Abstract
The data such as features involved in recommendation systems often contain private information that can cause serious security problems if leaked to other participants in the system. At present, Federated Learning (FL) combined with encryption technology is a popular privacy preserving technology. However, the distributed computing of FL threatens the credibility of calculation results. Incorrect calculation results in the recommendation system can reduce the accuracy of the recommendation. In this paper, we design a verifiable and privacy-preserving framework for the federated recommendation system (VePriRec) to ensure the privacy of data and verifiability of calculation results. For three components involved in the system, we design three privacy-preserving protocols, including a secure similarity network construction protocol, a secure gradient descent protocol and a secure aggregation protocol. We conduct experiments on real-world datasets, the results demonstrate the effectiveness and efficiency of VePriRec.
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Notes
Information overload refers to the situation that social information exceeds the range that individuals or systems can accept, process or effectively use, resulting in failure.
The dataset from https://www.kaggle.com/mehdidag/black-friday#BlackFriday.csv.
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Funding
This research was supported by National Natural Science Foundation of China (Grant 62102212), Qingdao independent innovation major special project (Grant 21-1-2-21-XX) and K. C. Wong Education Foundation.
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Gao, F., Zhang, H., Lin, J. et al. A verifiable and privacy-preserving framework for federated recommendation system. J Ambient Intell Human Comput 14, 4273–4287 (2023). https://doi.org/10.1007/s12652-023-04531-x
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DOI: https://doi.org/10.1007/s12652-023-04531-x