Dynamic social privacy protection based on graph mode partition in complex social network

Gu, Qiong; Qilian, Ni; Meng, Xin; Zhijiao, Yang

doi:10.1007/s00779-019-01249-6

Cited by 10 publications

(3 citation statements)

References 29 publications

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“…This method can protect the system from internal attacks. Therefore, the data stored on the server is very secure [20,21].…”

Section: Table 1 Complexity Analysis Methods 1 Methods 2 Methodsmentioning

confidence: 99%

A Consumer Data Privacy Protection Model Based on Non-Parametric Statistics for Dynamic Data Publishing in e-Commerce Platforms

Jia

2024

HighTech. Innov. J.

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Objectives: Consumer data privacy on e-commerce platforms is increasingly crucial. This study aims to investigate privacy protection mechanisms, particularly focusing on personal and corporate secrets. It seeks to understand individual perspectives on privacy and preferences for data disclosure. The primary objective is to explore methods for safeguarding personal information while maintaining data integrity. Methods/Analysis: We employ non-parametric statistical techniques to analyze consumer behavior and preferences on e-commerce platforms. This involves examining patterns of data disclosure and identifying sensitive information shared by users. By studying communication dynamics and recording practices, we assess the efficacy of current privacy protection measures. Novelty/Improvement: This study contributes to the understanding of consumer privacy protection by emphasizing the importance of non-parametric statistical methods in e-commerce research. Our findings underscore the need for enhanced privacy measures. We advocate for further research and development of innovative privacy-enhancing technologies to address evolving privacy challenges in online commerce. Findings:Our research highlights the significance of personal privacy concerns in e-commerce settings. We identify a spectrum of privacy attitudes among users, ranging from strict confidentiality to selective disclosure. Furthermore, our analysis reveals potential vulnerabilities in current privacy safeguards, particularly regarding the collection and storage of sensitive data on e-commerce platforms. Doi: 10.28991/HIJ-2024-05-02-013 Full Text: PDF

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“…This method can protect the system from internal attacks. Therefore, the data stored on the server is very secure [20,21].…”

Section: Table 1 Complexity Analysis Methods 1 Methods 2 Methodsmentioning

confidence: 99%

A Consumer Data Privacy Protection Model Based on Non-Parametric Statistics for Dynamic Data Publishing in e-Commerce Platforms

Jia

2024

HighTech. Innov. J.

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“…Social data should be properly anonymized to maintain the user's data privacy before publishing. The adversary uses a range of background information about the target individual to infer its private information [15]. Background knowledge is referred to as information an adversary uses to perform any attack on a published dataset [16].…”

Section: Related Workmentioning

confidence: 99%

k-NDDP: An Efficient Anonymization Model for Social Network Data Release

et al. 2021

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With the evolution of Internet technology, social networking sites have gained a lot of popularity. People make new friends, share their interests, experiences in life, etc. With these activities on social sites, people generate a vast amount of data that is analyzed by third parties for various purposes. As such, publishing social data without protecting an individual’s private or confidential information can be dangerous. To provide privacy protection, this paper proposes a new degree anonymization approach k-NDDP, which extends the concept of k-anonymity and differential privacy based on Node DP for vertex degrees. In particular, this paper considers identity disclosures on social data. If the adversary efficiently obtains background knowledge about the victim’s degree and neighbor connections, it can re-identify its victim from the social data even if the user’s identity is removed. The contribution of this paper is twofold. First, a simple and, at the same time, effective method k–NDDP is proposed. The method is the extension of k-NMF, i.e., the state-of-the-art method to protect against mutual friend attack, to defend against identity disclosures by adding noise to the social data. Second, the achieved privacy using the concept of differential privacy is evaluated. An extensive empirical study shows that for different values of k, the divergence produced by k-NDDP for CC, BW and APL is not more than 0.8%, also added dummy links are 60% less, as compared to k-NMF approach, thereby it validates that the proposed k-NDDP approach provides strong privacy while maintaining the usefulness of data.

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“…For instance, [19] developed the user-tailored privacy by design framework, aiming to address the types of privacy adaptations that should be implemented, in order for Facebook users with different privacy management strategies to be supported more personalized. However, as other previous ambitious self-adaptive privacy schemes for SNs [20,21] that have been proposed, their work does not identify users' social categories and attributes in depth. These attributes affect their privacy norms, which is of great importance for the developers and the design of adequate privacy solutions [2], in order for instance to support users' authentication, authorization and confidentiality of personal information, which considered being of the most significant privacy challenges in Internet of Things environments [22].…”

Section: Introductionmentioning

confidence: 99%

Identifying Privacy Related Requirements for the Design of Self-Adaptive Privacy Protections Schemes in Social Networks

et al. 2021

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Social Networks (SNs) bring new types of privacy risks threats for users; which developers should be aware of when designing respective services. Aiming at safeguarding users’ privacy more effectively within SNs, self-adaptive privacy preserving schemes have been developed, considered the importance of users’ social and technological context and specific privacy criteria that should be satisfied. However, under the current self-adaptive privacy approaches, the examination of users’ social landscape interrelated with their privacy perceptions and practices, is not thoroughly considered, especially as far as users’ social attributes concern. This study, aimed at elaborating this examination in depth, in order as to identify the users’ social characteristics and privacy perceptions that can affect self-adaptive privacy design, as well as to indicate self-adaptive privacy related requirements that should be satisfied for users’ protection in SNs. The study was based on an interdisciplinary research instrument, adopting constructs and metrics from both sociological and privacy literature. The results of the survey lead to a pilot taxonomic analysis for self-adaptive privacy within SNs and to the proposal of specific privacy related requirements that should be considered for this domain. For further establishing of our interdisciplinary approach, a case study scenario was formulated, which underlines the importance of the identified self-adaptive privacy related requirements. In this regard, the study provides further insight for the development of the behavioral models that will enhance the optimal design of self-adaptive privacy preserving schemes in SNs, as well as designers to support the principle of PbD from a technical perspective.

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Dynamic social privacy protection based on graph mode partition in complex social network

Cited by 10 publications

References 29 publications

A Consumer Data Privacy Protection Model Based on Non-Parametric Statistics for Dynamic Data Publishing in e-Commerce Platforms

A Consumer Data Privacy Protection Model Based on Non-Parametric Statistics for Dynamic Data Publishing in e-Commerce Platforms

k-NDDP: An Efficient Anonymization Model for Social Network Data Release

Identifying Privacy Related Requirements for the Design of Self-Adaptive Privacy Protections Schemes in Social Networks

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