Practical Lossless Federated Singular Vector Decomposition over Billion-Scale Data
Di Chai, Leye Wang, Junxue Zhang, Yang Liu, Shuowei Cai, Kai Chen, Qiang Yang
2022Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining32 citationsDOI
Abstract
With the enactment of privacy-preserving regulations, e.g., GDPR, federated SVD is proposed to enable SVD-based applications over different data sources without revealing the original data. However, many SVD-based applications cannot be well supported by existing federated SVD solutions. The crux is that these solutions, adopting either differential privacy (DP) or homomorphic encryption (HE), suffer from accuracy loss caused by unremovable noise or degraded efficiency due to inflated data.
Topics & Concepts
Singular value decompositionHomomorphic encryptionLossless compressionComputer scienceDifferential privacyData miningEncryptionNoise (video)Scale (ratio)Differential (mechanical device)Computer securityTheoretical computer scienceInformation retrievalAlgorithmData compressionArtificial intelligenceEngineeringQuantum mechanicsPhysicsImage (mathematics)Aerospace engineeringPrivacy-Preserving Technologies in DataStochastic Gradient Optimization TechniquesCryptography and Data Security