The diffusion model with multiple conditions has received widespread attention in the field of drug design due to its high-quality generation ability. However, the paradigm of directly generating new molecules from conditions used in existing work has not accurately fitted the joint distribution of multiple conditions during the generation process. To address this issue, we propose Graph Diffusion Evolution Model(GDEM) for multi conditional molecule generation. GDEM decomposes the process of molecular generation into a chain-like Markov evolution process, continuously adjusting the molecular structure and gradually approaching the true multi-conditional joint distribution. Meanwhile, in order to effectively train this chain evolution generative model, we also propose a two-stage training approximation method to complete the training of intermediate steps. We validated the effectiveness of GDEM on multiple polymer datasets and small molecule datasets, and the results showed that GDEM has advantages in molecular properties and condition control compared to traditional methods.

Federated Learning (FL) is a privacy-preserving machine learning paradigm, enabling decentralized devices to collaboratively train models without sharing local data. Traditional FL approaches, however, rely on averaging parameters across clients with homogeneous models, which limits their applicability in scenarios where clients require heterogeneous models. In this paper, we propose FedISMH, a novel approach to address model heterogeneity in FL. Instead of directly applying knowledge distillation, FedISMH clusters clients based on the structural similarities of client models, where clients’ structural features can be extracted through either labeled or unlabeled dataset This allows the proposed model to identify clients with similar model architectures while preserving privacy. Additionally, FedISMH introduces a dynamic mechanism to manage noise clients by aligning them with the most structurally similar clusters, ensuring that their inclusion promote the performance of the cluster. Experimental results on MNIST and SVHN demonstrate that FedISMH consistently outperforms state-of-the-art methods in both IID and Non-IID settings, offering improved accuracy, robustness, and flexibility in heterogeneous FL environments.

Social networks are considered to be heterogeneous graph neural networks (HGNNs) with deep learning technological advances. HGNNs, compared to homogeneous data, absorb various aspects of information about individuals in the training stage. That means more information has been covered in the learning result, especially sensitive information. However, the privacy-preserving methods on homogeneous graphs only preserve the same type of node attributes or relationships, which cannot effectively work on heterogeneous graphs due to the complexity. To address this issue, we propose a novel heterogeneous graph neural network privacy-preserving method based on a differential privacy mechanism named HeteDP, which provides a double guarantee on graph features and topology. In particular, we first define a new attack scheme to reveal privacy leakage in the heterogeneous graphs. Specifically, we design a two-stage pipeline framework, which includes the privacy-preserving feature encoder and the heterogeneous link reconstructor with gradients perturbation based on differential privacy to tolerate data diversity and against the attack. To better control the noise and promote model performance, we utilize a bi-level optimization pattern to allocate a suitable privacy budget for the above two modules. Our experiments on four public benchmarks show that the HeteDP method is equipped to resist heterogeneous graph privacy leakage with admirable model generalization.

Symmetric Searchable Encryption(SSE) is deemed to tackle the privacy issue as well as the operability and confidentiality in data outsourcing. However, most SSE schemes assume that the cloud is honest but curious. This assumption is not always applicable. In this paper, we propose an efficient SSE scheme based on B+-Tree and Counting Bloom Filter (CBF) which supports secure verification, dynamic updating, and multi-user queries. Comparing with the previous state of the arts, we design the new data structure CBF to support dynamic updating and boost verification. we evaluate our scheme through comprehensive experiments. The results are consistent with our analysis and show that our scheme is secure, and more efficient compared with the previous schemes with the same functionalities.The average performance can be improved by about 20% for both the cloud servers and users when the missing rate of the searching keywords is 20%. And the higher the missing rate is, the more the performance can be improved.

Many approaches have been proposed for publishing useful information while preserving data privacy. Among them, the privacy models of identity-reserved (k, l)-anonymity and identity-reserved $$(\alpha , \beta )$$ -anonymity have been proposed to handle the situation where an individual could have multiple records. However, the two models fail to prevent attribute disclosure. To this end, we propose two new privacy models: enhanced identity-reserved l-diversity and enhanced identity-reserved $$(\alpha , \beta )$$ -anonymity. Moreover, to implement the two privacy models we design a general anonymization algorithm, called DAnonyIR, with clustering technique by calling different decision functions, which can decrease the information loss caused by generalization. Further, we compare DAnonyIR concerning our two privacy models with existing generalization method GeneIR concerning identity-reserved (k, l)-anonymity and identity-reserved $$(\alpha , \beta )$$ -anonymity, respectively. The experimental results show that our two approaches provide stronger privacy preservation, and their information loss and relative error ratio of query answering are less than those of GeneIR.

In this paper, we propose a novel approach for composing existing web services to satisfy the correctness constraints to the design, including freeness of deadlock and unspecified reception, and temporal constraints in Computation Tree Logic formula. An automated synthesis algorithm based on learning algorithm is introduced, which guarantees that the composite service is the most general way of coordinating services so that the correctness is ensured. We have implemented a prototype system evaluating the effectiveness and efficiency of our synthesis approach through an experimental study.