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Many researchers work on improving the data efficiency of machine learning. What would happen if they succeed? This paper explores the social-economic impact of increased data efficiency. Specifically, we examine the intuition that data…

This paper explores the integration of advanced cryptographic techniques for secure computation in data spaces to enable secure and trusted data sharing, which is essential for the evolving data economy. In addition, the paper examines the…

Individuals lack oversight over systems that process their data. This can lead to discrimination and hidden biases that are hard to uncover. Recent data protection legislation tries to tackle these issues, but it is inadequate. It does not…

Central to privacy concerns is that firms may use consumer data to price discriminate. A common policy response is that consumers should be given control over which firms access their data and how. Since firms learn about a consumer's…

Decarbonization, decentralization and digitalization are the three key elements driving the twin energy transition. The energy system is evolving to a more data driven ecosystem, leading to the need of communication and storage of large…

This paper offers a new privacy approach for the growing ecosystem of services -- ranging from open banking to healthcare -- dependent on sensitive personal data sharing between individuals and third parties. While these services offer…

The prospect of collusive agreements being stabilized via the use of pricing algorithms is widely discussed by antitrust experts and economists. However, the literature is often lacking the perspective of computer scientists, and seems to…

We summarize the fundamental issues at stake in algorithmic trading, and the progress made in this field over the last twenty years. We first present the key problems of algorithmic trading, describing the concepts of optimal execution,…

In recent years, the amount of information collected about human beings has increased dramatically. This development has been partially driven by individuals posting and storing data about themselves and friends using online social networks…

Big data revolutionizes accounting and auditing, offering deep insights but also introducing challenges like data privacy and security. With data from IoT, social media, and transactions, traditional practices are evolving. Professionals…

We study the problem of collaborative machine learning markets where multiple parties can achieve improved performance on their machine learning tasks by combining their training data. We discuss desired properties for these machine…

This paper considers a market for trading Internet of Things (IoT) data that is used to train machine learning models. The data, either raw or processed, is supplied to the market platform through a network and the price of such data is…

Data is the new oil; this refrain is repeated extensively in the age of internet tracking, machine learning, and data analytics. As data collection becomes more personal and pervasive, however, public pressure is mounting for privacy…

The explosive growth of machine learning has made it a critical infrastructure in the era of artificial intelligence. The extensive use of data poses a significant threat to individual privacy. Various countries have implemented…

There is a growing trend regarding perceiving personal data as a commodity. Existing studies have built frameworks and theories about how to determine an arbitrage-free price of a given query according to the privacy loss quantified by…

Online platforms, including social media and search platforms, have routinely used their users' data for targeted ads, to improve their services, and to sell to third-party buyers. But an increasing awareness of the importance of users'…

In the past years we have witnessed the rise of new data sources for the potential production of official statistics, which, by and large, can be classified as survey, administrative, and digital data. Apart from the differences in their…

The exponential growth of collected, processed, and shared data has given rise to concerns about individuals' privacy. Consequently, various laws and regulations have been established to oversee how organizations handle and safeguard data.…

Many ethical issues in machine learning are connected to the training data. Online data markets are an important source of training data, facilitating both production and distribution. Recently, a trend has emerged of for-profit "ethical"…

The escalating influx of data generated by networked edge devices, coupled with the growing awareness of data privacy, has restricted the traditional data analytics workflow, where the edge data are gathered by a centralized server to be…