Decentralized learning (DL) allows IoT devices to exchange local model updates with only their neighboring devices instead of sending their model updates to a central server for aggregation. However, current DL frameworks cannot support the emerging Social IoT(SIoT) paradigm since SIoT devices exchange model updates with only social neighbors based on specific social relations (e.g., ownership and parental relationships). Conversely, sharing model updates with non-social neighbors can improve training performance but may violate social relations. Differential privacy (DP) is thus engaged with DL to ensure data security, while excessive devices engaging DP may downgrade the training performance. However, most research neglects the effect of neighbor selection for each device based on social networks, physical networks, and DP. Therefore, in this paper, we explore the non-trivial relation among the above factors to present a DL framework, DeepPrivacy, and prove its convergence rate and DP. Then, we formulate a novel optimization problem, CoTOPO, to find an efficient communication topology 1 for model updates exchange among devices in DL, and propose an algorithm, AutoTag, for CoTOPO. Last, experiment results manifest that DeepPrivacy and AutoTag combined outperform the state of the art in terms of convergence rate and physical training time significantly on CIFAR10 and FMNIST.