Data Mining Techniques for Privacy Preservation in Social Network Sites Using SVM

Kalunge, Vishvas V.; Deepika, S.

doi:10.1007/978-3-030-69921-5_73

“…We summarize and compare the famous AI-based G anonymization techniques used for PPGP in Table 6. [175] Technical Predicts the SI in a G using ML and suggests how to safeguard it NB, SVM, RF S Yin et al [176] Technical Strikes a balance between privacy and utility in distributing G k-means algorithm R Wang et al [177] Technical Privacy preservation of degree information in releasing G k-means algorithm R Ju et al [178] Technical Strong privacy of V in G along with higher accuracy and utility k-means algorithm R Zheng et al [179] Technical Strong privacy of V in G and fewer changes to G's structure GNN algorithm R Paul et al [180] Technical Preserves the structural properties of G in anonymization process k-means algorithm R Hoang et al [181] Technical Preserves the privacy of SN users modelled via knowledge of G k-ad algorithm R Hoang et al [182] Technical Preserves the privacy of SN users when G is subject to multiple releases CTKGA algorithm R Chen et al [183] Technical Privacy preservation of SN users when G contains outliers and categorical attributes DBSCAN clustering R Narula et al [184] Technical Privacy preservation of identity and emotion-related information in OSN data CNN algorithm R Zitouni et al [185] Technical Privacy preservation by concealing the identity in image data CNN and LSTM R Ahmed et al [186] Technical Privacy preservation by concealing the identity and other SI in images Neural Network R Matheswaran et al [187] Technical Privacy preservation of image data in retrieval and storage in clouds Watermarking R Li et al [188] Technical Both anonymity-and utility-preserving solutions for OSN data GAN Algorithm R Lu et al [189] Technical Privacy preservation by reducing the prediction accuracy of sensitive links in G VGAE and ARVGA R Li et al [190] Technical Privacy preservation using profile, graph structure, and behavioral information GCNN algorithm R Wanda et al [191] Technical Privacy preservation of vulnerable nodes in G using dynamic deep learning CNN architecture R Li et al [192] Technical Privacy preservation of users when a user's job/education-place changes with time Supervised ML R Bioglio et al [193] Technical Privacy preservation of contents in OSN platforms based on sensitivity analysis Deep NN R Hermansson et al [194] Technical Preserves better accuracy for data-mining and analytical tasks from G SVM algorithm R Kalunge et al [195] Technical Preserves better utility (path length and IL) ...…”

Section: Artificial Intelligence-based Graph Anonymization Methodsmentioning

confidence: 99%

A Comprehensive Analysis of Privacy-Preserving Solutions Developed for Online Social Networks

Majeed

¹

,

Khan

²

,

Hwang

³

2022

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Owning to the massive growth in internet connectivity, smartphone technology, and digital tools, the use of various online social networks (OSNs) has significantly increased. On the one hand, the use of OSNs enables people to share their experiences and information. On the other hand, this ever-growing use of OSNs enables adversaries to launch various privacy attacks to compromise users’ accounts as well as to steal other sensitive information via statistical matching. In general, a privacy attack is carried out by the exercise of linking personal data available on the OSN site and social graphs (or statistics) published by the OSN service providers. The problem of securing user personal information for mitigating privacy attacks in OSNs environments is a challenging research problem. Recently, many privacy-preserving solutions have been proposed to secure users’ data available over OSNs from prying eyes. However, a systematic overview of the research dynamics of OSN privacy, and findings of the latest privacy-preserving approaches from a broader perspective, remain unexplored in the current literature. Furthermore, the significance of artificial intelligence (AI) techniques in the OSN privacy area has not been highlighted by previous research. To cover this gap, we present a comprehensive analysis of the state-of-the-art solutions that have been proposed to address privacy issues in OSNs. Specifically, we classify the existing privacy-preserving solutions into two main categories: privacy-preserving graph publishing (PPGP) and privacy preservation in application-specific scenarios of the OSNs. Then, we introduce a high-level taxonomy that encompasses common as well as AI-based privacy-preserving approaches that have proposed ways to combat the privacy issues in PPGP. In line with these works, we discuss many state-of-the-art privacy-preserving solutions that have been proposed for application-specific scenarios (e.g., information diffusion, community clustering, influence analysis, friend recommendation, etc.) of OSNs. In addition, we discuss the various latest de-anonymization methods (common and AI-based) that have been developed to infer either identity or sensitive information of OSN users from the published graph. Finally, some challenges of preserving the privacy of OSNs (i.e., social graph data) from malevolent adversaries are presented, and promising avenues for future research are suggested.

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“…V. Kalunge et al in the study [5] proposed data mining techniques to maintain the privacy of persons on social networking sites using a support vector machine (SVM). In their proposed scheme, privacy protection based on anonymity is performed by sending data to cuckoo search (CS) for optimization and grouping with an SVM algorithm.…”

Section: Simple Graph Uncertain Graph Labeled Graph Bipartite Graphmentioning

confidence: 99%

Protecting the privacy of social network data using graph correction

Toroghi,

Hamidzadeh

2024

Knowl Inf Syst

0

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Today, the rapid development of online social networks, as well as low costs, easy communication, and quick access with minimal facilities have made social networks an attractive and very influential phenomenon among people. The users of these networks tend to share their sensitive and private information with friends and acquaintances. This has caused the data of these networks to become a very important source of information about users, their interests, feelings, and activities. Analyzing this information can be very useful in predicting the behavior of users in dealing with various issues. But publishing this data for data mining can violate the privacy of users. As a result, data privacy protection of social networks has become an important and attractive research topic. In this context, various algorithms have been proposed, all of which meet privacy requirements by making changes in the information as well as the graph structure. But due to high processing costs and long execution times, these algorithms are not very appropriate for anonymizing big data. In this research, we improved the speed of data anonymization by using the number factorization technique to select and delete the best edges in the graph correction stage. We also used the Chaotic Krill Herd algorithm to add edges, and considering the effect of all edges together on the structure of the graph, we selected edges and added them to the graph so that it preserved the graph's utility. The evaluation results on the real-world datasets, show the efficiency of the proposed algorithm in Comparison with the state-of-the-art methods to reduce the execution time and maintain the utility of the anonymous graph.

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Protecting the privacy of social network data using graph correction

Toroghi,

Hamidzadeh

2023

Preprint

0

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Today, the rapid development of online social networks, as well as low costs, easy communication, and quick access with minimal facilities have made social networks an attractive and very influential phenomenon among people. The users of these networks tend to share their sensitive and private information with friends and acquaintances. This has caused the data of these networks to become a very important source of information about users, their interests, feelings, and activities. Analyzing this information can be very useful in predicting the behavior of users in dealing with various issues. But publishing this data for data mining can violate the privacy of users. As a result, data privacy protection of social networks has become an important and attractive research topic. In this context, various algorithms have been proposed, all of which meet privacy requirements by making changes in the information as well as the graph structure. But due to high processing costs and long execution times, these algorithms are not very appropriate for anonymizing big data. In this research, we improved the speed of data anonymization by using the number factorization technique to select and delete the best edges in the graph correction stage. We also used the Chaotic Krill Herd algorithm to add edges, and considering the effect of all edges together on the structure of the graph, we selected edges and added them to the graph so that it preserved the graph's utility. The evaluation results on the real-world datasets, show the efficiency of the proposed algorithm in Comparison with the state-of-the-art methods to reduce the execution time and maintain the utility of the anonymous graph.

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Data Mining Techniques for Privacy Preservation in Social Network Sites Using SVM

Cited by 3 publications

References 15 publications

A Comprehensive Analysis of Privacy-Preserving Solutions Developed for Online Social Networks

A Comprehensive Analysis of Privacy-Preserving Solutions Developed for Online Social Networks

Protecting the privacy of social network data using graph correction

Protecting the privacy of social network data using graph correction

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