A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/10/6201 |
| _version_ | 1797601165401128960 |
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| author | Muhammad Asad Saima Shaukat Ehsan Javanmardi Jin Nakazato Manabu Tsukada |
| author_facet | Muhammad Asad Saima Shaukat Ehsan Javanmardi Jin Nakazato Manabu Tsukada |
| author_sort | Muhammad Asad |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T03:57:33Z |
| format | Article |
| id | doaj.art-48e1e5d34077405c94149054f62082d4 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T03:57:33Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-48e1e5d34077405c94149054f62082d42023-11-18T00:22:04ZengMDPI AGApplied Sciences2076-34172023-05-011310620110.3390/app13106201A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation SystemsMuhammad Asad0Saima Shaukat1Ehsan Javanmardi2Jin Nakazato3Manabu Tsukada4Graduate School of Information Science and Technology, Department of Creative Informatics, The University of Tokyo, Tokyo 113-8654, JapanGraduate School of Information Science and Technology, Department of Creative Informatics, The University of Tokyo, Tokyo 113-8654, JapanGraduate School of Information Science and Technology, Department of Creative Informatics, The University of Tokyo, Tokyo 113-8654, JapanGraduate School of Information Science and Technology, Department of Creative Informatics, The University of Tokyo, Tokyo 113-8654, JapanGraduate School of Information Science and Technology, Department of Creative Informatics, The University of Tokyo, Tokyo 113-8654, JapanBig 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.https://www.mdpi.com/2076-3417/13/10/6201federated recommendation systemsprivacy preservingbig datadata sharing |
| spellingShingle | Muhammad Asad Saima Shaukat Ehsan Javanmardi Jin Nakazato Manabu Tsukada A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems Applied Sciences federated recommendation systems privacy preserving big data data sharing |
| title | A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems |
| title_full | A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems |
| title_fullStr | A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems |
| title_full_unstemmed | A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems |
| title_short | A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems |
| title_sort | comprehensive survey on privacy preserving techniques in federated recommendation systems |
| topic | federated recommendation systems privacy preserving big data data sharing |
| url | https://www.mdpi.com/2076-3417/13/10/6201 |
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