The Concept of Identifiability in ML Models

Recent research indicates that the machine learning process can be reversed by adversarial attacks. These attacks can be used to derive personal information from the training. The supposedly anonymising machine learning process represents a process of pseudonymisation and is, therefore, subject to technical and organisational measures. Consequently, the unexamined belief in anonymisation as a guarantor for privacy cannot be easily upheld. It is, therefore, crucial to measure privacy through the lens of adversarial attacks and precisely distinguish what is meant by personal data and non-personal data and above all determine whether ML models represent pseudonyms from the training data

Erasure and Anonymisation of Personal Data in Context of General Data Protection Regulation

Many controllers have a desire to be able to continue using personal data instead of deleting them after the processing purpose has been fulfilled. The discussion regularly arises whether the erasure of personal data is required by the General Data Protection Regulation (GDPR) and whether it can also happen by anonymising the data. This article examines how the GDPR regulates the two terms of “erasure” and “anonymisation” as well as what requirements are demanded by using any of these in the personal data lifecycle. An obligation to delete personal data always requires personal data. In the case of anonymous data, erasure is not required and cannot be claimed. The question to be examined and discussed in the article is therefore: If personal data exist and there is a claim for erasure, can the obligation to erase be fulfilled by anonymising the personal data? Such question has not yet been addressed in the case law and has only been examined to a limited extent in the literature by different authors with no exact court ruling. Some authors state that the question can be answered in such a way that an obligation to delete can also be fulfilled by anonymising the data (Dierks & Roßnagel, 2021; Taeger & Gabel, 2021); meanwhile, others consider that anonymisation cannot be considered as data erasure. The answer to this question is important because it determines whether large data processors are allowed to keep data that they would have to delete and use in anonymised form for Big Data analysis or Artificial Intelligence applications that are an integral part of the world of technology

Report to the Childhood Development Initiative on Archiving of C.D.I. Data

This report presents the ethical and legal issues involved in depositing data-sets of research for secondary use in Ireland

The Family Courts Information Pilot : November 2009 - December 2010

"This paper reviews the working of the Family Courts Information Pilot (FCIP). The pilot made written anonymised judgments available to the parties in certain Children Act cases (listed at paragraph 9) and to the wider public through the British and Irish Legal Information Institute (BAILII) website" -- page 4

Sharing Social Research Data in Ireland: A Practical Tool

Your data is valuable and has an importance outside your own original project. Allowing other researchers to reuse your data maximises the impact of your work, and benefits both the scholarly community and society in general. Sharing your data allows other researchers to use your material in ways you may not have thought of, or may not have been able to do within your research project. It allows other researchers to replicate your findings, to verify your results, test your instruments and compare with other studies. It also allows them to use your work to expand knowledge in important areas. It provides value for money by reducing duplication and advancing knowledge and also has a significant value in education, as it allows both graduate and under-graduate students to develop their skills in qualitative and quantitative research by using high-quality data in their studies, without having to conduct their own surveys.Archiving your data also guarantees its long-term preservation and accessibility. As many research teams are assembled only for individual projects, long-term preservation and access to research data collections can only be guaranteed if they are deposited in an archive which will manage them, ensure access and provide user-support. In addition, the archives will ensure that the datasets do not become obsolescent or corrupted.Finally, increasingly funders require that you make your research data available as a condition of their funding your research, so that other researchers can test your findings, and use your data to extend research in your area. Equally, publishers are also specifying access to research data as a condition for publication

Academic literacies in the digital university

Academic Literacies is an international field of study concerned with literacies and learning in tertiary education. Some recent work in this field has focused on online and elearning environments. In our book of 2007 (Goodfellow & Lea 2007) we used an academic literacies perspective to critique what we see as the focus in much elearning practice on the 'management of learning' at the expense of disciplinary pedagogies. We argued for attention to be paid to the centrality of texts, however mediated, in the construction of knowledge and the practices of learning. Our current focus on the 'digital' extends this critique to engage with three major discourses of technology currently constructing the 'digital age' in relation to education. The first is the metaphor of the 'digital native' or 'net generation'. The second is the discourse of 'Learning 2.0, the third is the trope of the 'unbundled university'. We conclude that we need to pay much more attention to textual practice around learning and scholarship,and that, as researchers and scholars, we need to work for the reconciliation of new discourses of the digital with the continuing development of critical pedagogical and social practice in the academy and the public sphere

Open Science in Software Engineering

Open science describes the movement of making any research artefact available to the public and includes, but is not limited to, open access, open data, and open source. While open science is becoming generally accepted as a norm in other scientific disciplines, in software engineering, we are still struggling in adapting open science to the particularities of our discipline, rendering progress in our scientific community cumbersome. In this chapter, we reflect upon the essentials in open science for software engineering including what open science is, why we should engage in it, and how we should do it. We particularly draw from our experiences made as conference chairs implementing open science initiatives and as researchers actively engaging in open science to critically discuss challenges and pitfalls, and to address more advanced topics such as how and under which conditions to share preprints, what infrastructure and licence model to cover, or how do it within the limitations of different reviewing models, such as double-blind reviewing. Our hope is to help establishing a common ground and to contribute to make open science a norm also in software engineering.Comment: Camera-Ready Version of a Chapter published in the book on Contemporary Empirical Methods in Software Engineering; fixed layout issue with side-note