Wash trading in decentralized markets remains a significant concern magnified by the pseudonymous and public nature of blockchains. In this paper, we introduce an innovative methodology designed to detect wash-trading activities beyond surface-level transactions. Our approach integrates NFT ownership traces with the Ethereum Transaction Network, encompassing the complete historical record of all Ethereum-account normal transactions. By analyzing both networks, our method offers a notable advancement over techniques proposed in existing research. We analyzed the wash-trading activity of 7 notable NFT collections. Our results show that wash trading in unregulated NFT markets is an underestimated concern and is much more widespread in terms of both frequency and volume. Excluding the Meebits collection, which emerged as an outlier, wash trading constitutes up to 24% of the total trading volume. Specifically, for the Meebits collection, 93% of the total trade volume was attributed to wash trading.

Currently, the computational power present in the sensors forming a wireless sensor network (WSN) allows for implementing most of the data processing and analysis directly on the sensors in a decentralized way. This shift in paradigm introduces a shift in the privacy and security problems that need to be addressed. While a decentralized implementation avoids the single point of failure problem that typically applies to centralized approaches, it is subject to other threats, such as external monitoring, and new challenges, such as the complexity of providing decentralized implementations for data mining algorithms. In this paper, we present a solution for privacy-aware distributed data mining on wireless sensor networks. Our solution uses a permissioned blockchain to avoid a single point of failure in the system. Contracts are used to construct an onion-like structure encompassing the Hoeffding trees and a route. The onion-routed query conceals the network identity of the sensors from external adversaries, and obfuscates the actual computation to hide it from internally compromised nodes. We validate our solution on a use case related to an air quality-monitoring sensor network. We compare the quality of our model against traditional models to support the feasibility and viability of the solution.

In the past two decades, technological advancements in smart devices, IoT, and smart sensors have paved the way towards numerous implementations of indoor location systems. Indoor location has many important applications in numerous fields, including structural engineering, behavioral studies, health monitoring, etc. However, with the recent COVID-19 pandemic, indoor location systems have gained considerable attention for detecting violations in physical distancing requirements and monitoring restrictions on occupant capacity. However, existing systems that rely on wearable devices, cameras, or sound signal analysis are intrusive and often violate privacy. In this research, we propose a new framework for indoor location. We present an innovative, non-intrusive implementation of indoor location based on wireless sensor networks. Further, we introduce a new protocol for querying and performing computations in wireless sensor networks (WSNs) that preserves sensor network anonymity and obfuscates computation by using onion routing. We also consider the single point of failure (SPOF) of sink nodes in WSNs and substitute them with a blockchain-based application through smart contracts. Our set of smart contracts is able to build the onion data structure and store the results of computation. Finally, a role-based access control contract is used to secure access to the system.

The large number of devices characterizing Wireless Sensor Networks (WSNs) provide the benefits of observing, tracking, and recording everything; nonetheless, the cumulative computing power of those devices is typically not utilized, and the few implementations taking advantage of it neglect privacy or are application-specific. This manuscript describes a privacy-preserving protocol that enables WSN nodes to jointly compute an arbitrary function without disclosing their own private inputs. The computation takes place at data source nodes, while computation instructions and intermediate results move across the network secured by cryptography. The protocol relies on the Onion Routing technique to provide uniformly distributed network traffic and confine the knowledge a foreign actor can gain from monitoring messages traveling the network. We show that the communication protocol is privacy-preserving against the external and internal attacker models, and we validate our protocol implementation using the NS3 network simulator.

Indoor Air Quality monitoring is a major asset to improving quality of life and building management. Today, the evolution of embedded technologies allows the implementation of such monitoring on the edge of the network. However, several concerns need to be addressed related to data security and privacy, routing and sink placement optimization, protection from external monitoring, and distributed data mining. In this paper, we describe an integrated framework that features distributed storage, blockchain-based Role-based Access Control, onion routing, routing and sink placement optimization, and distributed data mining to answer these concerns. We describe the organization of our contribution and show its relevance with simulations and experiments over a set of use cases.