ArticleProceedings. IEEE Symposium on Security and Privacy2023
Scalable and Privacy-Preserving Federated Principal Component Analysis.
Article in Proceedings. IEEE Symposium on Security and Privacy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Secure distributed multiple imputation enables missing data inference for private data proprietors.NPJ digital medicine · 2026Article
- Shechi: A Secure Distributed Computation Compiler Based on Multiparty Homomorphic Encryption.Proceedings. UNIX Security Symposium · 2025Article
- Secure and federated quantitative trait loci mapping with privateQTL.Cell genomics · 2025Article
- Secure discovery of genetic relatives across large-scale and distributed genomic data sets.Genome research · 2024Article
- Privacy-Enhancing Technologies in Biomedical Data Science.Annual review of biomedical data science · 2024Review
- Encrypted federated learning for secure decentralized collaboration in cancer image analysis.Medical image analysis · 2024Article
- sfkit: a web-based toolkit for secure and federated genomic analysis.Nucleic acids research · 2023Article
- Scalable and Privacy-Preserving Federated Principal Component Analysis.Proceedings. IEEE Symposium on Security and Privacy · 2023Article
- Secure Discovery of Genetic Relatives across Large-Scale and Distributed Genomic Datasets.Research in computational molecular biology : ... Annual International Conference, RECOMB ... : proceedings. RECOMB (Conference : 2005- )Article
Corrections and comments
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Authors and funding
9 authors.
Funding
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.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.