Nothing to Hide? On the Security and Privacy Threats Beyond Open Data

Pastor-Galindo, Javier; Mármol, Félix Gómez; Pérez, Gregorio Martínez

doi:10.1109/mic.2021.3088335

Cited by 11 publications

(4 citation statements)

References 10 publications

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“…• Ensuring sufficient resilience against AI-powered attacks: In recent years, due to the proliferation of AIbased systems, privacy breaches have increased significantly because traditional anonymization mechanisms cannot ensure sufficient resilience against AIpowered attacks [256]- [259]. AI-powered attacks can be launched to disclose identities, SAs, and memberships from large and complex datasets with the help of hyperparameter tuning [260].…”

Section: A Open Challenges In Persevering User Privacy Leveraging Camsmentioning

confidence: 99%

Toward Privacy Preservation Using Clustering Based Anonymization: Recent Advances and Future Research Outlook

2022

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With the continuous increase in avenues of personal data generation, privacy protection has become a hot research topic resulting in various proposed mechanisms to address this social issue. The main technical solutions for guaranteeing a user's privacy are encryption, pseudonymization, anonymization, differential privacy (DP), and obfuscation. Despite the success of other solutions, anonymization has been widely used in commercial settings for privacy preservation because of its algorithmic simplicity and low computing overhead. It facilitates unconstrained analysis of published data that DP and the other latest techniques cannot offer, and it is a mainstream solution for responsible data science. In this paper, we present a comprehensive analysis of clustering-based anonymization mechanisms (CAMs) that have been recently proposed to preserve both privacy and utility in data publishing. We systematically categorize the existing CAMs based on heterogeneous types of data (tables, graphs, matrixes, etc.), and we present an up-to-date, extensive review of existing CAMs and the metrics used for their evaluation. We discuss the superiority and effectiveness of CAMs over traditional anonymization mechanisms. We highlight the significance of CAMs in different computing paradigms, such as social networks, the internet of things, cloud computing, AI, and location-based systems with regard to privacy preservation. Furthermore, we present various proposed representative CAMs that compromise individual privacy, rather than safeguarding it. Finally, we discuss the technical challenges of applying CAMs, and we suggest promising opportunities for future research. To the best of our knowledge, this is the first work to systematically cover current CAMs involving different data types and computing paradigms.

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Section: A Open Challenges In Persevering User Privacy Leveraging Camsmentioning

confidence: 99%

Toward Privacy Preservation Using Clustering Based Anonymization: Recent Advances and Future Research Outlook

2022

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“…Frequently without prior consent of individuals, personal information becomes globally available to other organisations (Poritskiy et al , 2019). This has made individuals, as well as organisations, highly vulnerable about security and data privacy (Naughton and Daly, 2020; Pastor-Galindo et al , 2021; Tikkinen-Piri et al , 2018).…”

Section: Introductionmentioning

confidence: 99%

Understanding challenges of GDPR implementation in business enterprises: a systematic literature review

Smirnova,

Travieso-Morales

2024

IJLMA

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Purpose The general data protection regulation (GDPR) was designed to address privacy challenges posed by globalisation and rapid technological advancements; however, its implementation has also introduced new hurdles for companies. This study aims to analyse and synthesise the existing literature that focuses on challenges of GDPR implementation in business enterprises, while also outlining the directions for future research. Design/methodology/approach The methodology of this review follows the preferred reporting items for systematic reviews and meta-analysis guidelines. It uses an extensive search strategy across Scopus and Web of Science databases, rigorously applying inclusion and exclusion criteria, yielding a detailed analysis of 16 selected studies that concentrate on GDPR implementation challenges in business organisations. Findings The findings indicate a predominant use of conceptual study methodologies in prior research, often limited to specific countries and technology-driven sectors. There is also an inclination towards exploring GDPR challenges within small and medium enterprises, while larger enterprises remain comparatively unexplored. Additionally, further investigation is needed to understand the implications of emerging technologies on GDPR compliance. Research limitations/implications This study’s limitations include reliance of the search strategy on two databases, potential exclusion of relevant research, limited existing literature on GDPR implementation challenges in business context and possible influence of diverse methodologies and contexts of previous studies on generalisability of the findings. Originality/value The originality of this review lies in its exclusive focus on analysing GDPR implementation challenges within the business context, coupled with a fresh categorisation of these challenges into technical, legal, organisational, and regulatory dimensions.

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“…The social media has become an ideal arena for communicating with a large audience and freely transmitting information [1]. While this has brought numerous benefits to society, it has also given rise to malicious operations that steer public opinion and manipulate users by spreading misinformation and deploying fake accounts [2].…”

Section: Introductionmentioning

confidence: 99%

“…The danger this poses for democracy and freedom of thought has mobilized major companies (e.g. Facebook, Instagram, Twitter, YouTube, and TikTok) to transparently fight against influence operations 1 , platform manipulation 2 , integrity breakdowns 3 , or misinformation spread 4 , but have not yet achieved a mature defence strategy [5].…”

Section: Introductionmentioning

confidence: 99%

BOTTER: A framework to analyze social bots in Twitter

Pastor-Galindo¹,

Mármol²,

Pérez³

2021

Preprint

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Social networks have triumphed in communicating people online, but they have also been exploited to launch influence operations for manipulating society. The deployment of software-controlled accounts (e.g., social bots) has proven to be one of the most effective enablers for that purpose, and tools for their detection have been developed and widely adopted. However, the way to analyze these accounts and measure their impact is heterogeneous in the literature, where each case study performs unique measurements. To unify these efforts, we propose a common framework to analyze the interference of social bots in Twitter. The methodology compares the nonauthentic actors with the rest of users from different perspectives, thus building objective metrics to measure their actual impact. We validate the framework by applying it to a dataset of Twitter iterations dated in the weeks preceding the 2019 Spanish general election. In this sense, we check that our framework facilitates the quantitative evaluation of unauthentic groups, particularly discovering that social bots changed the natural dynamics of the network in these days, but did not have a significant impact. We also consider this methodology as a practical tool for the qualitative interpretation of experimental results, particularly suggesting within the aforementioned electoral context that semiautomated accounts are potentially more threatening than fully automated ones.

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Nothing to Hide? On the Security and Privacy Threats Beyond Open Data

Cited by 11 publications

References 10 publications

Toward Privacy Preservation Using Clustering Based Anonymization: Recent Advances and Future Research Outlook

Toward Privacy Preservation Using Clustering Based Anonymization: Recent Advances and Future Research Outlook

Understanding challenges of GDPR implementation in business enterprises: a systematic literature review

BOTTER: A framework to analyze social bots in Twitter

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