To address the issues of user privacy leakage and message authentication in Vehicular Ad hoc NETwork （VANET）， an authenticatable privacy-preserving scheme based on signcryption from lattice was proposed. Firstly， the public key of receiver was used to signcrypt the message to generate the ciphertext， and only the receiver with corresponding private key could decrypt the ciphertext， which ensures messages visible only to authorized users. Secondly， after decrypting the message， the receiver calculated the hash value of the message by one-way secure hash function， and judged whether the hash value of the message changed， which realized message authentication. Finally， Number Theoretic Transform （NTT） algorithm was used to reduce the computational overhead of polynomial multiplication and improve the computational efficiency of the scheme. The proposed scheme was proved to have INDistinguishability under Chosen Ciphertext Attack （IND-CCA2） and Strong UnForgeability under Chosen Message Attack （SUF-CMA） under the random oracle model. In addition， the security of the proposed scheme is based on lattice hardness problems， so that it can resist quantum algorithm attack. Simulation experiment results show that the proposed scheme improves the performance in terms of communication delay （at least reducing 10.01%）， message loss rate （at least reducing 31.79%） and communication overhead （at least reducing 31.25%） compared to similar authenticated privacy-preserving schemes and a lattice-based signature scheme. Therefore， the proposed scheme is more suitable for resource-constrained VANETs.

To address the privacy leakage problem caused by the model parameters memorizing some features of the data during the training process of the Convolutional Neural Network （CNN） models， a Gradient Descent with Momentum algorithm based on Differential Privacy in CNN （DPGDM） was proposed. Firstly， the Gaussian noise meeting differential privacy was added to the gradient in the backpropagation process of model optimization， and the noise-added gradient value was used to participate in the model parameter update process， so as to achieve differential privacy protection for the overall model. Secondly， to reduce the impact of the introduction of differential privacy noise on convergence speed of the model， a learning rate decay strategy was designed and then the gradient descent with momentum algorithm was improved. Finally， to reduce the influence of noise on the accuracy of the model， the value of the noise scale was adjusted dynamically during model optimization， thereby changing the amount of noise that needs to be added to the gradient in each round of iteration. Experimental results show that compared with DP-SGD （Differentially Private Stochastic Gradient Descent） algorithm， the proposed algorithm can improve the accuracy of the model by about 5 and 4 percentage points at privacy budget of 0.3 and 0.5， respectively， proving that by using the proposed algorithm， the model usability is improved and privacy protection of the model is achieved.