Large-scale social networks can be modeled as decentralized graphs, where each node holds a part of the overall network. Local differential privacy (LDP) has been widely adopted in decentralized graph analysis to ensure privacy for individual nodes. However, existing LDP-based methods often fail to accommodate personalized privacy requirements due to their uniform encoding and equal perturbation mechanisms. To address this issue, we propose PEGS, a novel privacy-preserving decentralized graph synthesis approach that significantly improves utility while respecting user-specific privacy preferences. Specifically, we introduce interactive local differential privacy (iLDP), a new edge-level definition of LDP that relaxes the constraints of node-independent perturbation, thereby enabling the fulfillment of individual privacy needs. Furthermore, we develop a decentralized graph perturbation framework offering three levels of privacy settings. To optimize the balance between information preservation and privacy, we design encoding and perturbation mechanisms leveraging information entropy tailored to different privacy levels. Extensive experimental evaluations and rigorous theoretical analysis demonstrate that our method produces high-quality synthetic graphs while adhering to iLDP guarantees.

Many real-world networks can be modeled as decentralized heterogeneous graphs with different types of nodes, each holds a piece of whole network and has its own privacy preference. Local differential privacy (LDP) has been widely used in decentralized graph synthesis to provide privacy guarantees. This paper shows that existing LDP-based graph synthesis approaches are insufficient for preserving topological properties and satisfying the different privacy requirements of heterogeneous nodes when generating synthetic decentralized graphs. To address these problems due to the clueless perturbation and ignorance of the topological properties of existing approaches, we introduce HeG-LDP, a novel privacy-preserving decentralized heterogeneous graph synthesis approach that achieves a considerable utility boost compared to other methods while satisfying the privacy requirements of different types of nodes. Concretely, we propose a heterogeneous graph information extraction approach that exploits the inherent topological nature of graphs to construct background knowledge from the partial nodes in a diffusion manner, which is then used to guide individuals in topological information extraction and perturbation. In addition, to further improve the quality of the generated graph, we propose a dK-series-based graph generation approach, which can optimize the connection probability of node pairs via a delicate combination of degree values and dK-series information, resulting in better maintenance of the topological utility. Comprehensive experiments and theoretical analysis show that our proposed HeG-LDP can yield high-quality synthetic heterogeneous graphs while satisfying edge-LDP. To promote research in this field, we make our source code and data publicly available athttps://github.com/HeG-LDP/Paper-codes.

Trajectory publication under local differential privacy (LDP) has recently become a research focus. Existing solutions rely on geospatial discretization, elevating trajectory description granularity to alleviate noise injection. However, discretization itself also leads to trajectory information loss. Determining discretization granularity to balance differential noise and trajectory accuracy is challenging. Besides, these solutions commonly protect trajectory privacy via whole-region perturbation yet ignore the actual reachable range of trajectories, resulting in impractical published trajectories. To address these issues, we propose LDPTP, a novel LDP-based trajectory publication method that achieves high-quality publication by perceiving regional popularity in a privacy-preserving way. Specifically, we design a privacy-accuracy balancing mechanism for discretization granularity selection, which can effectively measure the impact of different granularities on noise error and information loss through the Bernoulli model and information entropy, enabling optimized discrete trajectories acquisition. Furthermore, a regional popularity-based perturbation method is presented, which utilizes trajectory distribution features to capture popular regions and then combines region similarity to generate private mobility patterns that better preserve trajectory utility. Finally, we devise a transition probability correction method to enhance the accuracy of Markov model learned from these private patterns, realizing high-utility trajectory synthesis for publication. Extensive experiments are conducted on real-world and synthetic datasets under three levels of utility metrics. The results demonstrate that our proposed LDPTP significantly outperforms the baseline methods.

Local differential privacy federated learning has attracted wide attention because it can solve the problem of data islands without damaging data privacy, but it faces data heterogeneity problems on the client side. Existing solutions commonly cluster clients with similar data distributions into the same training groups by leveraging clients’ model parameters, thus mitigating the impact of data heterogeneity on federated learning performance. However, model parameters, as an indirect representation for client data, often fail to accurately reflect the true data distribution, resulting in inaccurate client groupings. Furthermore, these solutions perturb high-dimensional parameter vectors dimension-by-dimension to protect client data privacy, which introduces substantial LDP noise that significantly further compromises the client grouping accuracy. To tackle these challenges, we propose PCFed-LDP, a privacy-preserving clustered federated learning framework that improves the federated learning performance while protecting client data and satisfying LDP in heterogeneous environments. Specifically, we introduce a client clustering method based on geometric properties of client data subspaces, which conducts label grouping-based principal angle analysis on client data subspaces to accurately capture the similarities in client data distributions, thereby enabling precise client grouping. To reduce the amount of introduced LDP noise, we design an adaptive noise addition method that utilizes the Haar wavelet technique to decouple the relationship between the noise amount and vector dimensionality, and employs noise error minimization strategy-based vector segmentation to inject LDP noise with finer granularity. Theoretical analysis and experiments on real datasets demonstrate that our solution not only satisfies the constraints of local differential privacy but also outperforms state-of-the-art methods.

This paper proposes the Global-Local Similarity Function (GLSF) to exploit the multi-scale cues in track sequences for automatic playlist generation (APG). Unlike previous neighborhood-based methods only looking on local similarities for a given playlist, GLTS is constructed by first modeling the fine-grained audio features of each track, then capturing the long-term relations among consecutive tracks. Specifically, the fine-grained audio features are captured beat-by-beat to represent the rhythmic variation of music. The long- term relations are modeled by a designed track distance constraint (TD-constraint) to alleviate the incoherences and un- smooth transition in track sequences. The fine-grained audio features and TD-constraint are aggregated as the final GLSF by a simple distance function. Objective and subjective evaluations show that GLSF-based APG achieves better smooth transition and ensure the long-term content consistency among the tracks. Furthermore, GLSF yields a better understanding of the sequential relationship between tracks and propose a promising way to improve APG algorithms.1