Contextualization of Personal Data Discovery and Anonymization Tools

Tahir, Hassane; Brézillon, Patrick

doi:10.1007/978-981-16-6309-3_28

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…Three types of challenges can be distinguished when in the process of data anonymization, which are related to organizational context, functional or business context, and technical context. Each of these three contexts includes a list of related contextual elements [28], and the instantiation of these contextual elements can alert if the anonymization process can be short-circuited. Here, the contextual elements are clearly identified (i.e.…”

Section: Challenges Faced By Data Anonymization Expertsmentioning

confidence: 99%

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Untitled

2023

IJDMS

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Data anonymization is one of the solutions allowing companies to comply with the GDPR directive in terms of data protection. In this context, developers must follow several steps in the process of data anonymization in development and testing environments. Indeed, real personal and sensitive data must not leave the production environment which is very secure. Often, anonymization experts are faced with difficulties including the lack of data flows and mapping between data sources, the non-cooperation of the database project teams (refusal to change) or even the lack of skills of these teams present due to the age of the systems developed by experienced teams who unfortunately left the project. Other problems are lack of data models. The aim of this paper is to discuss an anonymization process of databases of banking applications and present our context-based recommendations to overcome the different issues met and the solutions to improve methodologies of data anonymization process.

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Section: Challenges Faced By Data Anonymization Expertsmentioning

confidence: 99%

“…The architecture of the context-based personal data discovery tool is shown in the Figure 4. All details about our approach and the implementation can be found in our book chapter in [28].…”

Section: Context-based Tools For Improving Anonymizationmentioning

confidence: 99%

Untitled

2023

IJDMS

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“…In Figure 2, five new edges have been introduced to change the structure of G for privacy preservation. Recently, many solutions have been proposed to preserve the privacy of SN users in G publishing [15][16][17][18][19][20][21]. These solutions have been used to preserve either nodes' or edges' privacy in the release of G. Recently, differential privacy-based solutions have also been proposed to alter the G's structure for privacy preservation [22].…”

Section: Introductionmentioning

confidence: 99%

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

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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