Quantifying Location Privacy Leakage from Transaction Prices

Large-scale datasets of consumer behavior might revolutionize the way we gain competitive advantages and increase our knowledge in the respective domains. At the same time, valuable datasets pose potential privacy risks that are difficult to foresee. In this paper we study the impact that the prices from consumers’ purchase histories have on the consumers’ location privacy. We show that using a small set of low-priced product prices from the consumers’ purchase histories, an adversary can determine the country, city, and local retail store where the transaction occurred with high confidence. Our paper demonstrates that even when the product category, precise time of purchase, and currency are removed from the consumers’ purchase history (e.g., for privacy reasons), information about the consumers’ location is leaked. The results are based on three independent datasets containing thousands of low-priced and frequently-bought consumer products. In addition, we show how to identify the local currency, given only the total price of a consumer purchase in a global currency (e.g., in Bitcoin). The results show the existence of location privacy risks when releasing consumer purchase histories. As such, the results highlight the need for systems that hide transaction details in consumer purchase histories

On Responsible Machine Learning Datasets with Fairness, Privacy, and Regulatory Norms

Artificial Intelligence (AI) has made its way into various scientific fields, providing astonishing improvements over existing algorithms for a wide variety of tasks. In recent years, there have been severe concerns over the trustworthiness of AI technologies. The scientific community has focused on the development of trustworthy AI algorithms. However, machine and deep learning algorithms, popular in the AI community today, depend heavily on the data used during their development. These learning algorithms identify patterns in the data, learning the behavioral objective. Any flaws in the data have the potential to translate directly into algorithms. In this study, we discuss the importance of Responsible Machine Learning Datasets and propose a framework to evaluate the datasets through a responsible rubric. While existing work focuses on the post-hoc evaluation of algorithms for their trustworthiness, we provide a framework that considers the data component separately to understand its role in the algorithm. We discuss responsible datasets through the lens of fairness, privacy, and regulatory compliance and provide recommendations for constructing future datasets. After surveying over 100 datasets, we use 60 datasets for analysis and demonstrate that none of these datasets is immune to issues of fairness, privacy preservation, and regulatory compliance. We provide modifications to the ``datasheets for datasets" with important additions for improved dataset documentation. With governments around the world regularizing data protection laws, the method for the creation of datasets in the scientific community requires revision. We believe this study is timely and relevant in today's era of AI.Comment: corrected typo

Censorship-Resilient and Confidential Collateralized Second-Layer Payments

Permissionless blockchains are too slow for applications like point-of-sale payments. While several techniques have been proposed to speed up blockchain payments, none of them are satisfactory for application scenarios like retail shopping. In particular, existing solutions like payment channels require users to lock up significant funds and schemes based on pre-defined validators enable easy transaction censoring. In this paper, we develop Quicksilver, the first blockchain payment scheme that works with practical collaterals and is fast, censorship-resilient, and confidential at the same time.We implement Quicksilver for EVM-compatible chains and show that censoring-resilient payments are fast and affordable on currently popular blockchains platforms like Ethereum and Polygon