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28 pages, 5058 KiB

Open AccessArticle

A Parallel Privacy-Preserving k-Means Clustering Algorithm for Encrypted Databases in Cloud Computing

by Youngho Song, Hyeong-Jin Kim, Hyun-Jo Lee and Jae-Woo Chang

Appl. Sci. 2024, 14(2), 835; https://doi.org/10.3390/app14020835 - 18 Jan 2024

Cited by 4 | Viewed by 1622

With the development of cloud computing, interest in database outsourcing has recently increased. However, when the database is outsourced, there is a problem in that the information of the data owner is exposed to internal and external attackers. Therefore, in this paper, we [...] Read more.

With the development of cloud computing, interest in database outsourcing has recently increased. However, when the database is outsourced, there is a problem in that the information of the data owner is exposed to internal and external attackers. Therefore, in this paper, we propose decimal-based encryption operation protocols that support privacy preservation. The proposed protocols improve the operational efficiency compared with binary-based encryption operation protocols by eliminating the need for repetitive operations based on bit length. In addition, we propose a privacy-preserving k-means clustering algorithm using decimal-based encryption operation protocols. The proposed k-means clustering algorithm utilizes efficient decimal-based protocols that enhance the efficiency of the encryption operations. To provide high query processing performance, we also propose a parallel k-means clustering algorithm that supports thread-based parallel processing by using a random value pool. Meanwhile, a security analysis of both the proposed k-means clustering algorithm and the proposed parallel algorithm was performed to prove their data protection, query protection, and access pattern protection capabilities. Through our performance analysis, the proposed k-means clustering algorithm shows about 10~13 times better performance compared with the existing algorithms. Full article

(This article belongs to the Special Issue Privacy-Preserving Methods and Applications in Big Data Sharing)

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18 pages, 1175 KiB

Open AccessArticle

QuoTa: An Online Quality-Aware Incentive Mechanism for Fast Federated Learning

by Hui Cai, Chao Bian, Biyun Sheng, Jian Zhou, Juan Li and Xin He

Appl. Sci. 2024, 14(2), 833; https://doi.org/10.3390/app14020833 - 18 Jan 2024

Cited by 1 | Viewed by 1195

Abstract

In addition to federated optimization, more current studies focus on incentive mechanism design problems for federated learning (FL), stimulating data owners to share their resources securely. Most existing works only considered data quantity but neglected other key factors like data quality and training [...] Read more.

In addition to federated optimization, more current studies focus on incentive mechanism design problems for federated learning (FL), stimulating data owners to share their resources securely. Most existing works only considered data quantity but neglected other key factors like data quality and training time prediction. In combination with all the above factors, we proposed an online quality-aware incentive mechanism based on multi-dimensional reverse auction, QuoTa, for achieving fast FL. In particular, it first designs model quality detection to eliminate some malicious or dispensable devices based on their historical behaviors and marginal contributions. Due to the possible fluctuations of CPU frequency in realistic model training, it next predicts model training time based on the upper confidence bound algorithm. By combining the two modules, QuoTa incentivizes data owners with high data quality, high computing capability, and low cost to participate in the FL process. By rigorous theoretical proof and extensive experiments, we prove that QuoTa satisfies all desired economic properties and achieves higher model accuracy and less convergence time than the state-of-the-art work. Full article

(This article belongs to the Special Issue Privacy-Preserving Methods and Applications in Big Data Sharing)

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24 pages, 1244 KiB

Open AccessArticle

FedNow: An Efficiency-Aware Incentive Mechanism Enables Privacy Protection and Efficient Resource Utilization in Federated Edge Learning

by Jianfeng Lu, Wenxuan Yuan, Shuqin Cao and Pan Zhou

Appl. Sci. 2024, 14(2), 494; https://doi.org/10.3390/app14020494 - 5 Jan 2024

Viewed by 1580

Abstract

Federated edge learning (FEL) has recently attracted great interest due to its real-time response and energy-efficient characteristics. Most existing work focuses on designing algorithms to improve model performance, ignoring the malicious behavior and personal decision-making of self-interested edge servers. Although some efforts have [...] Read more.

Federated edge learning (FEL) has recently attracted great interest due to its real-time response and energy-efficient characteristics. Most existing work focuses on designing algorithms to improve model performance, ignoring the malicious behavior and personal decision-making of self-interested edge servers. Although some efforts have been devoted to incentivizing honest edge server engagement by compensating training costs, this rarely considers resource efficiency and often assumes that edge servers provide complete information to the platform, which may lead to the risk of private attribute leakage. Hence, we aim to achieve an incentive mechanism that promotes secure and efficient model training between the platform and edge servers. However, edge servers’ multi-dimensional private attributes and training strategies make the optimization problem nonconvex, and incomplete information further increases the complexity of the analysis. In order to address these challenges and by integrating contract theory and exponential mechanism, we propose an efficiency-aware incentive mechanism, FedNow, which enables edge servers to personally determine their local training rounds while motivating their participation without giving access to their true training strategies and private attributes. Specifically, we enabld edge servers to add noise to their submitted training strategy to hide their true training rounds; then, we carefully designed an efficiency score function to select honest and efficient edge servers without disclosing their private attributes. In order to demonstrate that FedNow strictly outperforms existing schemes in terms of total costs, we theoretically derived sufficient conditions for making the total costs of FedNow lower than existing schemes and designed a greedy algorithm that uses the Monte Carlo method to find feasible near-optimal solutions in polynomial time. Our extensive experimental assessment using synthetic and real datasets shows the superiority of FedNow. Full article

(This article belongs to the Special Issue Privacy-Preserving Methods and Applications in Big Data Sharing)

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15 pages, 1128 KiB

Open AccessArticle

The Use of Blockchain Technology and OCR in E-Government for Document Management: Inbound Invoice Management as an Example

by Fatima Azzam, Mariam Jaber, Amany Saies, Tareq Kirresh, Ruba Awadallah, Abdallah Karakra, Hafez Barghouthi and Saleh Amarneh

Appl. Sci. 2023, 13(14), 8463; https://doi.org/10.3390/app13148463 - 21 Jul 2023

Cited by 7 | Viewed by 4466

Abstract

The field of electronic government (e-government) is gaining prominence in contemporary society, as it has a significant influence on the wider populace within the context of a technologically advanced world. E-government makes use of information and communication technologies (ICTs) at various levels and [...] Read more.

The field of electronic government (e-government) is gaining prominence in contemporary society, as it has a significant influence on the wider populace within the context of a technologically advanced world. E-government makes use of information and communication technologies (ICTs) at various levels and domains within government agencies and the public sector. ICT reduces manual labour, potential fraud points, errors, and process lapses. The Internet’s quick accessibility and the widespread adoption of modern technologies and disciplines, such as big data, the Internet of Things, machine learning, and artificial intelligence, have accelerated the need for e-government. However, these developments raise a number of data reliability and precision concerns. The adoption of blockchain technology by researchers demonstrates its efficacy in addressing such issues. The present study proposes the SECHash system model, which integrates blockchain and Optical Character Recognition (OCR) technologies for the purpose of regulating the processing of incoming documents by governmental agencies. As a case study to assess the proposed system paradigm, the study uses a document containing incoming invoices. The proposal seeks to maintain the integrity of document data by prohibiting its modification after acceptance. Additionally, SECHash guarantees that accepted documents will not be destroyed or lost. The analysis demonstrates that using the SECHash model system will decrease fraudulent transactions by eradicating manual labour and storing documents on a blockchain network. Full article

(This article belongs to the Special Issue Privacy-Preserving Methods and Applications in Big Data Sharing)

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19 pages, 1268 KiB

Open AccessArticle

A Node Differential Privacy-Based Method to Preserve Directed Graphs in Wireless Mobile Networks

by Jun Yan, Yihui Zhou and Laifeng Lu

Appl. Sci. 2023, 13(14), 8089; https://doi.org/10.3390/app13148089 - 11 Jul 2023

Cited by 1 | Viewed by 1357

Abstract

With the widespread popularity of Wireless Mobile Networks (WMNs) in our daily life, the huge risk to disclose personal privacy of massive graph structure data in WMNs receives more and more attention. Particularly, as a special type of graph data in WMNs, the [...] Read more.

With the widespread popularity of Wireless Mobile Networks (WMNs) in our daily life, the huge risk to disclose personal privacy of massive graph structure data in WMNs receives more and more attention. Particularly, as a special type of graph data in WMNs, the directed graph contains an amount of sensitive personal information. To provide secure and reliable privacy preservation for directed graphs in WMNs, we develop a node differential privacy-based method, which combines differential privacy with graph modification. In the method, the original directed graph is first divided into several sub-graphs after it is transformed into a weighted graph. Then, in each sub-graph, the node degree sequences are obtained by using an exponential mechanism and micro-aggregation is adopted to get the noised node degree sequences, which is used to generate a synthetic directed sub-graph through edge modification. Finally, all synthetic sub-graphs are merged into a synthetic directed graph that can preserve the original directed graph. The theoretical analysis proves that the proposed method satisfies differential privacy. The results of the experiments demonstrate the effectiveness of the presented method in privacy preservation and data utility. Full article

(This article belongs to the Special Issue Privacy-Preserving Methods and Applications in Big Data Sharing)

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23 pages, 11713 KiB

Open AccessArticle

Shapley Values as a Strategy for Ensemble Weights Estimation

by Vaidotas Drungilas, Evaldas Vaičiukynas, Linas Ablonskis and Lina Čeponienė

Appl. Sci. 2023, 13(12), 7010; https://doi.org/10.3390/app13127010 - 10 Jun 2023

Cited by 1 | Viewed by 2304

Abstract

This study introduces a novel performance-based weighting scheme for ensemble learning using the Shapley value. The weighting uses the reciprocal of binary cross-entropy as a base learner’s performance metric and estimates its Shapley value to measure the overall contribution of a learner to [...] Read more.

This study introduces a novel performance-based weighting scheme for ensemble learning using the Shapley value. The weighting uses the reciprocal of binary cross-entropy as a base learner’s performance metric and estimates its Shapley value to measure the overall contribution of a learner to an equally weighted ensemble of various sizes. Two variants of this strategy were empirically compared with a single monolith model and other static weighting strategies using two large banking-related datasets. A variant that discards learners with a negative Shapley value was ranked as first or at least second when constructing homogeneous ensembles, whereas for heterogeneous ensembles this strategy resulted in a better or at least similar detection performance to other weighting strategies tested. The main limitation being the computational complexity of Shapley calculations, the explored weighting strategy could be considered as a generalization of performance-based weighting. Full article

(This article belongs to the Special Issue Privacy-Preserving Methods and Applications in Big Data Sharing)

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21 pages, 5012 KiB

Open AccessArticle

Blockchained Trustable Federated Learning Utilizing Voting Accountability for Malicious Actor Mitigation

by Brian Stanley, Sang-Gon Lee and Elizabeth Nathania Witanto

Appl. Sci. 2023, 13(11), 6707; https://doi.org/10.3390/app13116707 - 31 May 2023

Cited by 2 | Viewed by 1394

Abstract

The federated learning (FL) approach in machine learning preserves user privacy during data collection. However, traditional FL schemes still rely on a centralized server, making them vulnerable to security risks, such as data breaches and tampering of models caused by malicious actors attempting [...] Read more.

The federated learning (FL) approach in machine learning preserves user privacy during data collection. However, traditional FL schemes still rely on a centralized server, making them vulnerable to security risks, such as data breaches and tampering of models caused by malicious actors attempting to gain access by masquerading as trainers. To address these issues that hamper the trustability of federated learning, requirements were analyzed for several of these problems. The findings revealed that issues, such as the lack of accountability management, malicious actor mitigation, and model leakage, remained unaddressed in prior works. To fill this gap, a blockchain-based trustable FL scheme, MAM-FL, is proposed with the focus on providing accountability to trainers. MAM-FL established a group of voters responsible for evaluating and verifying the validity of the model updates submitted. The effectiveness of MAM-FL was tested based on the reduction of malicious actors present on both trainers’ and voters’ sides and the ability to handle colluding participants. Experiments show that MAM-FL succeeded at reducing the number of malicious actors, despite the test case involving initial collusion in the system. Full article

(This article belongs to the Special Issue Privacy-Preserving Methods and Applications in Big Data Sharing)

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26 pages, 850 KiB

Open AccessArticle

A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems

by Muhammad Asad, Saima Shaukat, Ehsan Javanmardi, Jin Nakazato and Manabu Tsukada

Appl. Sci. 2023, 13(10), 6201; https://doi.org/10.3390/app13106201 - 18 May 2023

Cited by 19 | Viewed by 7010

Abstract

Big data is a rapidly growing field, and new developments are constantly emerging to address various challenges. One such development is the use of federated learning for recommendation systems (FRSs). An FRS provides a way to protect user privacy by training recommendation models [...] Read more.

Big data is a rapidly growing field, and new developments are constantly emerging to address various challenges. One such development is the use of federated learning for recommendation systems (FRSs). An FRS provides a way to protect user privacy by training recommendation models using intermediate parameters instead of real user data. This approach allows for cooperation between data platforms while still complying with privacy regulations. In this paper, we explored the current state of research on FRSs, highlighting existing research issues and possible solutions. Specifically, we looked at how FRSs can be used to protect user privacy while still allowing organizations to benefit from the data they share. Additionally, we examined potential applications of FRSs in the context of big data, exploring how these systems can be used to facilitate secure data sharing and collaboration. Finally, we discuss the challenges associated with developing and deploying FRSs in the real world and how these challenges can be addressed. Full article

(This article belongs to the Special Issue Privacy-Preserving Methods and Applications in Big Data Sharing)

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