Privacy Probability Computation for Privacy-Preserving Statistical Analysis Under Multisource Real-Valued Data

Privacy-preserving statistical analysis (PSA) over distributed datasets is essential for achieving a balance between data utility and privacy protection. This process requires supporting diverse queries on sensitive data while ensuring both data and queries confidentiality. However, the existing Privacy Probability Computation (PPC) methods are typically restricted to discrete point queries due to their reliance on interpolation techniques. Consequently, how to effectively support range queries