SMAP: A Joint Dimensionality Reduction Scheme for Secure Multi-Party Visualization

• URL: http://arxiv.org/abs/2007.15591v1
• Date: Thu, 30 Jul 2020 16:54:57 GMT
• Title: SMAP: A Joint Dimensionality Reduction Scheme for Secure Multi-Party Visualization
• Authors: Jiazhi Xia, Tianxiang Chen, Lei Zhang, Wei Chen, Yang Chen, Xiaolong Zhang, Cong Xie, Tobias Schreck
• Abstract summary: We reformulate the conventional t-SNE method from the single-site mode into a secure distributed infrastructure. We present a secure multi-party scheme for joint t-SNE, which can minimize the risk of data leakage. We build a prototype system based on our method, SMAP, to support the organization, computation, and exploration of secure joint embedding.
• Score: 22.929450016843493
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Nowadays, as data becomes increasingly complex and distributed, data analyses often involve several related datasets that are stored on different servers and probably owned by different stakeholders. While there is an emerging need to provide these stakeholders with a full picture of their data under a global context, conventional visual analytical methods, such as dimensionality reduction, could expose data privacy when multi-party datasets are fused into a single site to build point-level relationships. In this paper, we reformulate the conventional t-SNE method from the single-site mode into a secure distributed infrastructure. We present a secure multi-party scheme for joint t-SNE computation, which can minimize the risk of data leakage. Aggregated visualization can be optionally employed to hide disclosure of point-level relationships. We build a prototype system based on our method, SMAP, to support the organization, computation, and exploration of secure joint embedding. We demonstrate the effectiveness of our approach with three case studies, one of which is based on the deployment of our system in real-world applications.

Related papers

• FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction [47.336599393600046]
  textscFedNE is a novel approach that integrates the textscFedAvg framework with the contrastive NE technique. We conduct comprehensive experiments on both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2024-09-17T19:23:24Z)
• Sparse outlier-robust PCA for multi-source data [2.3226893628361687]
  We introduce a novel PCA methodology that simultaneously selects important features as well as local source-specific patterns. We develop a regularization problem with a penalty that accommodates global-local structured sparsity patterns. We provide an efficient implementation of our proposal via the Alternating Direction Method of Multiplier.
  arXiv  Detail & Related papers  (2024-07-23T08:55:03Z)
• Decoupled Subgraph Federated Learning [57.588938805581044]
  We address the challenge of federated learning on graph-structured data distributed across multiple clients. We present a novel framework for this scenario, named FedStruct, that harnesses deep structural dependencies. We validate the effectiveness of FedStruct through experimental results conducted on six datasets for semi-supervised node classification.
  arXiv  Detail & Related papers  (2024-02-29T13:47:23Z)
• Learning Representations without Compositional Assumptions [79.12273403390311]
  We propose a data-driven approach that learns feature set dependencies by representing feature sets as graph nodes and their relationships as learnable edges. We also introduce LEGATO, a novel hierarchical graph autoencoder that learns a smaller, latent graph to aggregate information from multiple views dynamically.
  arXiv  Detail & Related papers  (2023-05-31T10:36:10Z)
• infoVerse: A Universal Framework for Dataset Characterization with Multidimensional Meta-information [68.76707843019886]
  infoVerse is a universal framework for dataset characterization. infoVerse captures multidimensional characteristics of datasets by incorporating various model-driven meta-information. In three real-world applications (data pruning, active learning, and data annotation), the samples chosen on infoVerse space consistently outperform strong baselines.
  arXiv  Detail & Related papers  (2023-05-30T18:12:48Z)
• Benchmarking FedAvg and FedCurv for Image Classification Tasks [1.376408511310322]
  This paper focuses on the problem of statistical heterogeneity of the data in the same federated network. Several Federated Learning algorithms, such as FedAvg, FedProx and Federated Curvature (FedCurv) have already been proposed. As a side product of this work, we release the non-IID version of the datasets we used so to facilitate further comparisons from the FL community.
  arXiv  Detail & Related papers  (2023-03-31T10:13:01Z)
• Adversarial Representation Sharing: A Quantitative and Secure Collaborative Learning Framework [3.759936323189418]
  We find representation learning has unique advantages in collaborative learning due to the lower communication overhead and task-independency. We present ARS, a collaborative learning framework wherein users share representations of data to train models. We demonstrate that our mechanism is effective against model inversion attacks, and achieves a balance between privacy and utility.
  arXiv  Detail & Related papers  (2022-03-27T13:29:15Z)
• Learning Multimodal VAEs through Mutual Supervision [72.77685889312889]
  MEME combines information between modalities implicitly through mutual supervision. We demonstrate that MEME outperforms baselines on standard metrics across both partial and complete observation schemes.
  arXiv  Detail & Related papers  (2021-06-23T17:54:35Z)
• Exploiting Shared Representations for Personalized Federated Learning [54.65133770989836]
  We propose a novel federated learning framework and algorithm for learning a shared data representation across clients and unique local heads for each client. Our algorithm harnesses the distributed computational power across clients to perform many local-updates with respect to the low-dimensional local parameters for every update of the representation. This result is of interest beyond federated learning to a broad class of problems in which we aim to learn a shared low-dimensional representation among data distributions.
  arXiv  Detail & Related papers  (2021-02-14T05:36:25Z)
• Evaluation Framework For Large-scale Federated Learning [10.127616622630514]
  Federated learning is proposed as a machine learning setting to enable distributed edge devices, such as mobile phones, to collaboratively learn a shared prediction model. In this paper, we introduce a framework designed for large-scale federated learning which consists of approaches to generating dataset and modular evaluation framework.
  arXiv  Detail & Related papers  (2020-03-03T15:12:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.