scholarly journals Lightweight, Secure, Similar-Document Retrieval over Encrypted Data

2021 ◽  
Vol 11 (24) ◽  
pp. 12040
Author(s):  
Mustafa A. Al Sibahee ◽  
Ayad I. Abdulsada ◽  
Zaid Ameen Abduljabbar ◽  
Junchao Ma ◽  
Vincent Omollo Nyangaresi ◽  
...  
Keyword(s):  
Limited Resource ◽  
Document Retrieval ◽  
Efficient Manner ◽  
Document Similarity ◽  
Encrypted Data ◽  
Detection Systems ◽  
Similarity Detection ◽  
Computational Performance ◽  
Paillier Cryptosystem ◽  
Application Requirements

Applications for document similarity detection are widespread in diverse communities, including institutions and corporations. However, currently available detection systems fail to take into account the private nature of material or documents that have been outsourced to remote servers. None of the existing solutions can be described as lightweight techniques that are compatible with lightweight client implementation, and this deficiency can limit the effectiveness of these systems. For instance, the discovery of similarity between two conferences or journals must maintain the privacy of the submitted papers in a lightweight manner to ensure that the security and application requirements for limited-resource devices are fulfilled. This paper considers the problem of lightweight similarity detection between document sets while preserving the privacy of the material. The proposed solution permits documents to be compared without disclosing the content to untrusted servers. The fingerprint set for each document is determined in an efficient manner, also developing an inverted index that uses the whole set of fingerprints. Before being uploaded to the untrusted server, this index is secured by the Paillier cryptosystem. This study develops a secure, yet efficient method for scalable encrypted document comparison. To evaluate the computational performance of this method, this paper carries out several comparative assessments against other major approaches.

scholarly journals A Computational Modeling for Knowledge Binding of the Unstructured Web Data

2019 ◽  
Vol 9 (2S) ◽  
pp. 418-424
Keyword(s):  
Theoretical Analysis ◽  
Computational Modeling ◽  
Computational Effort ◽  
Significant Extent ◽  
Web Data ◽  
Web Based ◽  
Matching Algorithm ◽  
Computational Performance ◽  
Application Requirements ◽  
Unstructured Information

The focus of this manuscript is laid towards extracting insightful data embedded into web-based information which is crucial for various academic and commercialized application requirements. The study thereby introduces a robust computational modeling by means of computing knowledge from collaborative web-based unstructured information. For this purpose, this design is simplified with Fuzzy based matching algorithm and also with a set of procedures which reduces the computational effort to a significant extent. The numerical theoretical analysis shows that the effectiveness of the formulated model. It also shows that the formulated concept outperforms the baseline modeling by almost 50% when computational performance is concerned.

Document ranking refinement using a Markov random field model

2012 ◽  
Vol 18 (2) ◽  
pp. 155-185 ◽  
Author(s):  
ESAÚ VILLATORO ◽  
ANTONIO JUÁREZ ◽  
MANUEL MONTES ◽  
LUIS VILLASEÑOR ◽  
L. ENRIQUE SUCAR
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Relevance Feedback ◽  
Document Retrieval ◽  
Document Similarity ◽  
Feedback Information ◽  
Markov Random ◽  
Novel Method ◽  
Cross Language ◽  
Recent Evaluation

AbstractThis paper introduces a novel ranking refinement approach based on relevance feedback for the task of document retrieval. We focus on the problem of ranking refinement since recent evaluation results from Information Retrieval (IR) systems indicate that current methods are effective retrieving most of the relevant documents for different sets of queries, but they have severe difficulties to generate a pertinent ranking of them. Motivated by these results, we propose a novel method to re-rank the list of documents returned by an IR system. The proposed method is based on a Markov Random Field (MRF) model that classifies the retrieved documents as relevant or irrelevant. The proposed MRF combines: (i) information provided by the base IR system, (ii) similarities among documents in the retrieved list, and (iii) relevance feedback information. Thus, the problem of ranking refinement is reduced to that of minimising an energy function that represents a trade-off between document relevance and inter-document similarity. Experiments were conducted using resources from four different tasks of the Cross Language Evaluation Forum (CLEF) forum as well as from one task of the Text Retrieval Conference (TREC) forum. The obtained results show the feasibility of the method for re-ranking documents in IR and also depict an improvement in mean average precision compared to a state of the art retrieval machine.

Efficient Document Similarity Detection Using Weighted Phrase Indexing

2016 ◽  
Vol 11 (5) ◽  
pp. 231-244
Author(s):  
Papias Niyigena ◽  
Zhang Zuping ◽  
Mansoor Ahmed Khuhro ◽  
Damien Hanyurwimfura
Keyword(s):  
Document Similarity ◽  
Similarity Detection

scholarly journals System on Chip (SoC) for Invisible Electrocardiography (ECG) Biometrics

Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 348
Author(s):  
Francisco de Melo ◽  
Horácio C. Neto ◽  
Hugo Plácido da Silva
Keyword(s):  
Neural Network ◽  
Embedded System ◽  
Convolutional Neural Network ◽  
Limited Resource ◽  
Security Systems ◽  
Fundamental Building Block ◽  
Computational Performance ◽  
Current State ◽  
System On A Chip ◽  
On Chip

Biometric identification systems are a fundamental building block of modern security. However, conventional biometric methods cannot easily cope with their intrinsic security liabilities, as they can be affected by environmental factors, can be easily “fooled” by artificial replicas, among other caveats. This has lead researchers to explore other modalities, in particular based on physiological signals. Electrocardiography (ECG) has seen a growing interest, and many ECG-enabled security identification devices have been proposed in recent years, as electrocardiography signals are, in particular, a very appealing solution for today’s demanding security systems—mainly due to the intrinsic aliveness detection advantages. These Electrocardiography (ECG)-enabled devices often need to meet small size, low throughput, and power constraints (e.g., battery-powered), thus needing to be both resource and energy-efficient. However, to date little attention has been given to the computational performance, in particular targeting the deployment with edge processing in limited resource devices. As such, this work proposes an implementation of an Artificial Intelligence (AI)-enabled ECG-based identification embedded system, composed of a RISC-V based System-on-a-Chip (SoC). A Binary Convolutional Neural Network (BCNN) was implemented in our SoC’s hardware accelerator that, when compared to a software implementation of a conventional, non-binarized, Convolutional Neural Network (CNN) version of our network, achieves a 176,270× speedup, arguably outperforming all the current state-of-the-art CNN-based ECG identification methods.

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