Compressing Time Series Towards Lightweight Integrity Commitments

In the current era of big data, the generation and preservation of time series data has seen widespread adoption in various sectors such as trade, healthcare, and industry. At the same time, data have become a prized asset, as we are witnessing an unprecedented explosion in the volume of data generated and collected. This includes data from various sources, such as sensors, social media, and transactions, offering a competitive edge to organizations that harness the power of data, leading them to informed decisions and rapid responses to changing conditions. However, it is essential to responsibly manage data, addressing privacy and security concerns to ensure their integrity and availability, while efficiently managing the cost of storage and operations. To address these challenges, there is a compelling de-mand for methods that compress these time series while ensuring that the compressed versions faithfully represent the original data and remain secure against tampering or unauthorized alterations. This paper introduces a novel approach to condensing time-series data into lightweight integrity commitments, leveraging polynomial vector commitments as a key component. These integrity commitments are integrated with blockchain technology, serving as a cornerstone in guaranteeing data integrity and authenticity, particularly in supply chain scenarios where trust and transparency are of the utmost importance.