A Privacy-Preserving Scheme for Interactive Messaging Over Online Social Networks

Cai, Ying; Zhang, Shunan; Xia, Hongke; Fan, Yanfang; Zhang, Haochen

doi:10.1109/jiot.2020.2986341

Cited by 20 publications

(8 citation statements)

References 40 publications

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“…The research presented in [43] identifies privacy bounds on the data of individuals' unique mobility traces; individuals' privacy is preserved by coarsening data spatially and temporally for anonymity. A mechanism for the preservation of privacy during message transmission was introduced in [44] via message obfuscation, using the message replication and sensitive attributes replacement strategy; the authors analyzed the social behaviors of each user in order to compute their credibility for privacy preservation in OSNs. The differential privacy scheme proposed in [45] combines different series to achieve privacy of all of the graph elements; the dK-1 series holds the degree frequency, the dK-2 series holds the joint degree frequency, and the linking knowledge between the edges is stored in the dK-3 series.…”

Section: Privacy Methods In Osnsmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Privacy Methods In Osnsmentioning

confidence: 99%

“…Other different types of OSN graph-based methods [34][35][36][37][38][42][43][44] fail to achieve privacy in all of the components, i.e., nodes, edges, and attributes of nodes. In such techniques, attackers can efficiently utilize the structural information of anonymized network graphs, leading to information loss.…”

Section: Motivationmentioning

confidence: 99%

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

et al. 2021

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“…Believability is an individual's perception and there is no specific benchmarking to decide the quality of content (Cai et al, 2020;Shah et al, 2020). The same piece of information can be mentioned in diverse ways, depending upon the expertise, perception, and target user (Li, 2020) .…”

Section: Elsinoëampelina Elsinoaceae Incertaesedismentioning

confidence: 99%

Credibility Evaluation of User-generated Content using Novel Multinomial Classification Technique

Gayakwad¹,

Patil²,

Joshi³

et al. 2022

IJRITCC

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Awareness about the features of the internet, easy access to data using mobile, and affordable data facilities have caused a lot of traffic on the internet. Digitization came with a lot of opportunities and challenges as well. One of the important advantages of digitization is paperless transactions, and transparency in payment, while data privacy, fake news, and cyber-attacks are the evolving challenges. The extensive use of social media networks and e-commerce websites has caused a lot of user-generated information, misinformation, and disinformation on the Internet. The quality of information depends upon various stages (of information) like generation of information, medium of propagation, and consumption of information. Content being user-generated, information needs a quality assessment before consumption. The loss of information is also necessary to be examined by applying the machine learning approach as the volume of content is extremely huge. This research work focuses on novel multinomial classification (based on multinoulli distribution) techniques to determine the quality of the information in the given content. To evaluate the information content a single algorithm with some processing is not sufficient and various approaches are necessary to evaluate the quality of content. We propose a novel approach to calculate the bias, for which the Machine Learning model will be fitted appropriately to classify the content correctly. As an empirical study, rotten tomatoes’ movie review data set is used to apply the classification techniques. The accuracy of the system is evaluated using the ROC curve, confusion matrix, and MAP.

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“…By controlling the set of user attributes and the relationship among users to hide sensitive information, the proposed method resists against the set inference attack in the process of data publishing in social networks. Cai et al [38] proposed a privacy-preserving scheme for interactive messaging by leveraging user credibility and social behaviors, which guarantees the privacy protection in the process of information exchange through information confusion and sensitive attribute substitution. In order to solve the trust difficulties, Sharma et al [39] proposed a novel solution in the form of fission computing.…”

Section: Related Workmentioning

confidence: 99%

A Privacy-Preserving Personalized Service Framework through Bayesian Game in Social IoT

Chen

et al. 2020

Wireless Communications and Mobile Computing

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It is enormously challenging to achieve a satisfactory balance between quality of service (QoS) and users’ privacy protection along with measuring privacy disclosure in social Internet of Things (IoT). We propose a privacy-preserving personalized service framework (Persian) based on static Bayesian game to provide privacy protection according to users’ individual security requirements in social IoT. Our approach quantifies users’ individual privacy preferences and uses fuzzy uncertainty reasoning to classify users. These classification results facilitate trustworthy cloud service providers (CSPs) in providing users with corresponding levels of services. Furthermore, the CSP makes a strategic choice with the goal of maximizing reputation through playing a decision-making game with potential adversaries. Our approach uses Shannon information entropy to measure the degree of privacy disclosure according to the probability of game mixed strategy equilibrium. Experimental results show that Persian guarantees QoS and effectively protects user privacy despite the existence of adversaries.

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A Privacy-Preserving Scheme for Interactive Messaging Over Online Social Networks

Cited by 20 publications

References 40 publications

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

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

Credibility Evaluation of User-generated Content using Novel Multinomial Classification Technique

A Privacy-Preserving Personalized Service Framework through Bayesian Game in Social IoT

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