The value of trajectory data lies mainly in the spatio-temporal correlation. However, the existing privacy protection methods ignore the spatio-temporal correlation of trajectory data, resulting in a large error in trajectory proportion estimation and Top-K classification. For the privacy of truck trajectory in intelligent logistics, the location and trajectory data perturbation method based on quadtree indexing is proposed, which leverages location generalization and local differential privacy techniques. Our proposed algorithms are suitable for datasets with a large sample space and can protect the trajectory privacy of truck drivers while preserving the strong correlation between adjacent spatio-temporal nodes in the trajectory. The results of simulation on a real trajectory dataset show that the proposed methods not only meet the trajectory privacy requirements of users but also have a good performance in trajectory proportion estimation and Top-K classification.

In the case where the private data is not equally important, personalized local privacy preservation based on randomized response (RR) is studied in the collection of sensitive data. So far, the existing RR mechanisms for multiple discrete private sources, which are termed as conventional randomized response (CRR) mechanisms, focus on a universal approach that exerts the same amount of privacy preservation for all sensitive values, without catering for their concrete privacy requirements. An immediate consequence is that they may be offering insufficient protection to a subset of data contributors with relatively higher privacy requirements, while applying excessive privacy control to another subset with relatively lower privacy requirements. Motivated by this, a novel perturbation framework, which is termed as personalized randomized response (PRR) mechanism, is proposed to achieve personalized privacy preservation (Personalized-PP) by designing the statistical privatization mechanism for multiple sensitive values. The proposed PRR technique introduces the weights for different sensitive values according to their sensitivity, and then introduces the weights into the decision of PRR by considering the concrete requirements for privacy, and thus, attains a higher data utility with respect to the quality of statistics while guaranteeing Personalized-PP. The estimate error of the private distribution is used to measure the quality of statistics for the two RR mechanisms. Theoretical study shows that the estimate error of PRR mechanism is smaller than that of the CRR mechanism for a certain same subjective privacy leakage degree. In particular, simulation results reveal the circumstances where CRR mechanism fails to provide Personalized-PP, and then establish the superiority of PRR mechanism.

A rate adaption scheme is given at the receiver using rateless codes and log‐likelihood ratio (LLR) threshold‐based adaptive demodulation (ADM). The received bits with LLR absolute values higher than the preset threshold are demodulated by the receiver, otherwise, deleted. Through the theoretical analysis of the average mutual information (MI) of the demodulated bits, the method is obtained to calculate the LLR demodulation threshold under the required error performance after decoding and the constraint of codeword length for decoding. Then, the rate adaption scheme is applied in a parallel multiple relay wireless communication system. The LLR threshold is set according to the instantaneous SNR of the received signals in order to keep the average MI per demodulated bit of each relay link the same. The demodulated bits coming from all links are combined for decoding. The 256‐QAM constellation and Raptor code are then considered as a special case to design a sample scheme. Simulation results prove the correctness of the theoretical analysis and the feasibility of the proposed scheme, and when it is employed by a multi‐relay transmission system, the diversity gain can be obtained, and the anti‐fading and the anti‐jamming abilities of the system are promoted.