Towards CBDC-based machine-to-machine payments in consumer IoT

Pocher, Nadia; Zichichi, Mirko

doi:10.1145/3477314.3507078

Cited by 9 publications

(5 citation statements)

References 28 publications

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“…Untraceability of payments was among the goals of the Internet of Money (Filippi & Wright, 2018). However, in concrete terms, the latter is populated by many socio-technical notions of anonymity and transparency (Pocher & Zichichi, 2022). Following a holistic interpretation, cryptocurrencies are generated and exchanged within socio-technical systems that, as such, comprise of interdependent technology and human systems (Baxter & Sommerville, 2011;Desmond et al, 2019).…”

Section: Pseudonymity and De-anonymizationmentioning

confidence: 99%

“…Graphs represent a typical mathematical tool to model interactions among different entities: humans, elements of a biological system, computing nodes in a distributed system, and others (Pocher & Zichichi, 2022). In a blockchain, transactions are linked by nature since money spent in a transaction originates from previous transfers (Pocher & Zichichi, 2022). This allows the creation of a graph of transactions that can help the classification process.…”

Section: Transaction Graph Analysismentioning

confidence: 99%

“…The AML/CFT framework consists of a set of laws, regulations and procedures that aim to protect the integrity of the financial system mainly by making the concealment of the origin of illicit profits significantly troublesome (Pocher & Zichichi, 2022). Since the identification of customers and counterparties is a key part of AML/CFT compliance for entities such as financial institutions and cryptoasset service providers, some features of the Internet of Money that hinder identifiability emerge as problematic.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, we employed a dataset obtained from a set of Bitcoin transactions, represented as a directed graph network (Weber et al, 2019). The modeling of Bitcoin transactions as a complex network fosters the use of specific graph-related analysis techniques, which usually help identify peculiar nodes of a network (Pocher & Zichichi, 2022). As per our central hypothesis, since money laundering involves transaction flow relationships between entities creating a graph structure, AML/CFT analytics could benefit from novel graph analysis techniques in machine learning, namely Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT).…”

Section: Introductionmentioning

confidence: 99%

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Detecting anomalous cryptocurrency transactions: An AML/CFT application of machine learning-based forensics

et al. 2023

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In shaping the Internet of Money, the application of blockchain and distributed ledger technologies (DLTs) to the financial sector triggered regulatory concerns. Notably, while the user anonymity enabled in this field may safeguard privacy and data protection, the lack of identifiability hinders accountability and challenges the fight against money laundering and the financing of terrorism and proliferation (AML/CFT). As law enforcement agencies and the private sector apply forensics to track crypto transfers across ecosystems that are socio-technical in nature, this paper focuses on the growing relevance of these techniques in a domain where their deployment impacts the traits and evolution of the sphere. In particular, this work offers contextualized insights into the application of methods of machine learning and transaction graph analysis. Namely, it analyzes a real-world dataset of Bitcoin transactions represented as a directed graph network through various techniques. The modeling of blockchain transactions as a complex network suggests that the use of graph-based data analysis methods can help classify transactions and identify illicit ones. Indeed, this work shows that the neural network types known as Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) are a promising AML/CFT solution. Notably, in this scenario GCN outperform other classic approaches and GAT are applied for the first time to detect anomalies in Bitcoin. Ultimately, the paper upholds the value of public–private synergies to devise forensic strategies conscious of the spirit of explainability and data openness.

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Section: Pseudonymity and De-anonymizationmentioning

confidence: 99%

Section: Transaction Graph Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detecting anomalous cryptocurrency transactions: An AML/CFT application of machine learning-based forensics

et al. 2023

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“…The execution of the same protocol, i.e., source code, guarantees (most of the time) the property of being tamper-proof and not forgeable. This allows for a trust mechanism to be created without the need for third-party intermediaries [29,30].…”

Section: Distributed Ledger Technology (Dlt)mentioning

confidence: 99%

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

Zichichi

Ferretti

Rodrı́guez-Doncel

2022

Sensors

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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.

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