Call For Papers

Data-driven decision making powered by Machine Learning (ML) algorithms is changing how the society and the economy work and is having a profound positive impact on our daily life. With the exception of very large companies that have both the data and the skills to develop powerful ML-driven services, the large majority of provably possible ML services, from e-health, to transportation and predictive maintenance, to name just a few, still remain at the idea or prototype level for the simple reason that data, the skills to manipulate them, and the business models to bring them to market, seldom co-exist under the same roof. Data has to somehow meet with the ML and business skills that can unleash its full power for the society and economy. This has given rise to a highly dynamic sector around the Data Economy, involving Data Providers/Controllers, data Intermediaries, often-times in the form of Data Marketplaces or Personal Information Management Systems for end-users to control and even monetise their personal data. Despite its huge potential and observed initial growth, the Data Economy is still at its nascent phase and, therefore, faces a yet uncertain future and a series of existential challenges. Such challenges include a broad range of technical matters across multiple disciplines of Computer Science including networks and distributed systems, security and privacy, machine learning and human computer interaction. The mission of the ACM DE workshop will be to bring together all the CS skills required for helping the Data Economy liftoff by addressing a range of technical challenges including, but are not limited, to the ones below:

• Design, architecture, systems and protocols for Data Marketplaces, Data Vendors, and Personal Information Management Systems (PIMS)

• Secure data exchange and delivery mechanisms

• Understanding the value of data in different applications and domains and across the data value chain

• Data pricing mechanisms for individual and aggregated data

• How to buy data – data purchase policies and algorithms

• Protecting data ownership rights for commercial datasets

• NFTs, blockchains, smart contracts and their role in the data economy

• Trusted execution, cloud computing, IPFS and their role in the data economy

• Data representation and exchange standards in the data economy

• Cryptographic approaches, including FHE, SMPC, DP, and their role and limits in the data economy

• UX and HCI challenges for data marketplaces and PIMS

• Consent management and taxonomy of data processing purposes

• Sending the data to the algorithm vs. sending the algorithm to the data

• Federated and distributed learning in the data economy

• Understanding and decoding the Terms & Conditions of data marketplaces and data transactions

• Federation and interoperability standards and protocols in the data economy

• Federated data catalogues and data discovery mechanisms

• Privacy/data protection and the data economy

• Measurement studies related to the data economy

• Large-scale experiments and validation studies for the data economy

• Internet clean-slate proposals for supporting the data economy