homomorphic encryption

AntTech

Dec 6, 2024 · Artificial Intelligence

Nimbus: Secure and Efficient Two‑Party Inference for Transformers

The paper introduces Nimbus, a two‑party privacy‑preserving inference framework for Transformer models that leverages a client‑side outer‑product linear‑layer protocol and distribution‑aware polynomial approximations for non‑linear layers, achieving up to five‑fold speedups with negligible accuracy loss.

Machine LearningPerformance Optimizationhomomorphic encryption

0 likes · 15 min read

Nimbus: Secure and Efficient Two‑Party Inference for Transformers

AntTech

Nov 12, 2024 · Artificial Intelligence

Rhombus: Fast Homomorphic Matrix‑Vector Multiplication for Secure Two‑Party Inference – Paper Overview and Live Presentation

The article introduces the Rhombus protocol, a fast homomorphic matrix‑vector multiplication scheme that reduces ciphertext rotations and achieves O(1) communication complexity, enabling efficient privacy‑preserving two‑party inference, and announces a live streaming session where the first author will discuss its technical details and experimental results.

AI securityRhombus protocolhomomorphic encryption

0 likes · 3 min read

Rhombus: Fast Homomorphic Matrix‑Vector Multiplication for Secure Two‑Party Inference – Paper Overview and Live Presentation

AntTech

Oct 21, 2024 · Information Security

Second Homomorphic Encryption Computing Performance Optimization Forum – Hardware Accelerators

The second Homomorphic Encryption Computing Performance Optimization Forum, held on October 26 at the Summer Garden venue, gathers leading researchers to discuss hardware acceleration, cross‑disciplinary challenges, and recent advances in privacy‑preserving computation, presenting detailed abstracts and speaker bios for five technical sessions.

ConferencePerformance Optimizationcryptography

0 likes · 9 min read

Second Homomorphic Encryption Computing Performance Optimization Forum – Hardware Accelerators

vivo Internet Technology

Aug 23, 2023 · Artificial Intelligence

Federated Learning: Privacy-Preserving Collaborative AI Across Data Islands

Federated learning enables multiple organizations to jointly train high‑performing AI models without sharing raw data, using techniques such as secure multi‑party computation, differential privacy, and homomorphic encryption, thereby overcoming data‑island and regulatory constraints and supporting applications in mobile edge AI, finance, retail, and healthcare.

Data IslandFederated Learningartificial intelligence

0 likes · 19 min read

Federated Learning: Privacy-Preserving Collaborative AI Across Data Islands

Tencent Tech

Dec 9, 2022 · Artificial Intelligence

How Tencent’s Angel PowerFL Team Dominated iDASH with Homomorphic Encryption

Tencent’s Angel PowerFL team clinched the iDASH homomorphic encryption champion and secured top spots in MPC and SGX tracks, showcasing innovative privacy‑preserving machine‑learning models, CKKS‑based encrypted inference, and a scalable SGX clustering solution that push the boundaries of secure computation.

Machine Learninghomomorphic encryptioniDASH

0 likes · 5 min read

How Tencent’s Angel PowerFL Team Dominated iDASH with Homomorphic Encryption

AntTech

Jun 16, 2022 · Information Security

Privacy Computing: How Digital Technologies Address Privacy Protection Pain Points

This article examines the rapid growth of privacy computing in China, outlining policy and market drivers, explaining key technologies such as secure multiparty computation, trusted execution environments, homomorphic encryption, differential privacy and federated learning, and discussing the legal, technical and ecosystem challenges that hinder its wider adoption.

Data SecurityFederated LearningSecure Multiparty Computation

0 likes · 11 min read

Privacy Computing: How Digital Technologies Address Privacy Protection Pain Points

DataFunSummit

Jun 4, 2022 · Information Security

Privacy-Preserving Computation: Innovations and Practices at Jiànxìn Jīnke

This article outlines the rapid growth of data, the rising privacy risks, and Jiànxìn Jīnke's innovative platform for privacy‑preserving computation that integrates federated learning, secure multi‑party computation, homomorphic encryption, and industry‑level applications such as joint risk control and marketing models.

Data SecurityFederated Learningfinancial technology

0 likes · 8 min read

Privacy-Preserving Computation: Innovations and Practices at Jiànxìn Jīnke

DataFunTalk

Sep 20, 2021 · Artificial Intelligence

Integrating Fully Homomorphic Encryption with Federated Learning: Methods, Recent Advances, and Future Directions

This article reviews how fully homomorphic encryption can be combined with federated learning to enhance privacy protection, discusses recent FHE advancements, presents practical system designs such as POSEIDON, and outlines open research challenges and future research directions.

AIFHEFederated Learning

0 likes · 12 min read

Integrating Fully Homomorphic Encryption with Federated Learning: Methods, Recent Advances, and Future Directions

JD Tech Talk

Sep 30, 2020 · Artificial Intelligence

Secure Training Methods for Federated Transfer Learning

This article reviews the model structure of federated transfer learning and details three secure training approaches—additive homomorphic encryption, ABY, and SPDZ—combined with polynomial approximation, explaining their protocols, steps, and the role of federated transfer learning within the broader federated learning landscape.

Federated LearningSecure Computationhomomorphic encryption

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Secure Training Methods for Federated Transfer Learning

Tencent Cloud Developer

Sep 25, 2020 · Artificial Intelligence

Privacy-Preserving Federated Learning for Financial Risk Control Using Homomorphic Encryption

Tencent Shield‑Federated Computing enables banks to jointly train Gradient Boosted Decision Trees and Logistic Regression with external data owners by using homomorphic encryption to perform encrypted variable and split‑point searches, gradient aggregation, and model updates, delivering near‑centralized accuracy, up to 70 % speed gains, and full data confidentiality for financial risk control.

Federated LearningGradient Boosted TreesMachine Learning

0 likes · 15 min read

Privacy-Preserving Federated Learning for Financial Risk Control Using Homomorphic Encryption

JD Tech Talk

Jun 3, 2020 · Artificial Intelligence

JD Digital Science Unveils Fast Secure Federated Learning Framework and Two Industry‑First Techniques

JD Digital Science introduced its fast secure federated learning framework, highlighted two pioneering technologies—a kernel‑based nonlinear federated learning algorithm and a distributed fast homomorphic encryption method—both accepted at KDD 2020, and discussed their industrial applications, privacy benefits, and regulatory relevance.

AI InfrastructureFederated LearningKDD2020

0 likes · 6 min read

JD Digital Science Unveils Fast Secure Federated Learning Framework and Two Industry‑First Techniques