A new model for differential privacy, termed trust graph DP (TGDP), is proposed to accommodate varying levels of trust among users in data-sharing scenarios. This model interpolates between central and local differential privacy, allowing for more nuanced privacy controls while providing algorithms and error bounds for aggregation tasks based on user relationships. The approach has implications for federated learning and other applications requiring privacy-preserving data sharing.