mlsanityData to 14 Oct 2026

A Survey on Homomorphic Encryption Schemes: Theory and Implementation

arXiv 1704.03578 ↗First posted 12 Apr 2017cs.CRcited by 156

Not on the map. Why?

Abstract (arXiv)

Legacy encryption systems depend on sharing a key (public or private) among the peers involved in exchanging an encrypted message. However, this approach poses privacy concerns. Especially with popular cloud services, the control over the privacy of the sensitive data is lost. Even when the keys are not shared, the encrypted material is shared with a third party that does not necessarily need to access the content. Moreover, untrusted servers, providers, and cloud operators can keep identifying elements of users long after users end the relationship with the services. Indeed, Homomorphic Encryption (HE), a special kind of encryption scheme, can address these concerns as it allows any third party to operate on the encrypted data without decrypting it in advance. Although this extremely useful feature of the HE scheme has been known for over 30 years, the first plausible and achievable Fully Homomorphic Encryption (FHE) scheme, which allows any computable function to perform on the encrypted data, was introduced by Craig Gentry in 2009. Even though this was a major achievement, different implementations so far demonstrated that FHE still needs to be improved significantly to be practical on every platform. First, we present the basics of HE and the details of the well-known Partially Homomorphic Encryption (PHE) and Somewhat Homomorphic Encryption (SWHE), which are important pillars of achieving FHE. Then, the main FHE families, which have become the base for the other follow-up FHE schemes are presented. Furthermore, the implementations and recent improvements in Gentry-type FHE schemes are also surveyed. Finally, further research directions are discussed. This survey is intended to give a clear knowledge and foundation to researchers and practitioners interested in knowing, applying, as well as extending the state of the art HE, PHE, SWHE, and FHE systems.

Key facts. Cited by 156 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Apr 2017.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 19; 2022: 25; 2023: 27; 2024: 39; 2025: 30; 2026: 16.

156 papers.

Sort:

By area:

  1. 2024Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Securitycited by 260
  2. 2021Trustworthy AI: A Computational Perspectivecited by 93
  3. 2025TRiSM for Agentic AI: A Review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systemscited by 59
  4. 2021Privacy-Preserving Machine Learning: Methods, Challenges and Directionscited by 58
  5. 2021Survey: Leakage and Privacy at Inference Timecited by 44
  6. 2022A Survey on Heterogeneous Federated Learningcited by 42
  7. 2023A Survey on Vertical Federated Learning: From a Layered Perspectivecited by 30
  8. 2024Federated Unlearning: A Survey on Methods, Design Guidelines, and Evaluation Metricscited by 27
  9. 2024Security of AI Agentscited by 26
  10. 2024Overview of AI and Communication for 6G Network: Fundamentals, Challenges, and Future Research Opportunitiescited by 26
  11. 2022A Survey on Federated Recommendation Systemscited by 25
  12. 2021A Vertical Federated Learning Framework for Graph Convolutional Networkcited by 21
38 more
  1. 2023Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directionscited by 21
  2. 2025A Survey on Privacy Risks and Protection in Large Language Modelscited by 21
  3. 2025Shadow in the Cache: Unveiling and Mitigating Privacy Risks of KV-cache in LLM Inferencecited by 21
  4. 2024PointNCBW: Towards Dataset Ownership Verification for Point Clouds via Negative Clean-label Backdoor Watermarkcited by 20
  5. 2025Secure On-Device Video OOD Detection Without Backpropagationcited by 19
  6. 2021Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryptioncited by 18
  7. 2023Zero-Knowledge Proof-based Verifiable Decentralized Machine Learning in Communication Network: A Comprehensive Surveycited by 18
  8. 2026Intelligent AI Delegationcited by 18
  9. 2021Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challengescited by 17
  10. 2023Efficient and Secure Federated Learning for Financial Applicationscited by 17
  11. 2021Batch Label Inference and Replacement Attacks in Black-Boxed Vertical Federated Learningcited by 16
  12. 2022How to keep text private? A systematic review of deep learning methods for privacy-preserving natural language processingcited by 16
  13. 2023THC: Accelerating Distributed Deep Learning Using Tensor Homomorphic Compressioncited by 15
  14. 2024Federated Causal Discovery from Heterogeneous Datacited by 15
  15. 2022Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directionscited by 14
  16. 2022Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applicationscited by 14
  17. 2024Towards Efficient Communication and Secure Federated Recommendation System via Low-rank Trainingcited by 14
  18. 2024SLIP: Securing LLMs IP Using Weights Decompositioncited by 14
  19. 2025Top Ten Challenges Towards Agentic Neural Graph Databasescited by 13
  20. 2021A Quantitative Metric for Privacy Leakage in Federated Learningcited by 12
  21. 2022A Survey on Computationally Efficient Neural Architecture Searchcited by 12
  22. 2022Homomorphic Encryption and Federated Learning based Privacy-Preserving CNN Training: COVID-19 Detection Use-Casecited by 11
  23. 2022Federated learning: Applications, challenges and future directionscited by 11
  24. 2021FedMood: Federated Learning on Mobile Health Data for Mood Detectioncited by 10
  25. 2022Residue-based Label Protection Mechanisms in Vertical Logistic Regressioncited by 10
  26. 2023A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detectioncited by 10
  27. 2024Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimationcited by 9
  28. 2024A First Look At Efficient And Secure On-Device LLM Inference Against KV Leakagecited by 8
  29. 2022Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive Privacy Analysis and Beyondcited by 7
  30. 2022To Store or Not? Online Data Selection for Federated Learning with Limited Storagecited by 7
  31. 2023A Survey on Trustworthy Edge Intelligence: From Security and Reliability To Transparency and Sustainabilitycited by 7
  32. 2024Federated Knowledge Recycling: Privacy-Preserving Synthetic Data Sharingcited by 7
  33. 2025A Privacy-Preserving Federated Framework with Hybrid Quantum-Enhanced Learning for Financial Fraud Detectioncited by 7
  34. 2021Precision-Weighted Federated Learningcited by 6
  35. 2022Quant 4.0: Engineering Quantitative Investment with Automated, Explainable and Knowledge-driven Artificial Intelligencecited by 6
  36. 2023Implementing Responsible AI: Tensions and Trade-Offs Between Ethics Aspectscited by 6
  37. 2023Split Without a Leak: Reducing Privacy Leakage in Split Learningcited by 6
  38. 2024Delta: A Cloud-assisted Data Enrichment Framework for On-Device Continual Learningcited by 6

50 most cited of 156 All 156 (JSON)