Achieving secure and flexible M-services through tickets

Wang, Hua; Zhang, Yanchun; Cao, Tru H.; Varadharajan, Vijay

doi:10.1109/tsmca.2003.819917

Cited by 49 publications

(3 citation statements)

References 22 publications

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“…This scheme [ 2 ] used the Schnorr [ 4 ] signature scheme and Zheng [ 5 ] signature encryption scheme to propose a strong designated verifier signature scheme, which achieves signer identity privacy by avoiding the use of encryption algorithms and further improves the efficiency of signing and verification. A secure and flexible access control scheme and protocol for M-services based on role based access control (RBAC) [ 6 ] in the same year. In 2004, Laguillaumie et al [ 7 ] provided the first formal description of the concept of designated verifier signatures and a formal definition of the signer identity privacy property in strong designated verifier signatures.…”

Section: Related Workmentioning

confidence: 99%

Efficient attribute-based strong designated verifier signature scheme based on elliptic curve cryptography

Ma,

2024

PLoS ONE

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In an attribute-based strong designated verifier signature, a signer who satisfies the access structure signs the message and assigns it to a verifier who satisfies the access structure to verify it, which enables fine-grained access control for signers and verifiers. Such signatures are used in scenarios where the identity of the signer needs to be protected, or where the public verifiability of the signature is avoided and only the designated recipient can verify the validity of the signature. To address the problem that the overall overhead of the traditional attribute-based strong designated verifier signature scheme is relatively large, an efficient attribute-based strong designated verifier signature scheme based on elliptic curve cryptography is proposed, as well as a security analysis of the new scheme given in the standard model under the difficulty of the elliptic curve discrete logarithm problem (ECDLP). On the one hand, the proposed scheme is based on elliptic curve cryptography and uses scalar multiplication on elliptic curves, which is computationally lighter, instead of bilinear pairing, which has a higher computational overhead in traditional attribute-based signature schemes. This reduces the computational overhead of signing and verification in the system, improves the efficiency of the system, and makes the scheme more suitable for resource-constrained cloud end-user scenarios. On the other hand, the proposed scheme uses LSSS (Linear Secret Sharing Schemes) access structure with stronger access policy expression, which is more efficient than the "And" gate or access tree access structure, making the computational efficiency of the proposed scheme meet the needs of resource-constrained cloud end-users.

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Section: Related Workmentioning

confidence: 99%

Efficient attribute-based strong designated verifier signature scheme based on elliptic curve cryptography

Ma,

2024

PLoS ONE

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“…In the present era, data assumes a critical role in the daily lives of individuals [1][2][3][4][5][6]. The dissemination and utilization of data [7][8][9][10][11][12][13] have created enormous opportunities for decision-making and knowledge exploration [5,[14][15][16][17][18][19][20][21][22][23]. For instance, in 2006, Netflix released a dataset comprising 100 million movie ratings to enhance its recommendation system's performance [24].…”

Section: Introductionmentioning

confidence: 99%

Privacy-preserving data publishing: an information-driven distributed genetic algorithm

Ge,

Wang,

Cao

et al. 2024

World Wide Web

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The privacy-preserving data publishing (PPDP) problem has gained substantial attention from research communities, industries, and governments due to the increasing requirements for data publishing and concerns about data privacy. However, achieving a balance between preserving privacy and maintaining data quality remains a challenging task in PPDP. This paper presents an information-driven distributed genetic algorithm (ID-DGA) that aims to achieve optimal anonymization through attribute generalization and record suppression. The proposed algorithm incorporates various components, including an information-driven crossover operator, an information-driven mutation operator, an information-driven improvement operator, and a two-dimensional selection operator. Furthermore, a distributed population model is utilized to improve population diversity while reducing the running time. Experimental results confirm the superiority of ID-DGA in terms of solution accuracy, convergence speed, and the effectiveness of all the proposed components.

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“…Figure 2 represents the straggling device scenario where Device 3 takes more time delaying global model aggregation. The research work for preserving privacy has been a hot topic for many years [32][33][34] , it has also been discussed in edge framework [35] . In EFL, stragglers issue can intensify degradation of model performance as the participating devices are in millions and uncertain about their prolonged participation as well as their complete time dedication for model training.…”

Section: Introductionmentioning

confidence: 99%

Elastic Optimization for Stragglers in Edge Federated Learning

Sultana,

Ahmed,

et al. 2023

Big Data Min. Anal.

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To fully exploit enormous data generated by intelligent devices in edge computing, edge federated learning (EFL) is envisioned as a promising solution. The distributed collaborative training in EFL deals with delay and privacy issues compared to traditional centralized model training. However, the existence of straggling devices, responding slow to servers, degrades model performance. We consider data heterogeneity from two aspects: high dimensional data generated at edge devices where the number of features is greater than that of observations and the heterogeneity caused by partial device participation. With large number of features, computation overhead on the devices increases, causing edge devices to become stragglers. And incorporation of partial training results causes gradients to be diverged which further exaggerates when more training is performed to reach local optima. In this paper, we introduce elastic optimization methods for stragglers due to data heterogeneity in edge federated learning. Specifically, we define the problem of stragglers in EFL. Then, we formulate an optimization problem to be solved at edge devices. We customize a benchmark algorithm, FedAvg, to obtain a new elastic optimization algorithm (FedEN) which is applied in local training of edge devices. FedEN mitigates stragglers by having a balance between lasso and ridge penalization thereby generating sparse model updates and enforcing parameters as close as to local optima.We have evaluated the proposed model on MNIST and CIFAR-10 datasets. Simulated experiments demonstrate that our approach improves run time training performance by achieving average accuracy with less communication rounds. The results confirm the improved performance of our approach over benchmark algorithms.

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Achieving secure and flexible M-services through tickets

Cited by 49 publications

References 22 publications

Efficient attribute-based strong designated verifier signature scheme based on elliptic curve cryptography

Efficient attribute-based strong designated verifier signature scheme based on elliptic curve cryptography

Privacy-preserving data publishing: an information-driven distributed genetic algorithm

Elastic Optimization for Stragglers in Edge Federated Learning

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