Oct 2026· Proceedings on Privacy Enhancing Technologies· 0 citations
TL;DR
CoP is proposed, a coordinated perturbation mechanism designed to mitigate CIL in multidimensional data collection while preserving utility and significantly outperforms state-of-the-art LDP mechanisms in reducing disclosure while preserving analytical accuracy.
Abstract
Collecting multidimensional user data is essential for personalized services, yet it poses significant privacy risks. While privacy regulations like the GDPR and CPRA advocate for data minimization, attribute correlations can inadvertently amplify unintentional information disclosure, leading to correlation-induced information leakage (CIL). Although data collectors often possess rich prior knowledge of these correlations, existing Local Differential Privacy (LDP) mechanisms are inadequate for effectively leveraging this information to reduce CIL. In this paper, we propose CoP, a coordinated perturbation mechanism designed to mitigate CIL in multidimensional data collection while preserving utility. Unlike traditional LDP approaches, CoP explicitly incorporates prior distribution knowledge to coordinate the perturbation process across attributes. By optimizing the perturbation strategy based on known correlations, CoP achieves a better privacy-utility trade-off. Extensive evaluations across both synthetic and real-world datasets demonstrate that CoP significantly outperforms state-of-the-art LDP mechanisms in reducing disclosure while preserving analytical accuracy.
A novel metric, ``Dependency Triad''(DT), is proposed, which summarizes the pairwise dependency information relevant to CPL using three parameters and yields a conservative estimator of pairwise CPL, which is particularly suitable for high-cardinality attributes.
Sandaru Jayawardana, S. Ulukus, Ming Ding et al.· 0 citations
Federated Recommender Systems (FRSs) enable on-device personalization while preserving user privacy, yet the gradients shared during model training can still leak sensitive information. Local differential privacy (LDP) offers a formal defense, but classic mechanisms—randomized response and Laplace perturbation—inject substantial noise into the high-dimensional gradient vectors of latent-factor models, severely degrading recommendation accuracy. In this paper, we systematically evaluate these traditional LDP strategies in federated matrix factorization under both explicit (MovieLens 100K and 1M) and implicit (Steam, LastFM) feedback benchmarks. We then propose a novel directional noise mechanism based on the von Mises–Fisher distribution, which preserves gradient magnitudes while randomizing directions. Through extensive experiments across a wide range of privacy budgets, we show that our vMF mechanism consistently outperforms Laplace and binary randomized-response, recovering up to more than 100 % of the non-private utility at ε = 1 and delivering higher ranking accuracy under moderate privacy levels (ε ≤ 2). We further demonstrate that directional perturbation reduces variance and remains robust under partial client participation. Our findings highlight the importance of data geometry in privacy–utility trade-offs and provide practical guidance for deploying LDP in high-dimensional federated recommendation settings.
Nawfal Abbassi Saber, Loubna Mekouar, Youssef Iraqi· European Symposium on Securi...· 0 citations
It is shown, through a theoretical study of the dynamics and numerical experiments, that a finite privacy budget can outperform non-private estimation in the long term when the feedback loop between leakage and participation is sufficiently strong.
Uddalak Mukherjee, Edwige Cyffers, Y. Chevaleyre· 0 citations
PPGNN, a personalized differentially private framework for decentralized graph data, enables user-specific privacy budgets during local perturbation while preserving analytical utility in decentralized graph learning scenarios.
Longzhu He, Peng Tang, Chaozhuo Li et al.· IEEE Transactions on Knowled...· 0 citations
Differential privacy (DP) mechanisms have been widely adopted in federated learning (FL) to enhance model security. However, existing approaches predominantly employ uniform privacy budgets, neglecting personalized requirements arising from heterogeneous user privacy preferences. Such uniform privacy configurations typically necessitate compliance with the most stringent budget, which not only leads to the wasteful underutilization of privacy budgets for certain clients but also compromises overall model utility. To address this limitation, we propose FedSPA, a Subspace Projection Aggregation personalized differential private Federated learning framework. The proposed method conducts singular value decomposition operations on noise-perturbed local models to extract singular value vectors as compact representations of both model structure and privacy noise. The server then clusters clients and identifies a consensus subspace for projecting models with varying noise levels, ultimately aggregating the global model through a residual-aware mechanism. This method not only effectively guides the aggregation of client personalized differential privacy but also reduces communication overhead. Extensive experiments demonstrate the model's effectiveness. Additionally, we provide theoretical proof of the privacy and convergence of FedSPA. Experimental results also showcase its superior performance over personalized DP-FL baselines.
Tianchi Liao, Xiaojun Deng, Lele Fu et al.· Proceedings of the 32nd ACM...· 0 citations
This work investigates a probabilistic variant of PCD, where an LLM-driven probabilistic estimation of k-anonymity is augmented with an LLM-driven probabilistic estimation of k-anonymity, and proposes k-anonymity as a useful auxiliary metric for tackling PCD.