Privacy Protection, Measurement Error, and the Integration of Remote Sensing and Socioeconomic Survey Data
When publishing socioeconomic survey data, survey programs implement a variety of statistical methods designed to preserve privacy but which come at the cost of distorting the data. We explore the extent to which spatial anonymization methods to preserve privacy in the large-scale surveys supported by the World Bank Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) introduce measurement error in econometric estimates when that survey data is integrated with remote sensing weather data. Guided by a pre-analysis plan, we produce 90 linked weather-household datasets that vary by the spatial anonymization method and the remote sensing weather product. By varying the data along with the econometric model we quantify the magnitude and significance of measurement error coming from the loss of accuracy that results from protect privacy measures. We find that spatial anonymization techniques currently in general use have, on average, limited to no impact on estimates of the relationship between weather and agricultural productivity. However, the degree to which spatial anonymization introduces mismeasurement is a function of which remote sensing weather product is used in the analysis. We conclude that care must be taken in choosing a remote sensing weather product when looking to integrate it with publicly available survey data.
Code (0)
등록된 구현이 없습니다.
Tasks
SurveySimilar Papers 제목 키워드 기반
Ghost imaging-based Non-contact Heart Rate Detection
Remote heart rate measurement is an increasingly concerned research field, usually using remote photoplethysmography (rPPG) to collect heart rate information through video data collection. However, in certain specific sc…
An Overview of AI and Blockchain Integration for Privacy-Preserving
With the widespread attention and application of artificial intelligence (AI) and blockchain technologies, privacy protection techniques arising from their integration are of notable significance. In addition to protecti…
De-identificationManagementPrivacy PreservingA Meta-learning Framework for Tuning Parameters of Protection Mechanisms in Trustworthy Federated Learning
Trustworthy Federated Learning (TFL) typically leverages protection mechanisms to guarantee privacy. However, protection mechanisms inevitably introduce utility loss or efficiency reduction while protecting data privacy.…
Federated LearningMeta-LearningA General Pseudonymization Framework for Cloud-Based LLMs: Replacing Privacy Information in Controlled Text Generation
An increasing number of companies have begun providing services that leverage cloud-based large language models (LLMs), such as ChatGPT. However, this development raises substantial privacy concerns, as users' prompts ar…
Text GenerationLeveraging Membership Inference Attacks for Privacy Measurement in Federated Learning for Remote Sensing Images
Federated Learning (FL) enables collaborative model training while keeping training data localized, allowing us to preserve privacy in various domains including remote sensing. However, recent studies show that FL models…
Remote Sensing Image ClassificationScene ClassificationFederated Learning