Decentralized Personal Data Marketplaces: How Participation in a DAO Can Support the Production of Citizen-Generated Data.

Big Tech companies operating in a data-driven economy offer services that rely on their users' personal data and usually store this personal information in "data silos" that prevent transparency about their use and opportunities for data sharing for public interest. In this paper, we present a solution that promotes the development of decentralized personal data marketplaces, exploiting the use of Distributed Ledger Technologies (DLTs), Decentralized File Storages (DFS) and smart contracts for storing personal data and managing access control in a decentralized way. Moreover, we focus on the issue of a lack of efficient decentralized mechanisms in DLTs and DFSs for querying a certain type of data. For this reason, we propose the use of a hypercube-structured Distributed Hash Table (DHT) on top of DLTs, organized for efficient processing of multiple keyword-based queries on the ledger data. We test our approach with the implementation of a use case regarding the creation of citizen-generated data based on direct participation and the involvement of a Decentralized Autonomous Organization (DAO). The performance evaluation demonstrates the viability of our approach for decentralized data searches, distributed authorization mechanisms and smart contract exploitation.

PHDD: Corpus of Physical Health Data Disclosure on Twitter During COVID-19 Pandemic.

Health-related information is considered as 'highly sensitive' by the European General Data Protection Regulations (GDPR) and determining whether a text document contains health-related information or not is of interest for both individuals and companies in a number of different scenarios. Although some efforts have been made to detect different categories of personal data in texts, including health information, the classification task by machines is still challenging. In this work, we aim to contribute to solving this challenge by building a corpus of tweets being shared in the current COVID-19 pandemic context. The corpus is called PHDD(Corpus of Physical Health Data Disclosure on Twitter During COVID-19 Pandemic) and contains 1,494 tweets which have been manually tagged by three taggers in three dimensions: health-sensitivity status, categories of health information, and subject of health history. Furthermore, a lightweight ontology called PTHI (Privacy Tags for Health Information), which reuses two other vocabularies, namely hl7 and dpv, is built to represent the corpus in a machine-readable format. The corpus is publicly available and can be used by NLP experts for implementation of techniques to detect sensitive health information in textual documents.