Parking enforcement is crucial for addressing illegal parking in urban areas. In smart cities, the license plate recognition (LPR) systems have been adopted to enhance parking enforcement by enabling automated monitoring and detection of parking violations. However, the extensive information collection raises public privacy concerns about how the data are processed and stored on a central server. To address the privacy issue during parking enforcement and enable flexible data access control with the user consent, we propose a novel privacy-preserving and access-control-enhanced parking enforcement scheme, where the central server cannot obtain the license plate information of vehicles that follow the parking rules and can provide encrypted evidence for detected violations in case of disputes. Specifically, by utilizing the keyed-hash message authentication code, parking enforcement vehicles can generate a parking record based on the location and the license plate number of a vehicle, which is then used to identify whether there is a parking violation for the vehicle. Moreover, by integrating the designed time-based conditional proxy re-encryption scheme, the distributed key generation technique, and the blockchain technology, a central server can provide encrypted and tamper-proof evidence for violations. The evidence can only be decrypted by the corresponding vehicle owners (VOs), and the owners can grant the decryption permission to a judge when there is a dispute. The security analysis demonstrates that our scheme can achieve the privacy preservation of VOs and consent-based data access control. Simulation results show the efficiency and practicability of the proposed scheme.

Decentralized Finance (DeFi), a pivotal component of the emerging Web3 landscape, is gaining popularity but remains vulnerable to market manipulations, such as wash trading. Wash trading is an illegal practice, where traders buy and sell assets to themselves within cryptocurrency exchanges to artificially inflate trading volumes and distort market perceptions. However, current research primarily focuses on traditional exchanges based on the Order-book mechanism (similar to stock markets), while ignoring the Automated Market Maker (AMM) exchanges, which dominate over 75% of the market and represent a significant innovation within the DeFi. This study utilizes entity recognition technology to detect wash trading on AMM exchanges within Ethereum-like systems, based on the understanding that colluding addresses (perceived as the same entity) must use ETH for transaction fees and exhibit direct or indirect ETH transfer links. We identify wash trading when addresses with transfer connections almost simultaneously buy and sell assets while their total asset holdings remain nearly constant. This comprehensive blockchain network analysis, compared to focusing solely on transactions within exchanges, unveils covert wash trading activities. Our detection method achieves a 95.9% recall and a 96.7% true negative rate in identifying pools affected by wash trading, demonstrating its superiority over existing methods. Furthermore, we apply our method to 98,945 pools from Uniswap V2 & V3 (the most popular AMM exchanges on Ethereum) and identify 1,070,626 abnormal transactions, totaling $27.51 billion in trading volume. Analysis of these transactions uncovers insights into wash traders’ behaviors, including the utilization of multiple addresses and the dual roles of certain addresses as wash traders and liquidity providers. These insights are crucial for developing more effective strategies to combat fraudulent activities in the DeFi ecosystem and enhance financial scrutiny.

Flash Loan, a popular lending service in the decentralized finance (DeFi) ecosystem, allows users to borrow a large number of virtual assets without any collateral. It can be leveraged to support many financial activities (such as arbitrage, collateral swap, self-liquidation, etc.), but unfortunately, it is often abused for malicious intent. One example of abusing flash loan servicing is to simultaneously sell and buy the same crypto currency on the same exchange to mislead the market, aka wash trading. It can manipulate the crypto currency market at a very low cost (anecdotally average around 0.033 ETH gas fee for each transaction on Ethereum mainnet), thereby dramatically damaging the stability and fairness of the market. More seriously, attackers can amplify the market impact by borrowing more assets from Flash Loan platforms. Until now, there has been little attention paid to Flash-Loan-based wash trading, but meanwhile, we have started to witness significant wash trading activities using Flash Loan. In this research, we analyze the properties of Flash-Loan-based wash trading in detail and propose a heuristic-based detection method. The real-world Flash Loan transaction data from Ethereum is used to verify our proposed detection method and more than 6,000 wash transactions were found. Moreover, we analyze the relationship between wash transactions and fluctuations in the price and volume of targeted assets. Finally, we evaluate the cost difference between traditional wash trading and Flash-Loan-based wash trading to reveal the attackers' motivation.