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Nowadays companies are increasingly adopting the technology ofcloud computing. This technology is subject to a lot of research and continuousadvances are made. The use of cloud computing in the companies advantagessuch as: reducing costs,…
Stewards of social science data face a fundamental tension. On one hand, they want to make their data accessible to as many researchers as possible to facilitate new discoveries. At the same time, they want to restrict access to their data…
A Data Ecosystem offers a keystone-player or alliance-driven infrastructure that enables the interaction of different stakeholders and the resolution of interoperability issues among shared data. However, despite years of research in data…
Sharing data can often enable compelling applications and analytics. However, more often than not, valuable datasets contain information of a sensitive nature, and thus, sharing them can endanger the privacy of users and organizations. A…
Technology can pose signicant risks to a wide array of vulnerable populations. However, by addressing the challenges and opportunities in technology design, research, and deployment, we can create systems that benet everyone, fostering a…
This research paper conducts a survey of the various government organizations that are using Web 2.0 to enhance their functionality, interact with potential audience and to reach out to a huge customer base. The aim of the paper is to study…
Data-driven agriculture, which integrates technology and data into agricultural practices, has the potential to improve crop yield, disease resilience, and long-term soil health. However, privacy concerns, such as adverse pricing,…
Distributed ledgers are a new type of database technology that allows open access to data stored across distributed, decentralised, publicly maintained infrastructures. Current implementations of the such ledgers expect competition between…
Society is in constant transition to keep up with technological advancement, we are seeing traditional paradigms being increasingly challenged. The fundamentals of governance are one such paradigm. As society's values have shifted, so have…
Data-centric technologies provide exciting opportunities, but recent research has shown how lack of representation in datasets, often as a result of systemic inequities and socioeconomic disparities, can produce inequitable outcomes that…
Efficient handling of large data-volumes becomes a necessity in today's world. It is driven by the desire to get more insight from the data and to gain a better understanding of user trends which can be transformed into economic incentives…
Data cooperatives offer a new model for fair data governance, enabling individuals to collectively control, manage, and benefit from their information while adhering to cooperative principles such as democratic member control, economic…
Privacy is an increasingly feeble constituent of the present datafied world and apparently the reason for that is clear: powerful actors worked to invade everyone's privacy for commercial and surveillance purposes. The existence of those…
Given that there are a variety of stakeholders involved in, and affected by, decisions from machine learning (ML) models, it is important to consider that different stakeholders have different transparency needs. Previous work found that…
The Web community has introduced a set of standards and technologies for representing, querying, and manipulating a globally distributed data structure known as the Web of Data. The proponents of the Web of Data envision much of the world's…
Research on digital degrowth predominantly critiques digital expansion or presents alternative digital practices. Yet, analyzing the link between digital technologies and complexity is crucial to overcome systemic obstacles hindering…
The systems that operate the infrastructure of cities have evolved in a fragmented fashion across several generations of technology, causing city utilities and services to operate sub-optimally and limiting the creation of new value-added…
The public sector comprises government agencies, ministries, education institutions, health providers and other types of government, commercial and not-for-profit organisations. Unlike commercial enterprises, this environment is highly…
Generating synthetic data through generative models is gaining interest in the ML community and beyond. In the past, synthetic data was often regarded as a means to private data release, but a surge of recent papers explore how its…
Leakage of data from publicly available Machine Learning (ML) models is an area of growing significance as commercial and government applications of ML can draw on multiple sources of data, potentially including users' and clients'…