Value Before Disclosure: Data Valuation under Local Differential Privacy

Data privacy is a critical concern in data valuation. Assessing a record's task-dependent value before purchase often requires access to potentially sensitive information. We propose PIVOTS, a local differential privacy (LDP) framework that lets providers estimate their contributions without first disclosing their raw records. The buyer uses one locally randomized report from each provider and validation data to construct a broadcast valuation function that each provider evaluates on its retained record to estimate its value. Theoretically, we establish mean-squared-error upper bounds and matching minimax lower bounds when validation data are sufficiently abundant. We also consider settings in which some non-sensitive features remain public, developing a corresponding valuation method and providing theoretical guarantees that quantify the resulting gain in valuation accuracy brought by partial feature disclosure. Experiments on synthetic and real-world data demonstrate accurate valuation under privacy constraints and substantial improvements in downstream task performance.