Group Membership Verification with Privacy: Sparse or Dense?

• URL: http://arxiv.org/abs/2002.10362v1
• Date: Mon, 24 Feb 2020 16:47:19 GMT
• Title: Group Membership Verification with Privacy: Sparse or Dense?
• Authors: Marzieh Gheisari, Teddy Furon, Laurent Amsaleg
• Abstract summary: Group membership verification checks if a biometric trait corresponds to one member of a group without revealing the identity of that member. Recent contributions provide privacy for group membership protocols through the joint use of two mechanisms. This paper proposes a mathematical model for group membership verification allowing to reveal the impact of sparsity on both security, compactness, and verification performances.
• Score: 21.365032455883178
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Group membership verification checks if a biometric trait corresponds to one member of a group without revealing the identity of that member. Recent contributions provide privacy for group membership protocols through the joint use of two mechanisms: quantizing templates into discrete embeddings and aggregating several templates into one group representation. However, this scheme has one drawback: the data structure representing the group has a limited size and cannot recognize noisy queries when many templates are aggregated. Moreover, the sparsity of the embeddings seemingly plays a crucial role on the performance verification. This paper proposes a mathematical model for group membership verification allowing to reveal the impact of sparsity on both security, compactness, and verification performances. This model bridges the gap towards a Bloom filter robust to noisy queries. It shows that a dense solution is more competitive unless the queries are almost noiseless.

Related papers

• The Research of Group Re-identification from Multiple Cameras [0.4955551943523977]
  Group re-identification is very challenging since it is not only interfered by view-point and human pose variations in the traditional re-identification tasks. This paper introduces a novel approach which leverages the multi-granularity information inside groups to facilitate group re-identification.
  arXiv  Detail & Related papers  (2024-07-19T18:28:13Z)
• Advancing Vision Transformers with Group-Mix Attention [59.585623293856735]
  Group-Mix Attention (GMA) is an advanced replacement for traditional self-attention. GMA simultaneously captures token-to-token, token-to-group, and group-to-group correlations with various group sizes. GroupMixFormer achieves state-of-the-art performance in image classification, object detection, and semantic segmentation.
  arXiv  Detail & Related papers  (2023-11-26T01:25:03Z)
• Uncovering Prototypical Knowledge for Weakly Open-Vocabulary Semantic Segmentation [59.37587762543934]
  This paper studies the problem of weakly open-vocabulary semantic segmentation (WOVSS) Existing methods suffer from a granularity inconsistency regarding the usage of group tokens. We propose the prototypical guidance network (PGSeg) that incorporates multi-modal regularization.
  arXiv  Detail & Related papers  (2023-10-29T13:18:00Z)
• Balanced Filtering via Disclosure-Controlled Proxies [8.477632486444353]
  We study the problem of collecting a cohort that is balanced with respect to sensitive groups when group membership is unavailable or prohibited from use at deployment time. Our deployment-time collection mechanism does not reveal significantly more about the group membership of any individual sample than can be ascertained from base rates alone.
  arXiv  Detail & Related papers  (2023-06-26T21:55:24Z)
• InstantGroup: Instant Template Generation for Scalable Group of Brain MRI Registration [5.361571536184391]
  We present InstantGroup, an efficient groupwise template generation framework based on variational autoencoder (VAE) models. Experiments on 3D brain MRI scans reveal that InstantGroup dramatically reduces runtime, generating templates within seconds for various group sizes.
  arXiv  Detail & Related papers  (2022-11-10T14:54:31Z)
• AggNet: Learning to Aggregate Faces for Group Membership Verification [20.15673797674449]
  In some face recognition applications, we are interested to verify whether an individual is a member of a group, without revealing their identity. Some existing methods, propose a mechanism for quantizing precomputed face descriptors into discrete embeddings and aggregating them into one group representation. We propose a deep architecture that jointly learns face descriptors and the aggregation mechanism for better end-to-end performances.
  arXiv  Detail & Related papers  (2022-06-17T10:48:34Z)
• Implicit Sample Extension for Unsupervised Person Re-Identification [97.46045935897608]
  Clustering sometimes mixes different true identities together or splits the same identity into two or more sub clusters. We propose an Implicit Sample Extension (OurWholeMethod) method to generate what we call support samples around the cluster boundaries. Experiments demonstrate that the proposed method is effective and achieves state-of-the-art performance for unsupervised person Re-ID.
  arXiv  Detail & Related papers  (2022-04-14T11:41:48Z)
• Towards Group Robustness in the presence of Partial Group Labels [61.33713547766866]
  spurious correlations between input samples and the target labels wrongly direct the neural network predictions. We propose an algorithm that optimize for the worst-off group assignments from a constraint set. We show improvements in the minority group's performance while preserving overall aggregate accuracy across groups.
  arXiv  Detail & Related papers  (2022-01-10T22:04:48Z)
• Learning Multi-Attention Context Graph for Group-Based Re-Identification [214.84551361855443]
  Learning to re-identify or retrieve a group of people across non-overlapped camera systems has important applications in video surveillance. In this work, we consider employing context information for identifying groups of people, i.e., group re-id. We propose a novel unified framework based on graph neural networks to simultaneously address the group-based re-id tasks.
  arXiv  Detail & Related papers  (2021-04-29T09:57:47Z)
• Overcoming Data Sparsity in Group Recommendation [52.00998276970403]
  Group recommender systems should be able to accurately learn not only users' personal preferences but also preference aggregation strategy. In this paper, we take Bipartite Graphding Model (BGEM), the self-attention mechanism and Graph Convolutional Networks (GCNs) as basic building blocks to learn group and user representations in a unified way.
  arXiv  Detail & Related papers  (2020-10-02T07:11:19Z)

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.