Privacy-Preserving Data Completion With Location and Value Obfuscation in Spatial Crowdsourcing

Spatial-temporal data is essential for various applications. Traditional data collection methods often have limited coverage, while Spatial Crowdsourcing, which leverages mobile devices, offers broader data acquisition but also presents challenges with data sparsity. To infer missing data based on observed data, existing data completion techniques often require detailed spatial-temporal information, raising privacy concerns. Some methods attempt to protect privacy by obfuscating locations; however, this can disrupt the original spatial-temporal correlations, leading to increased errors in data completion. To address this, we propose a differential privacy-based obfuscation strategy that safeguards privacy while maintaining data accuracy. Our approach employs a prediction model to extract an importance matrix and a relationship matrix from historical data. These matrices are utilized to map actual user locations to more beneficial locations for data completion, followed by value adjustments to maintain availability and preserve spatial-temporal correlations. We demonstrate that our mechanism satisfies differential privacy that provides effective privacy guarantees. Evaluations of five real-world datasets show that our obfuscation mechanism outperforms other baseline methods at the same level of privacy protection.