# Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications

arXiv: https://arxiv.org/abs/1801.03239 · HTML: https://mlsanity.com/paper/1801.03239/

## Key facts

- Cited by 67 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2018.

## Built on by (most cited first, 30 of 67)

- [CrypTen: Secure Multi-Party Computation Meets Machine Learning](https://mlsanity.com/paper/2109.00984/) (2021)
- [MPCFormer: fast, performant and private Transformer inference with MPC](https://mlsanity.com/paper/2211.01452/) (2022)
- [Privacy-Preserving Machine Learning: Methods, Challenges and Directions](https://mlsanity.com/paper/2108.04417/) (2021)
- [CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU](https://mlsanity.com/paper/2104.10949/) (2021)
- [SIRNN: A Math Library for Secure RNN Inference](https://mlsanity.com/paper/2105.04236/) (2021)
- [A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy](https://mlsanity.com/paper/2302.10637/) (2023)
- [LegoDNN: Block-grained Scaling of Deep Neural Networks for Mobile Vision](https://mlsanity.com/paper/2112.09852/) (2021)
- [Secure Quantized Training for Deep Learning](https://mlsanity.com/paper/2107.00501/) (2021)
- [Sisyphus: A Cautionary Tale of Using Low-Degree Polynomial Activations in Privacy-Preserving Deep Learning](https://mlsanity.com/paper/2107.12342/) (2021)
- [Privacy-Preserving Feature Selection with Secure Multiparty Computation](https://mlsanity.com/paper/2102.03517/) (2021)
- [Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration](https://mlsanity.com/paper/2205.01423/) (2022)
- [TransLinkGuard: Safeguarding Transformer Models Against Model Stealing in Edge Deployment](https://mlsanity.com/paper/2404.11121/) (2024)
- [Privacy-Preserving Video Classification with Convolutional Neural Networks](https://mlsanity.com/paper/2102.03513/) (2021)
- [A Survey on Intelligent Internet of Things: Applications, Security, Privacy, and Future Directions](https://mlsanity.com/paper/2406.03820/) (2024)
- [PRICURE: Privacy-Preserving Collaborative Inference in a Multi-Party Setting](https://mlsanity.com/paper/2102.09751/) (2021)
- [SecureFedYJ: a safe feature Gaussianization protocol for Federated Learning](https://mlsanity.com/paper/2210.01639/) (2022)
- [East: Efficient and Accurate Secure Transformer Framework for Inference](https://mlsanity.com/paper/2308.09923/) (2023)
- [Tetrad: Actively Secure 4PC for Secure Training and Inference](https://mlsanity.com/paper/2106.02850/) (2021)
- [Deploying Convolutional Networks on Untrusted Platforms Using 2D Holographic Reduced Representations](https://mlsanity.com/paper/2206.05893/) (2022)
- [QuMoS: A Framework for Preserving Security of Quantum Machine Learning Model](https://mlsanity.com/paper/2304.11511/) (2023)
- [MPC-Minimized Secure LLM Inference](https://mlsanity.com/paper/2408.03561/) (2024)
- [SAFELearning: Enable Backdoor Detectability In Federated Learning With Secure Aggregation](https://mlsanity.com/paper/2102.02402/) (2021)
- [Towards Secure and Practical Machine Learning via Secret Sharing and Random Permutation](https://mlsanity.com/paper/2108.07463/) (2021)
- [Volley Revolver: A Novel Matrix-Encoding Method for Privacy-Preserving Neural Networks (Inference)](https://mlsanity.com/paper/2201.12577/) (2022)
- [Training Large Scale Polynomial CNNs for E2E Inference over Homomorphic Encryption](https://mlsanity.com/paper/2304.14836/) (2023)
- [Attesting Distributional Properties of Training Data for Machine Learning](https://mlsanity.com/paper/2308.09552/) (2023)
- [PristiQ: A Co-Design Framework for Preserving Data Security of Quantum Learning in the Cloud](https://mlsanity.com/paper/2404.13475/) (2024)
- [State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey](https://mlsanity.com/paper/2404.16847/) (2024)
- [Privacy Inference Attacks and Defenses in Cloud-based Deep Neural Network: A Survey](https://mlsanity.com/paper/2105.06300/) (2021)
- [Adam in Private: Secure and Fast Training of Deep Neural Networks with Adaptive Moment Estimation](https://mlsanity.com/paper/2106.02203/) (2021)

## Abstract (arXiv)

We present Chameleon, a novel hybrid (mixed-protocol) framework for secure function evaluation (SFE) which enables two parties to jointly compute a function without disclosing their private inputs. Chameleon combines the best aspects of generic SFE protocols with the ones that are based upon additive secret sharing. In particular, the framework performs linear operations in the ring mathbbZ2l using additively secret shared values and nonlinear operations using Yao's Garbled Circuits or the Goldreich-Micali-Wigderson protocol. Chameleon departs from the common assumption of additive or linear secret sharing models where three or more parties need to communicate in the online phase: the framework allows two parties with private inputs to communicate in the online phase under the assumption of a third node generating correlated randomness in an offline phase. Almost all of the heavy cryptographic operations are precomputed in an offline phase which substantially reduces the communication overhead. Chameleon is both scalable and significantly more efficient than the ABY framework (NDSS'15) it is based on. Our framework supports signed fixed-point numbers. In particular, Chameleon's vector dot product of signed fixed-point numbers improves the efficiency of mining and classification of encrypted data for algorithms based upon heavy matrix multiplications. Our evaluation of Chameleon on a 5 layer convolutional deep neural network shows 133x and 4.2x faster executions than Microsoft CryptoNets (ICML'16) and MiniONN (CCS'17), respectively.