Challenges on Anonymity, Privacy, and Big Data

Basso, Tânia; Matsunaga, Roberta Mayumi; Moraes, Regina; Antunes, Nuno

doi:10.1109/ladc.2016.34

Cited by 20 publications

(16 citation statements)

References 34 publications

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“…However, such approaches are prone to human mistakes and privacy attacks, which can reveal private user information through background knowledge engineering and intersection attacks. For example, the exposure of Netflix users' data through linkage with other websites (Basso et al., 2016), and the infamous failure of InBloom of which the major issue was sharing of sensitive students' data with third parties (Gursoy et al., 2016).…”

Section: Privacy Enhancing Technologiesmentioning

confidence: 99%

The answer is (not only) technological: Considering student data privacy in learning analytics

Prinsloo

Slade

Khalil

2022

Brit J Educational Tech

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Evidence shows that appropriate use of technology in education has the potential to increase the effectiveness of, eg, teaching, learning and student support. There is also evidence that technology can introduce new problems and ethical issues, eg, student privacy. This article maps some limitations of technological approaches that ensure student data privacy in learning analytics from a critical data studies (CDS) perspective. In this conceptual article, we map the claims, grounds and warrants of technological solutions to maintaining student data privacy in learning analytics. Our findings suggest that many technological solutions are based on assumptions, such as that individuals have control over their data (‘data as commodity’), which can be exchanged under agreed conditions, or that individuals embrace their personal data privacy as a human right to be respected and protected. Regulating student data privacy in the context of learning analytics through technology mostly depends on institutional data governance, consent, data security and accountability. We consider alternative approaches to viewing (student) data privacy, such as contextual integrity; data privacy as ontological; group privacy; and indigenous understandings of privacy. Such perspectives destabilise many assumptions informing technological solutions, including privacy enhancing technology (PET). What is already known about this topic Various actors (including those in higher education) have access to and collect, use and analyse greater volumes of personal (student) data, with finer granularity, increasingly from multiplatforms and data sources. There is growing awareness and concern about individual (student) privacy. Privacy enhancing technologies (PETs) offer a range of solutions to individuals to protect their data privacy. What this paper adds A review of the assumption that technology provides adequate or complete solutions for ensuring individual data privacy. A mapping of five alternative understandings of personal data privacy and its implications for technological solutions. Consideration of implications for the protection of student privacy in learning analytics. Implications for practice and/or policy Student data privacy is not only a technological problem to be solved but should also be understood as a social problem. The use of PETs offers some solutions for data privacy in learning analytics. Strategies to protect student data privacy should include student agency, literacy and a whole‐system approach.

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Section: Privacy Enhancing Technologiesmentioning

confidence: 99%

The answer is (not only) technological: Considering student data privacy in learning analytics

Prinsloo

Slade

Khalil

2022

Brit J Educational Tech

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“…(6) Allowing the analytics to produce useful insight while preserving anonymity and individual privacy is highly challenging and constitutes one of the mounting concerns in Big Data (Basso et al 2016;Gahi et al 2016;Jensen 2013). Individuals can become widely vulnerable to security and privacy violations given that huge amounts of their behavioural data are being collected from online footprints, sensors, and sensory systems, notably without a pre-established goal (van der Sloot 2015), and are stored in dispersed databases thereby presenting an attractive target for attackers.…”

Section: Mrq6: Can We Identify Any Topics Within These Areas That Could Be Subjects For Further Research?mentioning

confidence: 99%

Enterprise architecture management as a solution for addressing general data protection regulation requirements in a big data context: a systematic mapping study

Georgiadis

Poels

2021

Inf Syst E-Bus Manage

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Context Big Data Analytics is a rapidly emerging IT practice whose applications offer benefits for a wide variety of business areas across an organisation. Given the wide scope of applications, the many types of processing involved, including those for purposes not yet foreseen, and the inherent privacy concerns resulting from collecting and storing personal data, the newly introduced General Data Protection Regulation (GDPR) poses specific challenges for safeguarding the security and protection of big data. These challenges are not limited to the IT function but extend across the entire organisation. This raises the question whether Enterprise Architecture Management (EAM), as an approach for ensuring the coherence, strategic alignment and focus on value creation of all organisational resources, offers guidance for addressing those challenges in a holistic manner, and thus provides a fruitful ground for developing an approach for complying to GDPR requirements in a Big Data context. Objective This study surveys the state-of-the-art in research on security, privacy, and protection of big data. The focus is on investigating which specific issues and challenges have been identified and whether these have been linked to GDPR requirements. Further, it examines whether previous research has investigated the potential of EAM in addressing those challenges and what the main findings of those studies are. Method We used Systematic Mapping Review (SMR), which is a methodology for literature review aimed at surveying the state-of-the-art in a research field as it is documented in the scientific literature. Further, we used Template Analysis, which is a thematic analysis technique, for coding the texts of the selected papers, classifying the research studies, and interpreting the different themes addressed in the literature.

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“…that is processed by data analytics algorithms. PRIVAaaS is based on anonymization techniques (e.g., generalization, suppression, encryption, masking [25]) and anonymization policies [26]. It is a free and open source tool, developed in Java language and suitable for big data and cloud computing contexts.…”

Section: Data Privacymentioning

confidence: 99%

An Integrated Big and Fast Data Analytics Platform for Smart Urban Transportation Management

et al. 2019

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Challenges on Anonymity, Privacy, and Big Data

Cited by 20 publications

References 34 publications

The answer is (not only) technological: Considering student data privacy in learning analytics

The answer is (not only) technological: Considering student data privacy in learning analytics

Enterprise architecture management as a solution for addressing general data protection regulation requirements in a big data context: a systematic mapping study

An Integrated Big and Fast Data Analytics Platform for Smart Urban Transportation Management

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