The proliferation of various devices in our daily lives generates an immense volume of data, necessitating significant storage space and resources. Cloud storage systems are required to manage and meet the storage demands of large-scale data, such as big data. However, cloud storage systems come with security concerns, including the potential exposure of sensitive personal information during user access, threats of data tampering, and data breaches. This paper proposes a system that stores the hash value of cloud data on the blockchain for verification purposes to thwart the risk of data tampering, thereby ensuring data reliability. The proposed system is designed to grant access permissions through smart contracts to data uploaded to the cloud, allowing data owners to control access. It effectively mitigates the risks of keylogger attacks, data leakage, and breaches in cloud storage systems. In this paper, it was designed to be easily verifiable and reliable for any cloud storage and was applied to Dropbox, IPFS, etc., to measure the performance of each cloud storage and the verification performance of the proposed system. For KB-size uploads, IPFS performed up to about 10 times better than the comparable storage, and for downloads, Dropbox performed up to about 1.8 times better than the comparable storage. However, no matter which storage the proposed verification process is applied to, it showed consistent and fast verification performance with an average of 131 ms. In other words, this paper provides factors for selecting appropriate storage according to file size and user requirements and proposes that the verification system can be applied to various storage options, contributing to the development of a more secure storage system with low latency.

While measuring socioeconomic indicators is critical for local governments to make informed policy decisions, such measurements are often unavailable at fine-grained levels like municipality. This study employs deep learning-based predictions from satellite images to close the gap. We propose a method that assigns a socioeconomic score to each satellite image by capturing the distributional behavior observed in larger areas based on the ground truth. We train an ordinal regression scoring model and adjust the scores to follow the common power law within and across regions. Evaluation based on official statistics in South Korea shows that our method outperforms previous models in predicting population and employment size at both the municipality and grid levels. Our method also demonstrates robust performance in districts with uneven development, suggesting its potential use in developing countries where reliable, fine-grained data is scarce.

High-resolution daytime satellite imagery has become a promising source to study economic activities. These images display detailed terrain over large areas and allow zooming into smaller neighborhoods. Existing methods, however, have utilized images only in a single-level geographical unit. This research presents a deep learning model to predict economic indicators via aggregating traits observed from multiple levels of geographical units. The model first measures hyperlocal economy over small communities via ordinal regression. The next step extracts district-level features by summarizing interconnection among hyperlocal economies. In the final step, the model estimates economic indicators of districts via aggregating the hyperlocal and district information. Our new multi-level learning model substantially outperforms strong baselines in predicting key indicators such as population, purchasing power, and energy consumption. The model is also robust against data shortage; the trained features from one country can generalize to other countries when evaluated with data gathered from Malaysia, the Philippines, Thailand, and Vietnam. We discuss the multi-level model's implications for measuring inequality, which is the essential first step in policy and social science research on inequality and poverty.

Reliable and timely measurements of economic activities are fundamental for understanding economic development and designing government policies. However, many developing countries still lack reliable data. In this paper, we introduce a novel approach for measuring economic development from high-resolution satellite images in the absence of ground truth statistics. Our method consists of three steps. First, we run a clustering algorithm on satellite images that distinguishes artifacts from nature (siCluster). Second, we generate a partial order graph of the identified clusters based on the level of economic development, either by human guidance or by low-resolution statistics (siPog). Third, we use a CNN-based sorter that assigns differentiable scores to each satellite grid based on the relative ranks of clusters (siScore). The novelty of our method is that we break down a computationally hard problem into sub-tasks, which involves a human-in-the-loop solution. With the combination of unsupervised learning and the partial orders of dozens of urban vs. rural clusters, our method can estimate the economic development scores of over 10,000 satellite grids consistently with other baseline development proxies (Spearman correlation of 0.851). This efficient method is interpretable and robust; we demonstrate how to apply our method to both developed (e.g., South Korea) and developing economies (e.g., Vietnam and Malawi).