Differential privacy provides strong privacy preservation guarantee in information sharing. As social network analysis has been enjoying many applications, it opens a new arena for applications of differential privacy. This article presents a comprehensive survey connecting the basic principles of differential privacy and applications in social network analysis. We concisely review the foundations of differential privacy and the major variants. Then, we discuss how differential privacy is applied to social network analysis, including privacy attacks in social networks, models of differential privacy in social network analysis, and a series of popular tasks, such as analyzing degree distribution, counting subgraphs and assigning weights to edges. We also discuss a series of challenges for future work.

Different from the conventional transmit-then-compute scheme, which has been widely considered in mobile edge computing (MEC) networks, we propose a transmit-while-compute scheme to reduce the average latency. Specifically, by means of the partial offloading technique, the computational task is divided into multiple subtasks which are sequentially offloaded to the edge server node (ESN) with short-packet communications. Therefore, the transmission and computation of the task can be executed almost simultaneously. Moreover, to minimize the average latency, we first prove the optimization of corresponding tasks is quasi-convex, then provide an optimal solution to determine the blocklength and the size of subtasks. A low-complexity solution based on the alternating optimization (AO) method is proposed as well. Numerical results show that the average latency of the proposed scheme is very close to the computing time of the task.

In this paper, we investigate a two-user uplink nonorthogonal multiple access (NOMA) short-packet communication system in flat Rayleigh fading channels, which includes a base station, a central user and a cell-edge user. To derive the average block error rate of the system model, unlike the works in the literature where the product term ( $\mathbb {E}[\epsilon _{c}\epsilon _{ee}]$ ) is omitted or set a lower bound, we propose a novel method based on probability density function that works well in the uplink NOMA system. We theoretically derive the asymptotic expression of the product term in uplink NOMA system by using double Riemann integral approximation. It is validated that simulation results are consistent with our theoretical analysis, which demonstrate that our closed-form of average block error rate can accurately measure the performance of short-packet communications in wireless systems.

A large amount of high-dimensional and heterogeneous data appear in practical applications, which are often published to third parties for data analysis, recommendations, targeted advertising, and reliable predictions. However, publishing these data may disclose personal sensitive information, resulting in an increasing concern on privacy violations. Privacy-preserving data publishing has received considerable attention in recent years. Unfortunately, the differentially private publication of high dimensional data remains a challenging problem. In this paper, we propose a differentially private high-dimensional data publication mechanism (DP2-Pub) that runs in two phases: a Markov-blanket-based attribute clustering phase and an invariant post randomization (PRAM) phase. Specifically, splitting attributes into several low-dimensional clusters with high intra-cluster cohesion and low inter-cluster coupling helps obtain a reasonable allocation of privacy budget, while a double-perturbation mechanism satisfying local differential privacy facilitates an invariant PRAM to ensure no loss of statistical information and thus significantly preserves data utility. We also extend our DP2-Pub mechanism to the scenario with a semi-honest server which satisfies local differential privacy. We conduct extensive experiments on four real-world datasets and the experimental results demonstrate that our mechanism can significantly improve the data utility of the published data while satisfying differential privacy.