi anonymity. The  Anonymity –F which fully prevent the identity leakage and achieve the full anonymity.

A Group Collaboratable Proof of Retrievability Scheme for Cloud Data Storage

2014 ◽  
Vol 13 (7) ◽  
pp. 4625-4632
Author(s):  
Jyh-Shyan Lin ◽  
Kuo-Hsiung Liao ◽  
Chao-Hsing Hsu
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Cloud Service ◽  
Third Party ◽  
Group Collaboration ◽  
Cloud Data ◽  
Cloud Data Storage ◽  
Important Trend ◽  
Data Update ◽  
Proof Of Retrievability

Cloud computing and cloud data storage have become important applications on the Internet. An important trend in cloud computing and cloud data storage is group collaboration since it is a great inducement for an entity to use a cloud service, especially for an international enterprise. In this paper we propose a cloud data storage scheme with some protocols to support group collaboration. A group of users can operate on a set of data collaboratively with dynamic data update supported. Every member of the group can access, update and verify the data independently. The verification can also be authorized to a third-party auditor for convenience.

Covalent Atomic Bridges Enable Unidirectional Enhancement of Electronic Transport in Aligned Carbon Nanotubes

2019 ◽  
Author(s):  
Mingguang Chen ◽  
Wangxiang Li ◽  
Anshuman Kumar ◽  
Guanghui Li ◽  
Mikhail Itkis ◽  
...  
Keyword(s):  
Carbon Nanotubes ◽  
Electrical Transport ◽  
Large Scale ◽  
The Novel ◽  
Electrical Measurements ◽  
Metal Atoms ◽  
Aligned Carbon Nanotubes ◽  
Transport Channels ◽  
Walled Carbon Nanotubes ◽  
Bulk Structures

<p>Interconnecting the surfaces of nanomaterials without compromising their outstanding mechanical, thermal, and electronic properties is critical in the design of advanced bulk structures that still preserve the novel properties of their nanoscale constituents. As such, bridging the p-conjugated carbon surfaces of single-walled carbon nanotubes (SWNTs) has special implications in next-generation electronics. This study presents a rational path towards improvement of the electrical transport in aligned semiconducting SWNT films by deposition of metal atoms. The formation of conducting Cr-mediated pathways between the parallel SWNTs increases the transverse (intertube) conductance, while having negligible effect on the parallel (intratube) transport. In contrast, doping with Li has a predominant effect on the intratube electrical transport of aligned SWNT films. Large-scale first-principles calculations of electrical transport on aligned SWNTs show good agreement with the experimental electrical measurements and provide insight into the changes that different metal atoms exert on the density of states near the Fermi level of the SWNTs and the formation of transport channels. </p>

scholarly journals IoT Traffic: Modeling and Measurement Experiments

IoT ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 140-162
Author(s):  
Hung Nguyen-An ◽  
Thomas Silverston ◽  
Taku Yamazaki ◽  
Takumi Miyoshi
Keyword(s):  
Smart Home ◽  
Large Scale ◽  
Traffic Modeling ◽  
The Novel ◽  
Network Behavior ◽  
Performance Accuracy ◽  
Traffic Generator ◽  
Multiple Devices ◽  
Iot Devices ◽  
The Internet Of Things

We now use the Internet of things (IoT) in our everyday lives. The novel IoT devices collect cyber–physical data and provide information on the environment. Hence, IoT traffic will count for a major part of Internet traffic; however, its impact on the network is still widely unknown. IoT devices are prone to cyberattacks because of constrained resources or misconfigurations. It is essential to characterize IoT traffic and identify each device to monitor the IoT network and discriminate among legitimate and anomalous IoT traffic. In this study, we deployed a smart-home testbed comprising several IoT devices to study IoT traffic. We performed extensive measurement experiments using a novel IoT traffic generator tool called IoTTGen. This tool can generate traffic from multiple devices, emulating large-scale scenarios with different devices under different network conditions. We analyzed the IoT traffic properties by computing the entropy value of traffic parameters and visually observing the traffic on behavior shape graphs. We propose a new method for identifying traffic entropy-based devices, computing the entropy values of traffic features. The method relies on machine learning to classify the traffic. The proposed method succeeded in identifying devices with a performance accuracy up to 94% and is robust with unpredictable network behavior with traffic anomalies spreading in the network.

scholarly journals Multi-Objective Optimization of Integrated Civilian-Military Scheduling of Medical Supplies for Epidemic Prevention and Control

Healthcare ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 126
Author(s):  
Hai-Feng Ling ◽  
Zheng-Lian Su ◽  
Xun-Lin Jiang ◽  
Yu-Jun Zheng
Keyword(s):  
Prevention And Control ◽  
Large Scale ◽  
Medical Services ◽  
The Novel ◽  
Multi Objective ◽  
Medical Supplies ◽  
Problem Instances ◽  
Novel Coronavirus ◽  
And Control ◽  
Minimum Ratio

In a large-scale epidemic, such as the novel coronavirus pneumonia (COVID-19), there is huge demand for a variety of medical supplies, such as medical masks, ventilators, and sickbeds. Resources from civilian medical services are often not sufficient for fully satisfying all of these demands. Resources from military medical services, which are normally reserved for military use, can be an effective supplement to these demands. In this paper, we formulate a problem of integrated civilian-military scheduling of medical supplies for epidemic prevention and control, the aim of which is to simultaneously maximize the overall satisfaction rate of the medical supplies and minimize the total scheduling cost, while keeping a minimum ratio of medical supplies reservation for military use. We propose a multi-objective water wave optimization (WWO) algorithm in order to efficiently solve this problem. Computational results on a set of problem instances constructed based on real COVID-19 data demonstrate the effectiveness of the proposed method.

scholarly journals Comparative genomics suggests a taxonomic revision of the Staphylococcus cohnii species complex

2021 ◽  
Author(s):  
Anna Lavecchia ◽  
Matteo Chiara ◽  
Caterina De Virgilio ◽  
Caterina Manzari ◽  
Carlo Pazzani ◽  
...  
Keyword(s):  
Comparative Genomics ◽  
Species Complex ◽  
Large Scale ◽  
Genomic Sequence ◽  
Bacterial Species ◽  
Taxonomic Revision ◽  
Distinct Species ◽  
The Novel ◽  
Staphylococcus Cohnii ◽  
Taxonomic Assignments

Abstract Staphylococcus cohnii (SC), a coagulase-negative bacterium, was first isolated in 1975 from human skin. Early phenotypic analyses led to the delineation of two subspecies (subsp.), Staphylococcus cohnii subsp. cohnii (SCC) and Staphylococcus cohnii subsp. urealyticus (SCU). SCC was considered to be specific to humans whereas SCU apparently demonstrated a wider host range, from lower primates to humans. The type strains ATCC 29974 and ATCC 49330 have been designated for SCC and SCU, respectively. Comparative analysis of 66 complete genome sequences—including a novel SC isolate—revealed unexpected patterns within the SC complex, both in terms of genomic sequence identity and gene content, highlighting the presence of 3 phylogenetically distinct groups. Based on our observations, and on the current guidelines for taxonomic classification for bacterial species, we propose a revision of the SC species complex. We suggest that SCC and SCU should be regarded as two distinct species: SC and SU (Staphylococcus urealyticus), and that two distinct subspecies, SCC and SCB (SC subsp. barensis, represented by the novel strain isolated in Bari) should be recognized within SC. Furthermore, since large scale comparative genomics studies recurrently suggest inconsistencies or conflicts in taxonomic assignments of bacterial species, we believe that the approach proposed here might be considered for more general application.

A multi-objective sparse evolutionary framework for large-scale weapon target assignment based on a reward strategy

2021 ◽  
Vol 40 (5) ◽  
pp. 10043-10061
Author(s):  
Xiaoping Shi ◽  
Shiqi Zou ◽  
Shenmin Song ◽  
Rui Guo
Keyword(s):  
Large Scale ◽  
The Novel ◽  
Genetic Operators ◽  
Multi Objective ◽  
Target Assignment ◽  
Solution Selection ◽  
Battlefield Environment ◽  
Sparse Problems ◽  
Novel Model ◽  
Error Parameter

 The asset-based weapon target assignment (ABWTA) problem is one of the important branches of the weapon target assignment (WTA) problem. Due to the current large-scale battlefield environment, the ABWTA problem is a multi-objective optimization problem (MOP) with strong constraints, large-scale and sparse properties. The novel model of the ABWTA problem with the operation error parameter is established. An evolutionary algorithm for large-scale sparse problems (SparseEA) is introduced as the main framework for solving large-scale sparse ABWTA problem. The proposed framework (SparseEA-ABWTA) mainly addresses the issue that problem-specific initialization method and genetic operators with a reward strategy can generate solutions efficiently considering the sparsity of variables and an improved non-dominated solution selection method is presented to handle the constraints. Under the premise of constructing large-scale cases by the specific case generator, two numerical experiments on four outstanding multi-objective evolutionary algorithms (MOEAs) show Runtime of SparseEA-ABWTA is faster nearly 50% than others under the same convergence and the gap between MOEAs improved by the mechanism of SparseEA-ABWTA and SparseEA-ABWTA is reduced to nearly 20% in the convergence and distribution.

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