1. Scalable and Privacy-Preserving Federated Principal Component Analysis
- Author
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Froelicher, David, Cho, Hyunghoon, Edupalli, Manaswitha, Sousa, Joao Sa, Bossuat, Jean-Philippe, Pyrgelis, Apostolos, Troncoso-Pastoriza, Juan R., Berger, Bonnie, and Hubaux, Jean-Pierre
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FOS: Computer and information sciences ,Computer Science - Cryptography and Security ,Cryptography and Security (cs.CR) - Abstract
Principal component analysis (PCA) is an essential algorithm for dimensionality reduction in many data science domains. We address the problem of performing a federated PCA on private data distributed among multiple data providers while ensuring data confidentiality. Our solution, SF-PCA, is an end-to-end secure system that preserves the confidentiality of both the original data and all intermediate results in a passive-adversary model with up to all-but-one colluding parties. SF-PCA jointly leverages multiparty homomorphic encryption, interactive protocols, and edge computing to efficiently interleave computations on local cleartext data with operations on collectively encrypted data. SF-PCA obtains results as accurate as non-secure centralized solutions, independently of the data distribution among the parties. It scales linearly or better with the dataset dimensions and with the number of data providers. SF-PCA is more precise than existing approaches that approximate the solution by combining local analysis results, and between 3x and 250x faster than privacy-preserving alternatives based solely on secure multiparty computation or homomorphic encryption. Our work demonstrates the practical applicability of secure and federated PCA on private distributed datasets., Comment: Published elsewhere. IEEE Symposium on Security and Privacy 2023 more...
- Published
- 2023
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