A Group Key Establishment Scheme
- URL: http://arxiv.org/abs/2109.15037v2
- Date: Sat, 4 May 2024 07:33:39 GMT
- Title: A Group Key Establishment Scheme
- Authors: Sueda Guzey, Gunes Karabulut Kurt, Enver Ozdemir,
- Abstract summary: Group authentication is a method of confirming that a set of users belong to a group.
Unlike the standard authentication schemes where one central authority authenticates users one by one, group authentication can handle the authentication process at once for all members of the group.
- Score: 1.4091801425319967
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Group authentication is a method of confirmation that a set of users belong to a group and of distributing a common key among them. Unlike the standard authentication schemes where one central authority authenticates users one by one, group authentication can handle the authentication process at once for all members of the group. The recently presented group authentication algorithms mainly exploit Lagrange's polynomial interpolation along with elliptic curve groups over finite fields. As a fresh approach, this work suggests use of linear spaces for group authentication and key establishment for a group of any size. The approach with linear spaces introduces a reduced computation and communication load to establish a common shared key among the group members. The advantages of using vector spaces make the proposed method applicable to energy and resource constrained devices. In addition to providing lightweight authentication and key agreement, this proposal allows any user in a group to make a non-member to be a member, which is expected to be useful for autonomous systems in the future. The scheme is designed in a way that the sponsors of such members can easily be recognized by anyone in the group. Unlike the other group authentication schemes based on Lagrange's polynomial interpolation, the proposed scheme doesn't provide a tool for adversaries to compromise the whole group secrets by using only a few members' shares as well as it allows to recognize a non-member easily, which prevents service interruption attacks.
Related papers
- Provably Secure Non-interactive Key Exchange Protocol for Group-Oriented Applications in Scenarios with Low-Quality Networks [11.986730976775437]
Non-interactive key exchange (NIKE) enables two or multiple parties to derive a (group) session key without the need for interaction.
We propose a secure and efficient NIKE protocol for secure communications in dynamic groups.
arXiv Detail & Related papers (2024-06-21T09:49:29Z) - Attribute-Based Authentication in Secure Group Messaging for Distributed Environments [2.254434034390528]
Messaging Layer security (MLS) and its underlying Continuous Group Key Agreement protocol allow a group of users to share a cryptographic secret in a dynamic manner.
The use of digital certificates for authentication in a group goes against the group members' privacy.
We provide an alternative method of authentication in which the solicitors, instead of revealing their identity, only need to prove possession of certain attributes.
arXiv Detail & Related papers (2024-05-20T14:09:28Z) - 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) - Lattice attack on group ring NTRU: The case of the dihedral group [2.106410091047004]
This paper shows that dihedral groups do not guarantee better security against lattice attacks on the public key of NTRU-like cryptosystems.
We prove that retrieving the private key is possible by solving the SVP in two lattices with half the dimension of the original lattice generated for GR-NTRU based on dihedral groups.
arXiv Detail & Related papers (2023-09-15T10:50:46Z) - 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) - Beyond the Prototype: Divide-and-conquer Proxies for Few-shot
Segmentation [63.910211095033596]
Few-shot segmentation aims to segment unseen-class objects given only a handful of densely labeled samples.
We propose a simple yet versatile framework in the spirit of divide-and-conquer.
Our proposed approach, named divide-and-conquer proxies (DCP), allows for the development of appropriate and reliable information.
arXiv Detail & Related papers (2022-04-21T06:21:14Z) - On the Convergence of Clustered Federated Learning [57.934295064030636]
In a federated learning system, the clients, e.g. mobile devices and organization participants, usually have different personal preferences or behavior patterns.
This paper proposes a novel weighted client-based clustered FL algorithm to leverage the client's group and each client in a unified optimization framework.
arXiv Detail & Related papers (2022-02-13T02:39:19Z) - 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) - Federated Learning with Only Positive Labels [71.63836379169315]
We propose a generic framework for training with only positive labels, namely Federated Averaging with Spreadout (FedAwS)
We show, both theoretically and empirically, that FedAwS can almost match the performance of conventional learning where users have access to negative labels.
arXiv Detail & Related papers (2020-04-21T23:35:02Z) - Group Membership Verification with Privacy: Sparse or Dense? [21.365032455883178]
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.
arXiv Detail & Related papers (2020-02-24T16:47:19Z)
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.