Balanced clustering aims to partition data into cohesive groups while satisfying size constraints. It is important in applications such as load balancing, resource allocation, and capacity-aware data analysis. In practice, structural quality and size balance often compete, which makes balanced clustering difficult for methods that commit to a single operating point. To address this issue, we propose Pairwise Multi-Objective Evolutionary Balanced Clustering (P-MOEBC), a permutation-invariant evolutionary framework that searches for a diverse set of structure-balance trade-offs. We formulate the task with a pairwise graph-cut objective and a flexible balance-violation objective, and then design two label-free operators tailored to this formulation: (1) a Consensus Block Crossover that recombines reliable co-membership structures without label alignment, and (2) a Balance-Aware Mutation with exact local updates that evaluates candidate moves in O(knn) time. Experiments on synthetic data show that scalarized baselines can be sensitive to penalty selection, while real-world benchmarks show that P-MOEBC is competitive across strict and relaxed feasibility regimes and remains scalable. Code is available at https://github.com/zjh308/P-MOEBC.

Wenhao Liu, Xiaohua Wang, Muling Wu, Tianlong Li, Changze Lv, Zixuan Ling, Zhu JianHao, Cenyuan Zhang, Xiaoqing Zheng, Xuanjing Huang. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Muling Wu, Wenhao Liu, Xiaohua Wang, Tianlong Li, Changze Lv, Zixuan Ling, Zhu JianHao, Cenyuan Zhang, Xiaoqing Zheng, Xuanjing Huang. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Laconic Private Set Intersection (LPSI) is a type of PSI protocols characterized by the requirement of only two-round interactions and by having a reused message in the first round that is independent of the set size. Recently, Aranha et al. (CCS'2022) proposed a LPSI protocol that utilizes the pairing-based accumulator. However, this protocol heavily relies on time-consuming bilinear pairing operations, which can potentially cause a bottleneck. Furthermore, in certain scenarios like contact tracing, it is sufficient to only reveal the intersection cardinality. To tackle this problem and expand on its functionalities, we introduce a cloud-assisted two-party LPSI cardinality (TLPSI-CA) that inherits the properties of LPSI. Interestingly, the cloud-assisted TLPSI-CA eliminates the direct interaction between the sender and receiver, enabling the sender's message to be reused across any number of protocol executions. Besides, we further extend it to the multi-party scenario, which also possesses laconic properties. Then, we prove the two protocols' security in achieving the defined ideal functionalities. Finally, we evaluate the performance of both protocols and find that TLPSI-CA successfully reduces the local computation costs for participants. Additionally, the multi-party protocol performs similarly to TLPSI-CA, with the exception of the higher communication costs incurred by the receiver.

Mobile cloud computing (MCC) integrates the advantages of mobile networks and cloud computing, enabling users to enjoy personalized services without constraints and restrictions of time and place. While this brings convenience, it also comes with risks such as privacy breaches and unauthorized access to outsourced data. Bilateral access control is a promising technique for addressing these issues. However, the current bilateral access control schemes cannot solve problems such as single point failure. To further enhance and enrich the existing schemes, we propose hierarchical bilateral access control. In the proposed scheme, the permission of generating encryption keys and decryption keys can be delegated to its child nodes, which alleviates the computation and communication overheads of the parent nodes and weaken the potential risks of single-point failure. Additionally, the ciphertext size remains constant, reducing the costs of transmitting and storing ciphertext and relieving resource limitations on devices. We then prove the privacy and authenticity of the scheme in the random oracle model. Finally, the comprehensive performance comparison and analysis demonstrate the efficiency of the proposed scheme.

Mobile crowdsensing (MCS) utilizes ubiquitous mobile devices to collect massive amounts of data and offer various high-quality services. During the data collection and upload process, bilateral access control is implemented to recruit qualified data providers and prevent unauthorized access to collected data. However, the efficiency of existing bilateral access control schemes applicable in the data collection phase is dissatisfactory, as their ciphertext sizes are linear with the number of attributes. Additionally, data confidentiality and authenticity, as well as lightweight encryption and decryption processes, are crucial for the deployment of MCS since the former eliminate the risks of data abuse and false data injection, and the latter are typically limited in their computation and communication resources. To reduce the resource consumption of these devices, we present EBAC-CC, an efficient bilateral access control with constant-size ciphertexts that ensures data confidentiality and authenticity and allows for flexible threshold bilateral access control. Besides, offline/online techniques and outsourced decryption are employed to quickly generate ciphertexts and recover perceptual data, which also alleviates their computation burdens. We also prove its privacy and authenticity in the standard model and evaluate its efficacy theoretically and experimentally, demonstrating its superiority over other bilateral access control schemes.