HP_FLAP: homomorphic and polymorphic federated learning aggregation of parameters framework
Abstract Protecting user privacy is essential in machine learning research, especially in the context of data collection. Federated learning (FL), which trains models across decentralized devices without sharing raw data, has emerged as a promising solution. However, FL is still vulnerable to securi...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-06-01
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| Series: | Cybersecurity |
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| Online Access: | https://doi.org/10.1186/s42400-024-00341-6 |
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| _version_ | 1849691311159902208 |
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| author | Mohammad Moshawrab Mehdi Adda Abdenour Bouzouane Hussein Ibrahim Ali Raad |
| author_facet | Mohammad Moshawrab Mehdi Adda Abdenour Bouzouane Hussein Ibrahim Ali Raad |
| author_sort | Mohammad Moshawrab |
| collection | DOAJ |
| description | Abstract Protecting user privacy is essential in machine learning research, especially in the context of data collection. Federated learning (FL), which trains models across decentralized devices without sharing raw data, has emerged as a promising solution. However, FL is still vulnerable to security threats, including inference attacks, which have been underexplored in comparison to poisoning and backdoor attacks that have received more attention in existing research. To address these vulnerabilities, this paper proposes a novel aggregation framework called homomorphic and polymorphic federated learning aggregation of parameters (HP_FLAP). HP_FLAP integrates both homomorphic and polymorphic encryption to enhance the security and privacy of FL. Homomorphic encryption allows the server to perform aggregation on encrypted parameters without decrypting them, ensuring that sensitive information is protected during the aggregation process. Polymorphic encryption further strengthens security by using different encryption keys for each set of parameters, mitigating the risk of system-wide compromise in case a key is leaked. This dual encryption approach effectively counters inference attacks while maintaining robust protections against other security threats. The framework is evaluated using multiple models, including logistic regression, Gaussian Naive Bayes, Stochastic Gradient Descent, and Multi-Layer Perceptron, demonstrating HP_FLAP’s ability to enhance both security and privacy in FL environments. |
| format | Article |
| id | doaj-art-09952e6fd3c34a6da9d5c420f5dae3df |
| institution | DOAJ |
| issn | 2523-3246 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Cybersecurity |
| spelling | doaj-art-09952e6fd3c34a6da9d5c420f5dae3df2025-08-20T03:21:03ZengSpringerOpenCybersecurity2523-32462025-06-018112410.1186/s42400-024-00341-6HP_FLAP: homomorphic and polymorphic federated learning aggregation of parameters frameworkMohammad Moshawrab0Mehdi Adda1Abdenour Bouzouane2Hussein Ibrahim3Ali Raad4Mathematics, Informatics and Engineering Department, University of Quebec at RimouskiMathematics, Informatics and Engineering Department, University of Quebec at RimouskiMathematics and Informatics Department, University of Quebec at ChicoutimiInstitut Technologique de Maintenance IndustrielleFaculty of Science and Arts, Islamic University of Lebanon, WardaniyehAbstract Protecting user privacy is essential in machine learning research, especially in the context of data collection. Federated learning (FL), which trains models across decentralized devices without sharing raw data, has emerged as a promising solution. However, FL is still vulnerable to security threats, including inference attacks, which have been underexplored in comparison to poisoning and backdoor attacks that have received more attention in existing research. To address these vulnerabilities, this paper proposes a novel aggregation framework called homomorphic and polymorphic federated learning aggregation of parameters (HP_FLAP). HP_FLAP integrates both homomorphic and polymorphic encryption to enhance the security and privacy of FL. Homomorphic encryption allows the server to perform aggregation on encrypted parameters without decrypting them, ensuring that sensitive information is protected during the aggregation process. Polymorphic encryption further strengthens security by using different encryption keys for each set of parameters, mitigating the risk of system-wide compromise in case a key is leaked. This dual encryption approach effectively counters inference attacks while maintaining robust protections against other security threats. The framework is evaluated using multiple models, including logistic regression, Gaussian Naive Bayes, Stochastic Gradient Descent, and Multi-Layer Perceptron, demonstrating HP_FLAP’s ability to enhance both security and privacy in FL environments.https://doi.org/10.1186/s42400-024-00341-6Federated machine learningAggregation algorithmsPolymorphic encryptionHomomorphic encryptionSecurityPrivacy |
| spellingShingle | Mohammad Moshawrab Mehdi Adda Abdenour Bouzouane Hussein Ibrahim Ali Raad HP_FLAP: homomorphic and polymorphic federated learning aggregation of parameters framework Cybersecurity Federated machine learning Aggregation algorithms Polymorphic encryption Homomorphic encryption Security Privacy |
| title | HP_FLAP: homomorphic and polymorphic federated learning aggregation of parameters framework |
| title_full | HP_FLAP: homomorphic and polymorphic federated learning aggregation of parameters framework |
| title_fullStr | HP_FLAP: homomorphic and polymorphic federated learning aggregation of parameters framework |
| title_full_unstemmed | HP_FLAP: homomorphic and polymorphic federated learning aggregation of parameters framework |
| title_short | HP_FLAP: homomorphic and polymorphic federated learning aggregation of parameters framework |
| title_sort | hp flap homomorphic and polymorphic federated learning aggregation of parameters framework |
| topic | Federated machine learning Aggregation algorithms Polymorphic encryption Homomorphic encryption Security Privacy |
| url | https://doi.org/10.1186/s42400-024-00341-6 |
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