**1. Introduction**

Recent scandals have shown the harm that current data collection, storage and sharing practices can cause with regard to the misuse of personal data [1,2]. As the world is becoming more "smart", so-called smart environments, of which smart cities [3] stand out the most, have in common the ability to transform data (in particular, personal data) into meaningful information needed by the liveness of the ecosystem they generate. Based on this transformation, indeed, they provide services that are becoming more and more targeted towards individuals. For instance, it is commonly known that personal information is used to recommend opportunities to individuals and to make their life easier. However, entities that control these data might not always operate with the aim of social good [4]. Many Big Tech companies rely on data collected about their users, usually storing this personal information in corporate databases, i.e., data silos, and transacting it to third parties with not enough transparency for individuals.

Meanwhile, among the many technologies used for general-purpose data management and storage, Distributed Ledger Technologies (DLTs) are rising up as powerful tools for avoiding control centralization. DLT and the realm of decentralized systems, such as Decentralized File Storages (DFS), that are emerging as solutions able to tackle the issue of obtaining large amounts of data that are not of dubious or of false origin, while providing more disintermediated processes [5,6]. DLTs, in this context, provide a new way of handling personal data, such as recording, storage and transfer. This can be carried out in combination with cryptographic schemes to ensure data confidentiality. By their

**Citation:** Zichichi, M.; Ferretti, S.; Rodríguez-Doncel, V. Decentralized Personal Data Marketplaces: How Participation in a DAO Can Support the Production of Citizen-Generated Data. *Sensors* **2022**, *22*, 6260. https://doi.org/10.3390/s22166260

Academic Editors: Pietro Manzoni, Claudio Palazzi and Ombretta Gaggi

Received: 25 May 2022 Accepted: 16 August 2022 Published: 20 August 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

decentralized nature, indeed, these technologies have the potential to make processes more democratic, transparent and efficient [7]. DLTs and DFS can support the creation of a Personal Information Management System (PIMS) based on decentralized data processing and Personal Data Stores (PDS) [8,9]. In PIMS, data access is granted in line with user policies and these ones, in a decentralized scenario, can be determined by the user via DLTs and smart contracts [8]. PIMS have been proposed by scholars [9–11] or companies [12] and are increasingly gaining attention from policymakers who currently consider mechanisms for regulating and advancing data intermediation services in general [7,8,13,14]. In the context of the European Union's General Data Protection Regulation (GDPR) [15], PIMSs enforce the right of individuals to know the data collected about them and the right to transfer data to other service providers, i.e., data portability. Such features enable the process of moving the data sovereignty towards users, i.e., Self-Sovereign Identity [16], and of providing them more influence over access control, while allowing anyone else to be able to consume this data with transparency. All of this paves the way towards the use of personal data for open data markets and for social good. The ability to easily obtain personal data has the potential to create a marketplace where users are consumers and providers at the same time. By creating a common, decentralized and trustless infrastructure, such as a decentralized personal data marketplace, it will be possible for data owners and consumers to interact and collaborate in Peer-to-Peer (P2P) transactions [17,18]. This means facilitating the transactions of data between owners and consumers without the need for a trusted third-party broker, enabling liquid data markets [19].

The underlying research questions we aim to explore in this work are as follows:

