The COVID-19 pandemic has changed work life profoundly and concerns regarding the mental well-being of employees’ have arisen. Organizations have made rapid digital advancements and have started to use new collaborative tools such as social media platforms overnight. Our study aimed to investigate how professional social media communication has affected work engagement before and during the COVID-19 pandemic and the role of perceived social support, task resources, and psychological distress as predictors and moderators of work engagement.

In this paper, weprovide an explicit algorithm and a proof-of-concept implementation to transform Data Flow Diagrams into Privacy-Aware DataFlow Diagrams. Our tool assists software engineers in the critical but error-prone task of systematically inserting privacy checksduring design (they are automatically added by our tool) while still allowing them to inspect and edit if necessary.

The rapid rollout of digital health approaches in the ongoing global COVID-19 pandemic has neglected to prioritize data privacy and is a missed opportunity for building users’ trust in these technologies for future outbreaks and quotidian healthcare.

And here another paper: "Patient trust must come at the top of researchers’ priority list"

There is a gap between people’s online sharing of personal data and their concerns about privacy. Till now, this gap is addressed by attempting to match individual privacy preferences with service providers’ options for data handling. This approach has ignored the role different contexts play in data sharing. This paper aims at giving privacy engineering a new direction putting context centre stage and exploiting the affordances of machine learning in handling contexts and negotiating data sharing policies.

This release from the Sovrin Foundation around SSI & IoT demonstrates great opportunities in IoT in addressing real security challenges, data provenance and opening whole new areas of operation.

The paper is related to https://www.mydata-control.de by Fraunhofer IESE Usage Control group. From the Abstract:

In the age of Industry 4.0, data exchange between different organizations is an essential prerequisite to add more value to data and to develop modern business models. However, we have to solve several challenges to facilitate a secure and trustworthy data exchange between different organizations. Data sovereignty is a key success factor for data-driven business models. In the Industrial Data Space, we provide solutions to realize a secure and trustworthy data exchange as well as data sovereignty.

This paper describes results of a Covid-19 contact tracing app measurement study carried out on a standard design of European commuter tram. The measurements indicate that in the tram there is little correlation between Bluetooth received signal strength and distance between handsets. The paper applies the detection rules used by the Italian, Swiss and German apps to measurement data and also characterised the impact on performance of changes in the parameters used in these detection rules.

One of the biggest concerns, given data is an intangible and valuable asset, is how business models can be structured so that stewards remain incentivized to serve the interests of individuals and communities, and do not use the data for their own ends. Data is a complex resource, and to understand potential revenue models of a steward, this paper studies resources or assets which embody the complexity of data and the accompanying ethical issues of protection.
You may also want to have a look at "Understanding data stewardship: taxonomy and use cases" from the same source.

Proximity tracing apps have been proposed as an aide in dealing with the COVID-19 crisis. Some of those apps leverage attenuation of Bluetooth beacons from mobile devices to build a record of proximate encounters between a pair of device owners. The underlying protocols are known to suffer from false positive and re-identification attacks. We present evidence that the attacker's difficulty in mounting such attacks has been overestimated.

Abstract: With the billions of Internet of Things devices connected via the 5G and other networks, loads of useful data are produced. However, the majority of these data are disappearing into the silos of cloud and IoT companies. This problem is exacerbated by the current economic system creating perverse incentives that push companies to keep their data private and not to sell or share them. From the society point of view, this leads to severe inefficiencies. More structurally, Adam Smith's invisible hand does not work: in the data markets, the public and private interests are not aligned by the current market forces.
Based on these observations, we present a conjecture wherein we state that any attempts to fix the market failure in the data markets within the current economic structures are bound to be inefficient. Only by redefining fundamental economic concepts, such as ownership and money, we can efficiently align the interests, clear the markets, and gain welfare potential. Furthermore, we briefly suggest an urban community currency experiment wherein this conjecture could be empirically tested.

A quantitative study of differences in COVID-19–related internet uses and outcomes among the general population.

The European Medical Writers Association (EMWA) invited MyData to write a feature article as part of their June 2020 Data Economy edition. With a heavy academic and health theme, Casandra from the health data thematic group took up the request to bring together experts from the community on writing a MyData article. 14 individuals from 9 countries contributed to shape the article around the 6 MyData principles drawing from real-world scenarios. The publication points to actionable areas for policy makers, healthcare stakeholders, and medical communicators to advocate for data rights. The article is open-access and publicly available on the EMWA journal website.

Paul-Olivier wrote a paper outlining the privacy and security risks associated to the Google/Apple Exposure Notification API. This is very Swiss centric but applicable to many different countries of course.

Privacy policies inform on personal data collection and processing practices, allowing people to make informed decisions about agiven service. However, they are difficult to understand due to theirlength and use of legal terminology. To address this issue, regulatory bod-ies propose the use of graphical representations for privacy policies. Thispaper reviews the development of current graphical and iconified repre-sentations for privacy policies

How should democracies effectively compete against authoritarian regimes in the AI space? This report offers a “terrain strategy” for the United States to leverage the malleability of artificial intelligence to offset authoritarians' structural advantages in engineering and deploying AI.

Ruoss et al. published the first method to train AI systems with mathematically provable certificates of individual fairness. Full source code is available on Github.

Study of people across six countries finds German users would charge most for sharing personal data. This article is based on this paper: How Much is Privacy Worth Around the World and Across Platforms?.

Commerce in some data is, and should be, limited by the law because some data embody values and interests (in particular, human dignity) that may be detrimentally affected by trade.

The not-so-secret ingredient that underlies all successful Artificial Intelligence / Machine Learning (AI/ML) methods is training data. There would be no facial recognition, no targeted advertisements and no self-driving cars if it was not for large enough data sets with which those algorithms have been trained to perform their tasks. Given how central these data sets are, important ethics questions arise: How is data collection performed? And how do we govern its' use? This chapter – part of a forthcoming book – looks at why new data governance strategies are needed; investigates the relation of different data governance models to historic consent approaches; and compares different implementations of personal data exchange models.

Commerce in some data is, and should be, limited by the law because some data embody values and interests (in particular, human dignity) that may be detrimentally affected by trade. In this article, drawing on the Roman law principles regarding res extra commercium, we investigate the example of personal data as regulated under the EU Charter and the GDPR. We observe that transactions in personal data are not forbidden but subject to what we call a dynamically limited alienability rule. This rule is based on two dynamic variables: the nature of data and the legal basis for commercially trading such data (at primary or secondary level).

Despite current ads and slogans, the world doesn’t change one person at a time. It changes as networks of relationships form among people who discover they share a common cause and vision of what’s possible.

This article examines the evolution of the ‘Barcelona Model’ of urban transformation through the lenses of worlding and provincialising urbanism. We trace this evolution from an especially dogmatic worlding vision of the smart city, under a centre-right city council, to its radical repurposing under the auspices of a municipal government led, after May 2015, by the citizens’ platform Barcelona en Comú.

Data-driven research is rapidly becoming mainstream across different disciplines in academia and in investigative journalism. One of the key challenges researchers often struggle with is how to obtain good data.

Reframing ‘my data’ to ‘data about me’ is a really helpful way to move beyond the simplistic, dualistic and dangerous notion of personal data as a monetisable resource towards a recognition of the inseparable nature of data and self. Another perspective provides this tweet that we are more impacted by other people’s data than we are by data about us. And Viivi recommended this article that explains the difference between market participation and right-based pariticpation.

This article introduces the tenets of a theory of datafication of and in the Souths. It calls for a de-Westernization of critical data studies, in view of promoting a reparation to the cognitive injustice that fails to recognize non-mainstream ways of knowing the world through data.

Jodi Forlizzi from the Carnegie Mellon University writes 'If I could wave a magic wand, I would use it to make the HCI community move beyond user-centered design to a notion of stakeholder-centered design.'

No doubt unintentionally, the debate about return of results has taken on a striking resemblance to the 1520–1547 debate about translation of the English vernacular Bible. (Very US law but the section on the Bible is great.)

Data activism has emerged as a response to asymmetries in how data and the means of knowledge production are distributed. This article examines MyData, a data activism initiative developing principles for a new technical and commercial ecosystem in which individuals control the use of personal data.

Delacroix and Lawrence emphasize the limits inherent in an ownership approach to data: at most, data ownership confers the kind of access rights that are similar to water rights.

An awesome piece of research on Indigenous Data by Dr Tahu Kukutai and Donna Cormack.

Data activism has emerged as a response to asymmetries in how data and the means of knowledge production are distributed. This article examines MyData, a data activism initiative developing principles for a new technical and commercial ecosystem in which individuals control the use of personal data.

AI is increasingly being embedded in our lives, supplementing our pervasive use of digital technologies. But this is being accompanied by disquiet over problematic and dangerous implementations of AI, or indeed, even AI itself deciding to do dangerous and problematic actions, especially in fields such as the military, medicine and criminal justice.

Interesting paper highlighting the different reconfiguration of the notion of privacy taking place under various technical schemes: blockchain, multiparty computation, fully homomorphic encryption, differential privacy