With the development of wireless communication technology and high-precision mobile positioning technology， Internet of Vehicles （IoV） has become deeply embedded in everyday life. While IoV brings convenience to people， it also brings privacy risks. Typically， in IoV， vehicle driving information interacts with information of other vehicles and infrastructure in real time. During the interaction process， privacy issues such as the leakage of sensitive information may occur. Firstly， the location privacy architecture and privacy risks of IoV were introduced. Secondly， the dynamic noise allocation mechanism， multi-dimensional differential privacy trajectory protection and data perturbation technology in differential privacy were presented. Thirdly， the spatial generalization based on anonymization and the K-anonymity， as well as the asymmetric encryption， symmetric encryption， and homomorphic encryption of encryption mechanism were introduced. Finally， the advantages， disadvantages， limitations and other aspects of differential privacy， anonymity， and encryption mechanisms were analyzed and evaluated.

The illegal-cash-advance is one of the major fraud risks of the credit-card industry. There is almost no difference between single illegal-cash- advance transaction and the normal one, so it could not distinguish them based on different characteristics. To detect the illegal-cash-advance accounts automatically and accurately, the authors picked up correlative variables first, then made a business analysis, and took advantage of the nonlinear curve feature—the one defection of Logistic, and overcame the sensitivity of the multidimensional relativity between independent variables — the another defection, at last computed the weight of coefficient, and constructed a credit scoring model. The applications indicate that the accuracy of the model has achieved 82.72%.