Using User Behavior to Measure Privacy on Online Social Networks

Abstract

Because social networks exemplify the phenomenon of homogeneity in complex networks, researchers generally believe that a user's privacy disclosure is closely related to that of the users around them, but we find that the related users studied in previous methods were not correct. That is, the analyzed user groups may have had nothing to do with the privacy disclosure of the target users. Since private information is time-sensitive, information held by users who are no longer in the same environment as the target user may no longer be true and have lost its value. For example, considering students and members of the working class, transfers to another school for further studies or job changes entail dramatic changes to most of their information. This lack of timeliness has an overarching impact on the effectiveness of social network analysis and privacy protection, but this problem has not been addressed by researchers. Therefore, we study and characterize this problem, add the user's behavior trace to solve this problem and measure the user's privacy status more accurately.

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

“…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) ...…”

Insights on Data Security Schemes and Authentication Adopted in Safeguarding Social Network

“…Li et al [24] have provided a de-anonymization strategy for the heterogeneous social network in a different piece of work-the model claims to improve the detection system by using user profile and network structure information. Li et al also presents a similar work form [25]. The investigation model assesses privacy factors associated with the behavioral attributes in social networks based on structural similarity.…”

Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey

“…Also, it helps the exposed users to customize their privacy level semiautomatically by limiting the number of manual operations. Li et al [231] suggested that private information in the SNs sites is time-sensitive, which means that information held by the users who are no longer in the same environment/place as the target user may no longer be reliable/true and have lost its value. The authors combined behavioral characteristics and structural similarity to accurately filter the user groups who hold the current private information of a target user for measuring a user's privacy status.…”