FedCDR: Privacy-preserving federated cross-domain recommendation

Yan, Dengcheng; Zhao, Yuchuan; Yang, Zhongxiu; Jin, Ying; Zhang, Yiwen

doi:10.1016/j.dcan.2022.04.034

Cited by 26 publications

(9 citation statements)

References 15 publications

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“…For example, for the CV problem, He et al [49] proposed the Momentum Contrast method for unsupervised learning of visual representations, i.e., MoCo, which was a model that used a contrastive learning-based approach to self-supervise the training of the image representer (encoder) to better encode the image and apply it to downstream tasks. In contrastive learning for language modeling, Yan et al [50] proposed the ConSERT framework that employed the contrastive learning approach to fine-tune BERT, thus solving the problem of data augmentation methods to modify semantic information in the natural language model. In the realm of RS, Xie et al [18] introduced the Contrastive Learning for Sequence Recommendation (CL4SRec) approach, which was a model that concentrated on deriving self-supervised signals from raw users' behavior sequences using three different data augmentation methods to improve personalized recommendation performance.…”

Section: Contrastive Learningmentioning

confidence: 99%

FSRPCL: Privacy-preserve federated social relationship prediction with contrastive learning

Liu,

Li,

Kou

et al. 2024

Tsinghua Sci. Technol.

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Cross-Platform Social Relationship Prediction (CPSRP) aims to utilize users' data information on multiple platforms to enhance the performance of social relationship prediction, thereby promoting socioeconomic development. Due to the highly sensitive nature of users' data in terms of privacy, CPSRP typically introduces various privacy-preserving mechanisms to safeguard users' confidential information. Although the introduction mechanism guarantees the security of the users' private information, it tends to degrade the performance of the social relationship prediction. Additionally, existing social relationship prediction schemes overlook the interdependencies among items invoked in a user behavior sequence. For this purpose, we propose a novel privacy-preserve Federated Social Relationship Prediction with Contrastive Learning framework called FSRPCL, which is a multi-task learning framework based on vertical federated learning. Specifically, the users' rating information is perturbed with a bounded differential privacy technology, and then the users' sequential representation information acquired through Transformer is applied for social relationship prediction and contrastive learning. Furthermore, each client uploads their respective weight information to the server, and the server aggregates the weight information and distributes it purposes to each client for updating.Numerous experiments on real-world datasets prove that FSRPCL delivers exceptional performance in social relationship prediction and privacy preservation, and effectively minimizes the impact of privacy-preserving technology on social relationship prediction accuracy.

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Section: Contrastive Learningmentioning

confidence: 99%

FSRPCL: Privacy-preserve federated social relationship prediction with contrastive learning

Liu,

Li,

Kou

et al. 2024

Tsinghua Sci. Technol.

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“…34 Furthermore, traditional anonymization approaches have been found to be vulnerable to GNN-based inference attacks. 35 To deal with the issue, the study 36 uses federated learning with local differential privacy to protect users' private information. In particular, a privacy-preserving embedding transformation mechanism is used to preserve user data.…”

Section: Privacy-preserving Graph Publishing (Ppgp)mentioning

confidence: 99%

Privacy‐preserving graph publishing with disentangled variational information bottleneck

Ma,

Li,

Sun

et al. 2023

Concurrency and Computation

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SummarySocial networks collect enormous amounts of user personal and behavioral data, which could threaten users' privacy if published or shared directly. Privacy‐preserving graph publishing (PPGP) can make user data available while protecting private information. For this purpose, in PPGP, anonymization methods like perturbation and generalization are commonly used. However, traditional anonymization methods are challenging in balancing high‐level privacy and utility, ineffective at defending against both various link and hybrid inference attacks, as well as vulnerable to graph neural network (GNN)‐based attacks. To solve those problems, we present a novel privacy‐disentangled approach that disentangles private and non‐private information for a better privacy‐utility trade‐off. Moreover, we propose a unified graph deep learning framework for PPGP, denoted privacy‐disentangled variational information bottleneck (PDVIB). Using low‐dimensional perturbations, the model generates an anonymized graph to defend against various inference attacks, including GNN‐based attacks. Particularly, the model fits various privacy settings by employing adjustable perturbations at the node level. With three real‐world datasets, PDVIB is demonstrated to generate robust anonymous graphs that defend against various privacy inference attacks while maintaining the utility of non‐private information.

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“…in which t denotes the check-in time, z (0 ≤ z ≤ 1) denotes the forgetting coefficient, and t min and t max represent the earliest and latest check-in times, respectively. In formula (14), parameter z adjusts the rangeability of user interest. According to the average rangeability of users' interests, we set the value of z to 0.5.…”

Section: Dt Rust(umentioning

confidence: 99%

“…These entities can process, analyze and mine data to extract useful information, but also sell and/or share the collected data with third parties, using it maliciously. Some scholars realize the importance of privacy-preserving in POI recommendation and design a certain number of methods to protect users' information [13][14][15]. These methods adopt several strategies such as privacy parameter optimization, tuning the influence of disturbances, and controlling the modeling errors [16,17] to solve the dilemma between privacy protection effect and recommendation quality.…”

Section: Introductionmentioning

confidence: 99%

An efficient privacy-preserving point-of-interest recommendation model based on local differential privacy

Mei

et al. 2022

Complex Intell. Syst.

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With the rapid development of point-of-interest (POI) recommendation services, how to utilize the multiple types of users’ information safely and effectively for a better recommendation is challenging. To solve the problems of imperfect privacy-preserving mechanism and insufficient response-ability to complex contexts, this paper proposes a hybrid POI recommendation model based on local differential privacy (LDP). Firstly, we introduce randomized response techniques k-RR and RAPPOR to disturb users’ ratings and social relationships, respectively and propose a virtual check-in time generation method to deal with the issue of missing check-in time after disturbance. Secondly, for simultaneously combining multiple types of information, we construct a hybrid model containing three sub-models. Sub-model 1 considers the effect of user preference, social relationship, forgetting feature, and check-in trajectory on similarity calculation. Sub-model 2 analyzes the geographical correlation of POIs. Sub-model 3 focuses on the categories of POIs. Finally, we generate the recommendation results. To test the performance of privacy-preserving and recommendation, we design three groups of experiments on three real-world datasets for comprehensive verifying. The experimental results show that the proposed method outperforms existing methods. Theoretically, our study contributes to the effective and safe usage of multidimensional data science and analytics for privacy-preserving POI recommender system design. Practically, our findings can be used to improve the quality of POI recommendation services.

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FedCDR: Privacy-preserving federated cross-domain recommendation

Cited by 26 publications

References 15 publications

FSRPCL: Privacy-preserve federated social relationship prediction with contrastive learning

FSRPCL: Privacy-preserve federated social relationship prediction with contrastive learning

Privacy‐preserving graph publishing with disentangled variational information bottleneck

An efficient privacy-preserving point-of-interest recommendation model based on local differential privacy

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