SecureML: A System for Scalable Privacy-Preserving Machine Learning
Payman Mohassel low, Yupeng Zhang
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Citation edge evidence
- Federated Machine Learning→SecureML: A System for Scalable Privacy-Preserving Machine LearningLegacy edge — source detail unavailable
- Advances and Open Problems in Federated Learning→SecureML: A System for Scalable Privacy-Preserving Machine LearningLegacy edge — source detail unavailable
- Privacy-Preserving Deep Learning via Additively Homomorphic Encryption→SecureML: A System for Scalable Privacy-Preserving Machine LearningLegacy edge — source detail unavailable
- SecureML: A System for Scalable Privacy-Preserving Machine Learning→The Elements of Statistical Learning: Data Mining, Inference, and PredictionLegacy edge — source detail unavailable
- SecureML: A System for Scalable Privacy-Preserving Machine Learning→Public-Key Cryptosystems Based on Composite Degree Residuosity ClassesLegacy edge — source detail unavailable
- SecureML: A System for Scalable Privacy-Preserving Machine Learning→Deep Learning with Differential PrivacyLegacy edge — source detail unavailable
- SecureML: A System for Scalable Privacy-Preserving Machine Learning→Universally composable security: a new paradigm for cryptographic protocolsLegacy edge — source detail unavailable
- SecureML: A System for Scalable Privacy-Preserving Machine Learning→Protocols for secure computationsLegacy edge — source detail unavailable
- SecureML: A System for Scalable Privacy-Preserving Machine Learning→Privacy-Preserving Deep LearningLegacy edge — source detail unavailable
- SecureML: A System for Scalable Privacy-Preserving Machine Learning→Privacy-preserving data miningLegacy edge — source detail unavailable
- Deep Learning with Differential Privacy→Privacy-Preserving Deep LearningLegacy edge — source detail unavailable
- Deep Learning with Differential Privacy→Privacy-preserving data miningLegacy edge — source detail unavailable
- Privacy-Preserving Deep Learning via Additively Homomorphic Encryption→Privacy-Preserving Deep LearningLegacy edge — source detail unavailable
- Privacy-Preserving Deep Learning via Additively Homomorphic Encryption→Public-Key Cryptosystems Based on Composite Degree Residuosity ClassesLegacy edge — source detail unavailable
- Privacy-Preserving Deep Learning via Additively Homomorphic Encryption→Deep Learning with Differential PrivacyLegacy edge — source detail unavailable
- Federated Machine Learning→Privacy-Preserving Deep LearningLegacy edge — source detail unavailable
- Federated Machine Learning→Deep Learning with Differential PrivacyLegacy edge — source detail unavailable
- Federated Machine Learning→Privacy-Preserving Deep Learning via Additively Homomorphic EncryptionLegacy edge — source detail unavailable
- Federated Machine Learning→Privacy-preserving data miningLegacy edge — source detail unavailable
- Advances and Open Problems in Federated Learning→Deep Learning with Differential PrivacyLegacy edge — source detail unavailable
- Advances and Open Problems in Federated Learning→Federated Machine LearningLegacy edge — source detail unavailable
What this paper cites, inside the corpus
What cites it, inside the corpus
| Paper | Year | Cited |
|---|---|---|
| Federated Machine Learning | 2019 | 6,228 |
| Advances and Open Problems in Federated Learning | 2020 | 5,383 |
| Privacy-Preserving Deep Learning via Additively Homomorphic Encryption | 2017 | 1,665 |
Links
Topics
| Privacy-Preserving Technologies in Data | Computer Science |
| Cryptography and Data Security | Computer Science |
| Stochastic Gradient Optimization Techniques | Computer Science |
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