scholarly journals Privacy Preservation in Online Social Networks Using Multiple-Graph-Properties-Based Clustering to Ensure k-Anonymity, l-Diversity, and t-Closeness

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2877
Author(s):  
Rupali Gangarde ◽  
Amit Sharma ◽  
Ambika Pawar ◽  
Rahul Joshi ◽  
Sudhanshu Gonge
Keyword(s):  
Online Social Networks ◽  
Privacy Preservation ◽  
State Of The Art ◽  
Online Social Network ◽  
Sensitive Information ◽  
Privacy Requirements ◽  
Graph Properties ◽  
Art Methods ◽  
Multiple Graph ◽  
High Level

As per recent progress, online social network (OSN) users have grown tremendously worldwide, especially in the wake of the COVID-19 pandemic. Today, OSNs have become a core part of many people’s daily lifestyles. Therefore, increasing dependency on OSNs encourages privacy requirements to protect users from malicious sources. OSNs contain sensitive information about each end user that intruders may try to leak for commercial or non-commercial purposes. Therefore, ensuring different levels of privacy is a vital requirement for OSNs. Various privacy preservation methods have been introduced recently at the user and network levels, but ensuring k-anonymity and higher privacy model requirements such as l-diversity and t-closeness in OSNs is still a research challenge. This study proposes a novel method that effectively anonymizes OSNs using multiple-graph-properties-based clustering. The clustering method introduces the goal of achieving privacy of edge, node, and user attributes in the OSN graph. This clustering approach proposes to ensure k-anonymity, l-diversity, and t-closeness in each cluster of the proposed model. We first design the data normalization algorithm to preprocess and enhance the quality of raw OSN data. Then, we divide the OSN data into different clusters using multiple graph properties to satisfy the k-anonymization. Furthermore, the clusters ensure improved k-anonymization by a novel one-pass anonymization algorithm to address l-diversity and t-closeness privacy requirements. We evaluate the performance of the proposed method with state-of-the-art methods using a “Yelp real-world dataset”. The proposed method ensures high-level privacy preservation compared to state-of-the-art methods using privacy metrics such as anonymization degree, information loss, and execution time.

Privacy in Online Social Networks

2021 ◽  
pp. 2078-2109
Author(s):  
Ramanpreet Kaur ◽  
Tomaž Klobučar ◽  
Dušan Gabrijelčič
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Online Social Networks ◽  
Privacy Protection ◽  
Privacy Preservation ◽  
Mitigation Strategies ◽  
Research Issues ◽  
Privacy Requirements ◽  
Privacy Threats

This chapter is concerned with the identification of the privacy threats to provide a feedback to the users so that they can make an informed decision based on their desired level of privacy. To achieve this goal, Solove's taxonomy of privacy violations is refined to incorporate the modern challenges to the privacy posed by the evolution of social networks. This work emphasizes on the fact that the privacy protection should be a joint effort of social network owners and users, and provides a classification of mitigation strategies according to the party responsible for taking these countermeasures. In addition, it highlights the key research issues to guide the research in the field of privacy preservation. This chapter can serve as a first step to comprehend the privacy requirements of online users and educate the users about their choices and actions in social media.

Content-Based Classification of Sensitive Tweets

2017 ◽  
Vol 11 (04) ◽  
pp. 541-562 ◽  
Author(s):  
Qiaozhi Wang ◽  
Jaisneet Bhandal ◽  
Shu Huang ◽  
Bo Luo
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Domain Knowledge ◽  
Online Social Network ◽  
Family Relationship ◽  
Semantic Content ◽  
Sensitive Information ◽  
Health Politics ◽  
The Public ◽  
Distribution Features

Online Social Networks (OSNs), such as Facebook and Twitter, provide open platforms for users to easily share their statuses, opinions, and ideas, ranging from personal experiences/activities to breaking news. With the increasing popularity of online social networks and the explosion of blog and microblog messages, we have observed large amounts of potentially sensitive or private messages being published to OSNs inadvertently or voluntarily. The owners of these messages may become vulnerable to online stalkers or adversaries, especially considering that many online social network platforms (e.g. Twitter) provide open access to the public, including unregistered users and search engine bots. Studies show that users often regret posting sensitive or private messages. However, it is very difficult to completely erase such messages from the Internet, especially when the messages have been indexed by the search engines or forwarded (e.g. re-tweet in Twitter) by other users. Therefore, it is critical to identify messages that reveal private/sensitive information, and warn users before they post the messages to the public. However, the definition of sensitive information is subjective and different from user to user. For example, some users may feel comfortable sharing political opinions, while others do not. To develop a privacy protection mechanism that is customizable to fit the needs of diverse audiences, it is essential to accurately and automatically classify potentially sensitive messages into topic categories, such as health, politics, family, relationship, religion, etc. In this paper, we make the first attempt to classify sensitive tweets into 13 pre-defined topic categories. In particular, we model the semantic content of tweets with term distribution features as well as users’ topic-preferences based on personal tweet history. We also add domain-specific features, i.e. domain knowledge, to improve classification performance. Experiments show that our method can boost classification accuracy compared with the well-known Bag-of-Words and TF-IDF methods. a

Privacy in Online Social Networks

2021 ◽  
pp. 567-598
Author(s):  
Ramanpreet Kaur ◽  
Tomaž Klobučar ◽  
Dušan Gabrijelčič
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Online Social Networks ◽  
Privacy Protection ◽  
Privacy Preservation ◽  
Mitigation Strategies ◽  
Research Issues ◽  
Privacy Requirements ◽  
Privacy Threats

This chapter is concerned with the identification of the privacy threats to provide a feedback to the users so that they can make an informed decision based on their desired level of privacy. To achieve this goal, Solove's taxonomy of privacy violations is refined to incorporate the modern challenges to the privacy posed by the evolution of social networks. This work emphasizes on the fact that the privacy protection should be a joint effort of social network owners and users, and provides a classification of mitigation strategies according to the party responsible for taking these countermeasures. In addition, it highlights the key research issues to guide the research in the field of privacy preservation. This chapter can serve as a first step to comprehend the privacy requirements of online users and educate the users about their choices and actions in social media.

scholarly journals Vulnerabilities to Online Social Network Identity Deception Detection Research and Recommendations for Mitigation

2020 ◽  
Vol 12 (9) ◽  
pp. 148 ◽  
Author(s):  
Max Ismailov ◽  
Michail Tsikerdekis ◽  
Sherali Zeadally
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Real World ◽  
Online Social Networks ◽  
Laboratory Experiments ◽  
Deception Detection ◽  
State Of The Art ◽  
Online Social Network ◽  
Ongoing Research ◽  
The Real

Identity deception in online social networks is a pervasive problem. Ongoing research is developing methods for identity deception detection. However, the real-world efficacy of these methods is currently unknown because they have been evaluated largely through laboratory experiments. We present a review of representative state-of-the-art results on identity deception detection. Based on this analysis, we identify common methodological weaknesses for these approaches, and we propose recommendations that can increase their effectiveness for when they are applied in real-world environments.

scholarly journals Multi-hop assortativities for network classification

2018 ◽  
Vol 7 (4) ◽  
pp. 603-622 ◽  
Author(s):  
Leonardo Gutiérrez-Gómez ◽  
Jean-Charles Delvenne
Keyword(s):  
Machine Learning ◽  
Scientific Collaboration ◽  
State Of The Art ◽  
Medical Engineering ◽  
Research Field ◽  
Classification Task ◽  
Collaboration Network ◽  
Structural Patterns ◽  
Art Methods

Abstract Several social, medical, engineering and biological challenges rely on discovering the functionality of networks from their structure and node metadata, when it is available. For example, in chemoinformatics one might want to detect whether a molecule is toxic based on structure and atomic types, or discover the research field of a scientific collaboration network. Existing techniques rely on counting or measuring structural patterns that are known to show large variations from network to network, such as the number of triangles, or the assortativity of node metadata. We introduce the concept of multi-hop assortativity, that captures the similarity of the nodes situated at the extremities of a randomly selected path of a given length. We show that multi-hop assortativity unifies various existing concepts and offers a versatile family of ‘fingerprints’ to characterize networks. These fingerprints allow in turn to recover the functionalities of a network, with the help of the machine learning toolbox. Our method is evaluated empirically on established social and chemoinformatic network benchmarks. Results reveal that our assortativity based features are competitive providing highly accurate results often outperforming state of the art methods for the network classification task.

scholarly journals Efficient End-to-End Sentence-Level Lipreading with Temporal Convolutional Networks

2021 ◽  
Vol 11 (15) ◽  
pp. 6975
Author(s):  
Tao Zhang ◽  
Lun He ◽  
Xudong Li ◽  
Guoqing Feng
Keyword(s):  
Performance Improvement ◽  
State Of The Art ◽  
Error Rates ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Sentence Level ◽  
End To End ◽  
High Level ◽  
Improved Accuracy ◽  
Talking Face

Lipreading aims to recognize sentences being spoken by a talking face. In recent years, the lipreading method has achieved a high level of accuracy on large datasets and made breakthrough progress. However, lipreading is still far from being solved, and existing methods tend to have high error rates on the wild data and have the defects of disappearing training gradient and slow convergence. To overcome these problems, we proposed an efficient end-to-end sentence-level lipreading model, using an encoder based on a 3D convolutional network, ResNet50, Temporal Convolutional Network (TCN), and a CTC objective function as the decoder. More importantly, the proposed architecture incorporates TCN as a feature learner to decode feature. It can partly eliminate the defects of RNN (LSTM, GRU) gradient disappearance and insufficient performance, and this yields notable performance improvement as well as faster convergence. Experiments show that the training and convergence speed are 50% faster than the state-of-the-art method, and improved accuracy by 2.4% on the GRID dataset.

scholarly journals Automatic Detection of Discrimination Actions from Social Images

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 325
Author(s):  
Zhihao Wu ◽  
Baopeng Zhang ◽  
Tianchen Zhou ◽  
Yan Li ◽  
Jianping Fan
Keyword(s):  
Action Recognition ◽  
State Of The Art ◽  
Automatic Detection ◽  
Experimental Results ◽  
Practical Approach ◽  
Detection And Identification ◽  
Art Methods ◽  
Image Set ◽  
Social Images ◽  
Relationship Identification

In this paper, we developed a practical approach for automatic detection of discrimination actions from social images. Firstly, an image set is established, in which various discrimination actions and relations are manually labeled. To the best of our knowledge, this is the first work to create a dataset for discrimination action recognition and relationship identification. Secondly, a practical approach is developed to achieve automatic detection and identification of discrimination actions and relationships from social images. Thirdly, the task of relationship identification is seamlessly integrated with the task of discrimination action recognition into one single network called the Co-operative Visual Translation Embedding++ network (CVTransE++). We also compared our proposed method with numerous state-of-the-art methods, and our experimental results demonstrated that our proposed methods can significantly outperform state-of-the-art approaches.

scholarly journals A Deep Learning Approach to Predict Autism Spectrum Disorder Using Multisite Resting-State fMRI

2021 ◽  
Vol 11 (8) ◽  
pp. 3636
Author(s):  
Faria Zarin Subah ◽  
Kaushik Deb ◽  
Pranab Kumar Dhar ◽  
Takeshi Koshiba
Keyword(s):  
Autism Spectrum Disorder ◽  
Resting State ◽  
State Of The Art ◽  
Resting State Fmri ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Bootstrap Analysis ◽  
Proposed Model ◽  
Art Methods ◽  
The Mean

Autism spectrum disorder (ASD) is a complex and degenerative neuro-developmental disorder. Most of the existing methods utilize functional magnetic resonance imaging (fMRI) to detect ASD with a very limited dataset which provides high accuracy but results in poor generalization. To overcome this limitation and to enhance the performance of the automated autism diagnosis model, in this paper, we propose an ASD detection model using functional connectivity features of resting-state fMRI data. Our proposed model utilizes two commonly used brain atlases, Craddock 200 (CC200) and Automated Anatomical Labelling (AAL), and two rarely used atlases Bootstrap Analysis of Stable Clusters (BASC) and Power. A deep neural network (DNN) classifier is used to perform the classification task. Simulation results indicate that the proposed model outperforms state-of-the-art methods in terms of accuracy. The mean accuracy of the proposed model was 88%, whereas the mean accuracy of the state-of-the-art methods ranged from 67% to 85%. The sensitivity, F1-score, and area under receiver operating characteristic curve (AUC) score of the proposed model were 90%, 87%, and 96%, respectively. Comparative analysis on various scoring strategies show the superiority of BASC atlas over other aforementioned atlases in classifying ASD and control.

scholarly journals A contour property based approach to segment nuclei in cervical cytology images

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Iram Tazim Hoque ◽  
Nabil Ibtehaz ◽  
Saumitra Chakravarty ◽  
M. Saifur Rahman ◽  
M. Sohel Rahman
Keyword(s):  
Pap Smear ◽  
State Of The Art ◽  
Cervical Cytology ◽  
Average Intensity ◽  
Nucleus Size ◽  
Real Dataset ◽  
Art Methods ◽  
Convolution Filter ◽  
Cervical Cells ◽  
Nucleus Segmentation

Abstract Background Segmentation of nuclei in cervical cytology pap smear images is a crucial stage in automated cervical cancer screening. The task itself is challenging due to the presence of cervical cells with spurious edges, overlapping cells, neutrophils, and artifacts. Methods After the initial preprocessing steps of adaptive thresholding, in our approach, the image passes through a convolution filter to filter out some noise. Then, contours from the resultant image are filtered by their distinctive contour properties followed by a nucleus size recovery procedure based on contour average intensity value. Results We evaluate our method on a public (benchmark) dataset collected from ISBI and also a private real dataset. The results show that our algorithm outperforms other state-of-the-art methods in nucleus segmentation on the ISBI dataset with a precision of 0.978 and recall of 0.933. A promising precision of 0.770 and a formidable recall of 0.886 on the private real dataset indicate that our algorithm can effectively detect and segment nuclei on real cervical cytology images. Tuning various parameters, the precision could be increased to as high as 0.949 with an acceptable decrease of recall to 0.759. Our method also managed an Aggregated Jaccard Index of 0.681 outperforming other state-of-the-art methods on the real dataset. Conclusion We have proposed a contour property-based approach for segmentation of nuclei. Our algorithm has several tunable parameters and is flexible enough to adapt to real practical scenarios and requirements.

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