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Which Strategies are Used in the Design of Technical LA Infrastructure?: A Qualitative Interview Study
... Traditionally, LA research focuses on learner behavior as it interfaces with the keyboard and mouse in the learning management system. This narrow perspective on learner behavior in digital environments can lead to incomplete or ambiguous data traces because many other factors are difficult to capture and thus cannot be taken into account [1]. To counteract such potential shortcomings, approaches such as multimodal learning analytics (MMLA) are used [2]. ...
... For the software architecture design, we used the Big Data Value Chain (BDVC) [64] as a guideline. The BDVC can be used to model the high-level activities that make up any information system [1]. The BDVC identifies the following key high-level activities: data acquisition, data analysis, data curation, data storage, and data usage. ...
Research shows that various contextual factors can have an impact on learning. Some of these factors can originate from the physical learning environment (PLE) in this regard. When learning from home, learners have to organize their PLE by themselves. This paper is concerned with identifying, measuring, and collecting factors from the PLE that may affect learning using mobile sensing. More specifically, this paper first investigates which factors from the PLE can affect distance learning. The results identify nine types of factors from the PLE associated with cognitive, physiological, and affective effects on learning. Subsequently, this paper examines which instruments can be used to measure the investigated factors. The results highlight several methods involving smart wearables (SWs) to measure these factors from PLEs successfully. Third, this paper explores how software infrastructure can be designed to measure, collect, and process the identified multimodal data from and about the PLE by utilizing mobile sensing. The design and implementation of the Edutex software infrastructure described in this paper will enable learning analytics stakeholders to use data from and about the learners’ physical contexts. Edutex achieves this by utilizing sensor data from smartphones and smartwatches, in addition to response data from experience samples and questionnaires from learners’ smartwatches. Finally, this paper evaluates to what extent the developed infrastructure can provide relevant information about the learning context in a field study with 10 participants. The evaluation demonstrates how the software infrastructure can contextualize multimodal sensor data, such as lighting, ambient noise, and location, with user responses in a reliable, efficient, and protected manner.
... Reidenberg's work in [24] highlights the importance of strong privacy measures when dealing with big data, emphasizing the need to address potential risks associated with the use of educational data. More recent studies explore current challenges and propose strategies for balancing the usefulness of data analytics with the necessity to protect student privacy [25]. These studies advocate for responsible handling of data, encryption methods, and the implementation of privacyenhancing technologies to ensure ethical use of data in educational analytics. ...
The demand for personalized learning experiences and effective analytics in education has significantly increased. The integration of technology in education has brought about significant changes in teaching and learning practices. In the era of digital technology, the integration of education technology in the classroom has led to a change in teaching methods and learning strategies. In this paper, we introduce the KNIGHT (AI in Education at Hochschule für Technik (HFT) Stuttgart) framework, which is a holistic solution designed to tackle the complex issue of personalized education in a digital era. The paper explores the application of multimodal data integration, the novel application of deep reinforcement learning to education analytics, and the ethical consideration of privacy-preserving personalized feedback. The proposed framework's efficacy is substantiated through a case study, demonstrating its potential to revolutionize person-alized education. This paper provides a comprehensive overview of the current discourse, providing valuable insights for educators, policymakers, and researchers into the multifaceted landscape of modern education, contributing to ongoing discussions and advancements in educational technology.
... Sie überführt die formal-mathematische Stringenz der Psychometrie in direkt verwertbare Aussagen darüber, was getestete Personen wissen und können und damit wie kompetent sie sind.Außer Frage steht, dass Learning Analytics und Kompetenzdiagnostik in Deutschland Hand in Hand mit den europäischen (DSGVO) und deutschen Datenschutzrichtlinien und ethischen Werten umgesetzt werden müssen. Neben technischen Lösungen zu einer datenschutzkonformen Verarbeitung von Prozessdaten aus Lernumgebungen(Ciordas-Hertel et al. 2019;Ciordas-Hertel et al. 2020), habenHansen, Rensing, Hermann und Drachsler (2020) einen Verhaltenskodex für Trusted Learning Analytics (TLA) veröffentlicht. Auf der Seite des Datenschutzes benennt der Kodex die aktuelle Rechtsprechung der DSGVO und erläutert diese für die Nutzung von Daten in der Bildungspraxis anhand sieben Prinzipien: 1. Zustimmung zur Datenerhebung, 2. Maxime der Datensparsamkeit, 3. Regelung der Zusammenarbeit mit Dritten, 4. Regelungen zur Datenlöschung, 5. Ermöglichung des Zugangs zu den Daten, 6. Transparenz der Datenquellen und 7. die Verwendung von Daten für Forschungszwecke. ...
Im Frühjahr 2020 wurden Schulen unerwartet vor die Herausforderung gestellt, Unterricht und Schulentwicklung vor dem Hintergrund kontinuierlicher pandemiebedingter Disruptionen zu ermöglichen. Unterricht vor Ort wurde ersetzt durch digitale Formate des Lernens und der Kommunikation auf Distanz.
Für die Bildungspraxis erweisen sich dabei die Herausforderungen im Bereich der digitalen Schulverwaltung, des digitalen Lernens und der Diagnostik von Lernfortschritten als besonders relevant. Insbesondere die computergestützte Diagnostik bietet großes Potenzial, um Erkenntnisse nicht nur über Lernergebnisse, sondern auch Lernprozesse zu generieren. Im Bereich der Bildungsforschung interessiert, wie Lernen durch digitale Medien gestaltet werden kann und wie die dabei generierten Daten für die Bildungspraxis gewinnbringend genutzt werden können.
Dieser Beitrag beschreibt die Herausforderungen und Potenziale, die sich im Bereich von computerbasierter, lernbegleitender Diagnostik gegenwärtig zeigen. Diese liegen insbesondere in der flächendeckenden Einführung entsprechender Instrumente in den Schulen sowie der Aus- und Weiterbildung von Lehrpersonen im Umgang mit diesen. Darauf aufbauend werden Bedarfe der Bildungspraxis und Desiderata der Bildungsforschung gegenübergestellt und auf Synergiepotenziale hingewiesen.
... In the following qualitative interview study, we conducted eleven semi-structured interviews with experts in the development of learning analytics infrastructure [2] based in six countries. The findings of this study revealed strategies that are used to develop technical LA infrastructure. ...
The advent of the European General Data Protection Regulation (GDPR) imposes organizations to cope with radical changes concerning user data protection paradigms. GDPR, by promoting a Privacy by Design approach, obliges organizations to drastically change their methods regarding user data acquisition, management, processing, as well as data breaches monitoring, notification and preparation of prevention plans. This enforces data subjects (e.g., citizens, customers) rights by enabling them to have more information regarding usage of their data, and to take decisions (e.g., revoking usage permissions). Moreover, organizations are required to trace precisely their activities on user data, enabling authorities to monitor and sanction more easily. Indeed, since GDPR has been introduced, authorities have heavily sanctioned companies found as not GDPR compliant. GDPR is difficult to apply also for its length, complexity, covering many aspects, and not providing details concerning technical and organizational security measures to apply. This calls for tools and methods able to support organizations in achieving GDPR compliance. From the industry and the literature, there are many tools and prototypes fulfilling specific/isolated GDPR aspects, however there is not a comprehensive platform able to support organizations in being compliant regarding all GDPR requirements. In this paper, we propose the design of an architecture for such a platform, able to reuse and integrate peculiarities of those heterogeneous tools, and to support organizations in achieving GDPR compliance. We describe the architecture, designed within the DEFeND EU project, and discuss challenges and preliminary benefits in applying it to the healthcare and energy domains.
Artificial intelligence and data analysis (AIDA) are increasingly entering the field of education. Within this context, the subfield of learning analytics (LA) has, since its inception, had a strong emphasis upon ethics, with numerous checklists and frameworks proposed to ensure that student privacy is respected and potential harms avoided. Here, we draw attention to some of the assumptions that underlie previous work in ethics for LA, which we frame as three tensions. These assumptions have the potential of leading to both the overcautious underuse of AIDA as administrators seek to avoid risk, or the unbridled misuse of AIDA as practitioners fail to adhere to frameworks that provide them with little guidance upon the problems that they face in building LA for institutional adoption. We use three edge cases to draw attention to these tensions, highlighting places where existing ethical frameworks fail to inform those building LA solutions. We propose a pilot open database that lists edge cases faced by LA system builders as a method for guiding ethicists working in the field towards places where support is needed to inform their practice. This would provide a middle space where technical builders of systems could more deeply interface with those concerned with policy, law and ethics and so work towards building LA that encourages human flourishing across a lifetime of learning.
Practitioner Notes
What is already known about this topic Applied ethics has a number of well‐established theoretical groundings that we can use to frame the actions of ethical agents, including, deontology, consequentialism and virtue ethics.
Learning analytics has developed a number of checklists, frameworks and evaluation methodologies for supporting trusted and ethical development, but these are often not adhered to by practitioners.
Laws like the General Data Protection Regulation (GDPR) apply to fields like education, but the complexity of this field can make them difficult to apply.
What this paper adds Evidence of tensions and gaps in existing ethical frameworks and checklists to support the ethical development and implementation of learning analytics.
A set of three edge cases that demonstrate places where existing work on the ethics of AI in education has failed to provide guidance.
A “practical ethics” conceptualisation that draws on virtue ethics to support practitioners in building learning analytics systems.
Implications for practice and/or policy Those using AIDA in education should collect and share example edge cases to support development of practical ethics in the field.
A multiplicity of ethical approaches are likely to be useful in understanding how to develop and implement learning analytics ethically in practical contexts.
Education and education research are experiencing increased digitization and datafication, partly thanks to the rise in popularity of massively open online courses (MOOCs). The infrastructures that collect, store and analyse the resulting big data have received critical scrutiny from sociological, epistemological, ethical and analytical perspectives. These critiques tend to highlight concerns and/or warnings about the lack of the infrastructures' and builders' understanding of various nontechnical aspects of big data research (eg seeing data as neutral rather than as products of social processes). These critiques have primarily come from outside of the builder community, rendering the conversation largely one‐sided and devoid of the voices of the builders themselves. The purpose of this paper is to re‐balance the conversation by reporting the results of interviews with 11 data infrastructure builders in higher education institutions. The interviews reveal that builders engage deeply with the issues the critiques outline, not only thinking about them, but also developing practices to address them. The paper focuses the findings on three themes: designing a productive science, navigating ubiquitous ethics and achieving real human impact. Researchers, policymakers and infrastructure builders can use these accounts to better understand the building process and experience.
The advent of the European General Data Protection Regulation (GDPR) imposes organizations to cope with radical changes concerning user data protection paradigms. GDPR, by promoting a Privacy by Design approach, obliges organizations to drastically change their methods regarding user data acquisition, management, processing, as well as data breaches monitoring, notification and preparation of prevention plans. This enforces data subjects (e.g., citizens, customers) rights by enabling them to have more information regarding usage of their data, and to take decisions (e.g., revoking usage permissions). Moreover, organizations are required to trace precisely their activities on user data, enabling authorities to monitor and sanction more easily. Indeed, since GDPR has been introduced, authorities have heavily sanctioned companies found as not GDPR compliant. GDPR is difficult to apply also for its length, complexity, covering many aspects, and not providing details concerning technical and organizational security measures to apply. This calls for tools and methods able to support organizations in achieving GDPR compliance. From the industry and the literature, there are many tools and prototypes fulfilling specific/isolated GDPR aspects, however there is not a comprehensive platform able to support organizations in being compliant regarding all GDPR requirements. In this paper, we propose the design of an architecture for such a platform, able to reuse and integrate peculiarities of those heterogeneous tools, and to support organizations in achieving GDPR compliance. We describe the architecture, designed within the DEFeND EU project, and discuss challenges and preliminary benefits in applying it to the healthcare and energy domains.
Evidence suggests that individuals are often willing to exchange personal data for (real or perceived) benefits. Such an exchange may be impacted by their trust in a particular context and their (real or perceived) control over their data.
Students remain concerned about the scope and detail of surveillance of their learning behavior, their privacy, their control over what data are collected, the purpose of the collection, and the implications of any analysis. Questions arise as to the extent to which students are aware of the benefits and risks inherent in the exchange of their data, and whether they are willing to exchange personal data for more effective and supported learning experiences.
This study reports on the views of entry level students at the Open University (OU) in 2018. The primary aim is to explore differences between stated attitudes to privacy and their online behaviors, and whether these same attitudes extend to their university's uses of their (personal) data. The analysis indicates, inter alia, that there is no obvious relationship between how often students are online or their awareness of/concerns about privacy issues in online contexts and what they actually do to protect themselves. Significantly though, the findings indicate that students overwhelmingly have an inherent trust in their university to use their data appropriately and ethically.
Based on the findings, we outline a number of issues for consideration by higher education institutions, such as the need for transparency (of purpose and scope), the provision of some element of student control, and an acknowledgment of the exchange value of information in the nexus of the privacy calculus.
Privacy and data protection are a major stumbling blocks for a data-driven educational future. Privacy policies are based on legal regulations, which in turn get their justification from political, cultural, economical and other kinds of discourses. Applied to learning analytics, do these policies also need a pedagogical grounding? This paper is based on an actual conundrum in developing a technical specification on privacy and data protection for learning analytics for an international standardisation organisation. Legal arguments vary a lot around the world, and seeking ontological arguments for privacy does not necessarily lead to a universal acclaim of safeguarding the learner meeting the new data-driven practices in education. Maybe it would be easier to build consensus around educational values, but is it possible to do so?
This paper explores the legal and cultural contexts that make it a challenge to define universal principles for privacy and data protection. If not universal principles, consent could be the point of departure for assuring privacy? In education, this is not necessarily the case as consent will be balanced by organisations’ legitimate interests and contract. The different justifications for privacy, the legal obligation to separate analysis from intervention, and the way learning and teaching works makes it necessary to argue data privacy from a pedagogical perspective. The paper concludes with three principles that are proposed to inform an educational maxim for privacy and data protection in learning analytics.
Multimodality in learning analytics and learning science is under the spotlight. The landscape of sensors and wearable trackers that can be used for learning support is evolving rapidly, as well as data collection and analysis methods. Multimodal data can now be collected and processed in real time at an unprecedented scale. With sensors, it is possible to capture observable events of the learning process such as learner's behaviour and the learning context. The learning process, however, consists also of latent attributes, such as the learner's cognitions or emotions. These attributes are unobservable to sensors and need to be elicited by human-driven interpretations. We conducted a literature survey of experiments using multimodal data to frame the young research field of multimodal learning analytics. The survey explored the multimodal data used in related studies (the input space) and the learning theories selected (the hypothesis space). The survey led to the formulation of the Multimodal Learning Analytics Model whose main objectives are of (O1) mapping the use of multimodal data to enhance the feedback in a learning context; (O2) showing how to combine machine learning with multimodal data; and (O3) aligning the terminology used in the field of machine learning and learning science. © 2018 The Authors. Journal of Computer Assisted Learning Published by John Wiley & Sons, Ltd.
Purpose
The purpose of this paper is to draw attention to the potential of “small data” to complement research in learning analytics (LA) and to share some of the insights learned from this approach.
Design/methodology/approach
This study demonstrates an approach inspired by design science research, making a dashboard available to n =1,905 students in 11 study programs (used by n =887) to learn how it is being used and to gather student feedback.
Findings
Students react positively to the LA dashboard, but usage and feedback differ depending on study success.
Research limitations/implications
More research is needed to explore the expectations of a high-performing student with regards to LA dashboards.
Originality/value
This publication demonstrates how a small data approach to LA contributes to building a better understanding.
As the field of learning analytics matures, and discourses surrounding the scope, definition, challenges, and opportunities of learning analytics become more nuanced, there is benefit both in reviewing how far we have come in considering associated ethical issues and in looking ahead. This chapter provides an overview of how our own thinking has developed and maps our journey against broader developments in the field. Against a backdrop of technological advances and increasing concerns around pervasive surveillance and the role and unintended consequences of algorithms, the development of research in learning analytics as an ethical and moral practice provides a rich picture of fears and realities. More importantly, we begin to see ethics and privacy as crucial enablers within learning analytics. The chapter briefly locates ethics in learning analytics in the broader context of the forces shaping higher education and the roles of data and evidence before tracking our personal research journey, highlighting current work in the field, and concluding by mapping future issues for consideration.
This paper presents the results of a review of eight policies for learning analytics of relevance for higher education, and discusses how these policies have tried to address prominent challenges in the adoption of learning analytics, as identified in the literature. The results show that more considerations need to be given to establishing communication channels among stakeholders and adopting pedagogy-based approaches to learning analytics. It also reveals the shortage of guidance for developing data literacy among end-users and evaluating the progress and impact of learning analytics. Moreover, the review highlights the need to establish formalised guidelines to monitor the soundness, effectiveness, and legitimacy of learning analytics. As interest in learning analytics among higher education institutions continues to grow, this review will provide insights into policy and strategic planning for the adoption of learning analytics.
Ethical and legal objections to learning analytics are barriers to development of the field, thus potentially denying students the benefits of predictive analytics and adaptive learning. Jisc, a charitable organisation which champions the use of digital technologies in UK education and research, has attempted to address this with the development of a Code of Practice for Learning Analytics. The Code covers the main issues institutions need to address in order to progress ethically and in compliance with the law. This paper outlines the extensive research and consultation activities which have been carried out to produce a document which covers the concerns of institutions and, critically, the students they serve. The resulting model for developing a code of practice includes a literature review, setting up appropriate governance structures, developing a taxonomy of the issues, drafting the code, consulting widely with stakeholders, publication, dissemination, and embedding it in institutions.
This issue of the Journal of Learning Analytics features a special section on ethics and privacy that is guest edited by a team of researchers involved in the European Learning Analytics Community Exchange (LACE) project. The issue also features a paper that looks at the use of new methods for the measurement of self-regulated learning. This editorial concludes with a summary of the future changes in the editorial team of the journal.
The widespread adoption of Learning Analytics (LA) and Educational Data Mining (EDM) has somewhat stagnated recently, and in some prominent cases even been reversed following concerns by governments, stakeholders and civil rights groups about privacy and ethics applied to the handling of personal data. In this ongoing discussion, fears and realities are often indistinguishably mixed up, leading to an atmosphere of uncertainty among potential beneficiaries of Learning Analytics, as well as hesitations among institutional managers who aim to innovate their institution's learning support by implementing data and analytics with a view on improving student success. In this paper, we try to get to the heart of the matter, by analysing the most common views and the propositions made by the LA community to solve them. We conclude the paper with an eight-point checklist named DELICATE that can be applied by researchers, policy makers and institutional managers to facilitate a trusted implementation of Learning Analytics.
Big data is the emerging field where innovative technology offers new ways to extract value from the tsunami of available information. As with any emerging area, terms and concepts can be open to different interpretations. The Big Data domain is no different. This chapter examines the different definitions of “Big Data” which have emerged over the last number of years to label data with different attributes. The Big Data Value Chain is introduced to describe the information flow within a big data system as a series of steps needed to generate value and useful insights from data. The value chain enables the analysis of big data technologies for each step within the chain. The chapter explores the concept of a Big Data Ecosystem. It examines the use of the ecosystem metaphor within the business community to describe the business environment and how it can be extended to the big data context. Key stakeholders of a big data ecosystem are identified together with the challenges that need to be overcome to enable a big data ecosystem in Europe.
Context: Technical debt (TD) is a metaphor reflecting technical compromises that can yield short-term benefit but may hurt the long-term health of a software system. Objective: This work aims at collecting studies on TD and TD management (TDM), and making a classification and thematic analysis on these studies, to obtain a comprehensive understanding on the TD concept and an overview on the current state of research on TDM. Method: A systematic mapping study was performed to identify and analyze research on TD and its management, covering publications between 1992 and 2013. Results: Ninety-four studies were finally selected. TD was classified into ten types, eight TDM activities were identified, and twenty-nine tools for TDM were collected. Conclusions: The term “debt” has been used in different ways by different people, which leads to ambiguous interpretation of the term. Code-related TD and its management have gained the most attention. There is a need for more empirical studies with high-quality evidence on the whole TDM process and on the application of specific TDM approaches in industrial settings. Moreover, dedicated TDM tools are needed for managing various types of TD in the whole TDM process.
Context: Technical debt (TD) is a metaphor reflecting technical compromises that can yield short-term benefit but may hurt the long-term health of a software system. Objective: This work aims at collecting studies on TD and TD management (TDM), and making a classification and thematic analysis on these studies, to obtain a comprehensive understanding on the TD concept and an overview on the current state of research on TDM. Method: A systematic mapping study was performed to identify and analyze research on TD and its management, covering publications between 1992 and 2013. Results: Ninety-four studies were finally selected. TD was classified into ten types, eight TDM activities were identified, and twenty-nine tools for TDM were collected. Conclusions: The term “debt” has been used in different ways by different people, which leads to ambiguous interpretation of the term. Code-related TD and its management have gained the most attention. There is a need for more empirical studies with high-quality evidence on the whole TDM process and on the application of specific TDM approaches in industrial settings. Moreover, dedicated TDM tools are needed for managing various types of TD in the whole TDM process.
One key factor for the successful outcome of a Learning Analytics (LA) infrastructure is the ability to decide which software architecture concept is necessary. Big Data can be used to face the challenges LA holds. Additional challenges on privacy rights are introduced to the Europeans by the General Data Protection Regulation (GDPR). Beyond that, the challenge of how to gain the trust of the users remains. We found diverse architectural concepts in the domain of LA. Selecting an appropriate solution is not straightforward. Therefore, we conducted a structured literature review to assess the state-of-the-art and provide an overview of Big Data architectures used in LA. Based on the examination of the results, we identify common architectural components and technologies and present them in the form of a mind map. Linking the findings, we are proposing an initial approach towards a Trusted and Interoperable Learning Analytics Infrastructure (TIILA).
Data protection regulations give individuals rights to obtain the information that entities have on them. However, providing such information can also reveal aspects of the entity's underlying technical infrastructure and organizational processes. This article explores the security implications this raises and highlights the need to consider such in rights in fulfillment processes.
The papers in ths special section focus on the topic of learning analytics.
Learning analytics open up a complex landscape of privacy and policy issues, which, in turn, influence how learning analytics systems and practices are designed. Research and development is governed by regulations for data storage and management, and by research ethics. Consequently, when moving solutions out the research labs implementers meet constraints defined in national laws and justified in privacy frameworks. This paper explores how the OECD, APEC and EU privacy frameworks seek to regulate data privacy, with significant implications for the discourse of learning, and ultimately, an impact on the design of tools, architectures and practices that now are on the drawing board. A detailed list of requirements for learning analytics systems is developed, based on the new legal requirements defined in the European General Data Protection Regulation, which from 2018 will be enforced as European law. The paper also gives an initial account of how the privacy discourse in Europe, Japan, South-Korea and China is developing and reflects upon the possible impact of the different privacy frameworks on the design of LA privacy solutions in these countries. This research contributes to knowledge of how concerns about privacy and data protection related to educational data can drive a discourse on new approaches to privacy engineering based on the principles of Privacy by Design. For the LAK community, this study represents the first attempt to conceptualise the issues of privacy and learning analytics in a cross-cultural context. The paper concludes with a plan to follow up this research on privacy policies and learning analytics systems development with a new international study.
This book focuses on the uses of big data in the context of higher education. The book describes a wide range of administrative and operational data gathering processes aimed at assessing institutional performance and progress in order to predict future performance, and identifies potential issues related to academic programming, research, teaching and learning?. Big data refers to data which is fundamentally too big and complex and moves too fast for the processing capacity of conventional database systems. The value of big data is the ability to identify useful data and turn it into useable information by identifying patterns and deviations from patterns.
The European Learning Analytics Community Exchange (LACE) project is responsible for an ongoing series of workshops on ethics and privacy in learning analytics (EP4LA), which have been responsible for driving and transforming activity in these areas. Some of this activity has been brought together with other work in the papers that make up this special issue. These papers cover the creation and development of ethical frameworks, as well as tools and approaches that can be used to address issues of ethics and privacy. This editorial suggests that it is worth taking time to consider the often intertangled issues of ethics, data protection and privacy separately. The challenges mentioned within the special issue are summarised in a table of 22 challenges that are used to identify the values that underpin work in this area. Nine ethical goals are suggested as the editors’ interpretation of the unstated values that lie behind the challenges raised in this paper.
Cloud computing is an emerging paradigm for large scale infrastructures. It has the advantage of reducing cost by sharing computing and storage resources, combined with an on-demand provisioning mechanism relying on a pay-per-use business model. These new features have a direct impact on the budgeting of IT budgeting but also affect traditional security, trust and privacy mechanisms. Many of these mechanisms are no longer adequate, but need to be rethought to fit this new paradigm. In this paper we assess how security, trust and privacy issues occur in the context of cloud computing and discuss ways in which they may be addressed.
We propose a fully homomorphic encryption scheme - i.e., a scheme that allows one to evaluate circuits over encrypted data without being able to decrypt. Our solution comes in three steps. First, we provide a general result - that, to construct an encryption scheme that permits evaluation of arbitrary circuits, it suffices to construct an encryption scheme that can evaluate (slightly augmented versions of) its own decryption circuit; we call a scheme that can evaluate its (augmented) decryption circuit bootstrappable. Next, we describe a public key encryption scheme using ideal lattices that is almost bootstrappable. Lattice-based cryptosystems typically have decryption algorithms with low circuit complexity, often dominated by an inner product computation that is in NC1. Also, ideal lattices provide both additive and multiplicative homomorphisms (modulo a public-key ideal in a polynomial ring that is represented as a lattice), as needed to evaluate general circuits. Unfortunately, our initial scheme is not quite bootstrap- pable - i.e., the depth that the scheme can correctly evalu- ate can be logarithmic in the lattice dimension, just like the depth of the decryption circuit, but the latter is greater than the former. In the final step, we show how to modify the scheme to reduce the depth of the decryption circuit, and thereby obtain a bootstrappable encryption scheme, with- out reducing the depth that the scheme can evaluate. Ab- stractly, we accomplish this by enabling the encrypter to start the decryption process, leaving less work for the de- crypter, much like the server leaves less work for the de- crypter in a server-aided cryptosystem. Categories and Subject Descriptors: E.3 (Data En-
Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Da
- C O T E Union
- E Parliament
C. o. t. E. Union and E. Parliament, "Regulation (EU) 2016/679
of the European Parliament and of the Council of 27 April 2016
on the protection of natural persons with regard to the processing
of personal data and on the free movement of such data, and
repealing Directive 95/46/EC (General Da," Official Journal of the
European Union, vol. L119, pp. 1-88, 2016. [Online]. Available:
https://eur-lex.europa.eu/eli/reg/2016/679/oj
Privacy and analytics
- H Drachsler
- W Greller
H. Drachsler and W. Greller, "Privacy and analytics," in Proceedings
of the Sixth International Conference on Learning Analytics &
Knowledge -LAK '16, vol. 25-29-Apri.
New York, New
York, USA: ACM Press, 2016, pp. 89-98. [Online]. Available:
http://dl.acm.org/citation.cfm?doid=2883851.2883893
Ethics and privacy as enablers of learning analytics
- D Gasevišć
- S Dawson
- J Jovanović
in New Horizons for a Data-Driven Economy: A Roadmap for Usage and Exploitation of Big Data in Europe
- E Curry
E. Curry, "The Big Data Value Chain: Definitions, Concepts,
and Theoretical Approaches," in New Horizons for a Data-Driven Economy: A Roadmap for Usage and Exploitation of
Big Data in Europe, J. M. Cavanillas, E. Curry, and W. Wahlster,
Eds. Cham: Springer International Publishing, 2016, pp. 29-37. [Online]. Available: https://doi.org/10.1007/978-3-319-21569-3 3
http://link.springer.com/10.1007/978-3-319-21569-3 3