Recently, active reconfigurable intelligent surfaces (RIS) have attracted much attention due to its ability to amplify signals. The combination of active RIS and unmanned aerial vehicle (UAV) can improve the performance of communication systems. For the multi-objective optimization problem of active RIS assisted UAV communication systems, this work proposes a new low-complexity scheme to maximize system energy efficiency (EE). Unlike previous work, we focus on the impact of UAV hovering altitude on the system's EE and separately investigate the effects of the amplification coefficient and phase shift of active RIS on the system's EE. We also adopt non-orthogonal multiple access (NOMA) technology to improve spectral efficiency, further boosting the system EE. To address challenges such as non-convexity caused by UAV altitude updates and the coupling between active RIS's amplification coefficients and phase shifts, this work proposes a block coordinate descent (BCD) iterative optimization framework that decomposes the problem into three subproblems. By solving each subproblem, the optimal UAV altitude and the amplification coefficients and phase shifts of the active RIS are derived to maximize system EE. Numerical results show that: 1) Compared with other benchmark schemes, the proposed active RIS-NOMA scheme achieves significant improvements in system EE. 2) UAV demonstrates great communication potential in active RIS-NOMA systems, especially when direct links are blocked.

The integration of social information into recommender systems (RSs) has gained significant popularity for enhancing recommendation performance and user experience. However, this practice introduces substantial privacy risks, particularly concerning the leakage of sensitive social relationships. While prior research has primarily focused on user-level and interaction-level privacy risks, the social relation-level privacy risks remain largely unexplored. To fill this gap, we investigate social privacy risks through membership inference attacks (MIA) and propose a Social relation-level MIA (SMIA) framework. Two key challenges arise: (1) the adversary can only access the recommended item IDs, which provide indirect and limited information about social relationships, and (2) extracting socially relevant preferences from recommendation results is inherently difficult. To tackle the first challenge, we leverage shadow models to transform sparse item IDs into dense features, enabling adversaries to effectively utilize recommendation outputs. For the second challenge, SMIA employs a dual-branch learning approach that disentangles social and behavioral preferences. Therefore, we can extract socially relevant signals from the disentangled preferences.Extensive experiments on real-world datasets demonstrate that both social and general RSs are highly vulnerable to such attacks, highlighting the urgent need for robust privacy protection mechanisms. To defend against these attacks, we introduce a Socially Adversarial Learning (SAL) defense mechanism that selectively obscures sensitive social information in user representations during training, effectively reducing privacy leakage. We further evaluate the effectiveness of our defense and discuss future directions for developing privacy-preserving mechanisms in social RSs.

Cloud computing develops rapidly in recent years and provides service to many applications, in which job scheduling becomes more and more important to improve the quality of service. Parallel processing on cloud requires different machines starting simultaneously on the same job and brings processing slowdown due to communications overhead, defined as synchronization constraint and parallel slowdown. This paper investigates a new job scheduling problem of makespan minimization on uniform machines and identical machines with synchronization constraint and parallel slowdown. We first conduct complexity analysis proving that the problem is difficult in the face of adversarial job allocation. Then we propose a novel job scheduling algorithm, United Wrapping Scheduling (UWS), and prove that UWS admits an O(logm)-approximation for makespan minimization over m uniform machines. For the special case of identical machines, UWS is simplified to Sequential Allocation, Refilling and Immigration algorithm (SARI), proved to have a constant approximation ratio of 8 (tight up to a factor of 4). Performance evaluation implies that UWS and SARI have better makespan and realistic approximation ratio of 2 compared to baseline methods United-LPT and FIFO, and lower bounds.