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Alimama Tech
Alimama Tech
Aug 20, 2025 · Information Security

How Private Information Retrieval Secures Data Queries in Modern Applications

Private Information Retrieval (PIR) is a core privacy-preserving technique that enables users to query databases without revealing their query content or access patterns, and its evolution—from early theoretical models to efficient, real‑world deployments across blockchain, cloud, and advertising—makes it essential for secure data collaboration.

Private Information Retrievaldata privacysecure multi-party computation
0 likes · 18 min read
How Private Information Retrieval Secures Data Queries in Modern Applications
Sohu Tech Products
Sohu Tech Products
Apr 2, 2025 · Artificial Intelligence

How SecretFlow Enables Privacy‑Preserving AI Model Training with Secure Multi‑Party Computation

SecretFlow is an open‑source privacy‑computing framework that lets multiple parties perform encrypted data analysis and AI model training without exposing raw data, offering unified MPC, federated learning and differential privacy features, with step‑by‑step Docker installation, Python examples, and a modular architecture for secure multi‑party computation.

AI model trainingData ProtectionPrivacy Computing
0 likes · 11 min read
How SecretFlow Enables Privacy‑Preserving AI Model Training with Secure Multi‑Party Computation
AntTech
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.

Homomorphic EncryptionPerformance OptimizationTransformer
0 likes · 15 min read
Nimbus: Secure and Efficient Two‑Party Inference for Transformers
Alimama Tech
Alimama Tech
Dec 21, 2023 · Information Security

Alibaba Mama Secure Data Hub: Cloud Architecture and Privacy-Preserving Advertising

Alibaba Mama’s Secure Data Hub delivers a privacy‑enhanced clean‑room for advertising by combining multi‑party computation, federated learning and differential privacy with encrypted operators on a Flink engine, offering cloud‑agnostic, scalable deployment that enables cross‑domain analytics while protecting raw user data and boosting ROI.

Federated LearningPrivacy Computingadvertising analytics
0 likes · 13 min read
Alibaba Mama Secure Data Hub: Cloud Architecture and Privacy-Preserving Advertising
AntTech
AntTech
Dec 12, 2023 · Information Security

Privacy Computing Case Study: Multi‑Party Secure Computation for Financial Risk Control by Jiangsu Bank and Ningbo Bank

This article presents a detailed case study of how Jiangsu Bank and Ningbo Bank leveraged Ant Group’s multi‑party secure computation platform and the “YinYu” privacy‑computing framework to build joint risk‑control models, enhancing data sharing, security, and approval rates for inclusive finance.

Inclusive FinanceMPCPrivacy Computing
0 likes · 9 min read
Privacy Computing Case Study: Multi‑Party Secure Computation for Financial Risk Control by Jiangsu Bank and Ningbo Bank
DataFunSummit
DataFunSummit
Nov 8, 2023 · Blockchain

Privacy Computing and Blockchain Integration for Secure Data Flow: Practices and Case Studies by WeBank

This article presents WeBank's exploration of privacy‑computing technologies combined with blockchain to enable secure, compliant data flow across enterprises, detailing the regulatory background, technical architectures, key use‑case scenarios such as anonymous query, privacy intersection, joint prediction and statistics, and real‑world deployments including the 2022 Big Data “Star River” benchmark cases.

BlockchainPrivacy ComputingWeBank
0 likes · 16 min read
Privacy Computing and Blockchain Integration for Secure Data Flow: Practices and Case Studies by WeBank
vivo Internet Technology
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 LearningHomomorphic Encryption
0 likes · 19 min read
Federated Learning: Privacy-Preserving Collaborative AI Across Data Islands
AntTech
AntTech
Jul 14, 2023 · Information Security

Open Privacy Computing Protocol SS‑LR: A Secret‑Sharing Based Logistic Regression Framework

The SS‑LR open protocol describes a secret‑sharing based logistic regression algorithm split into four layers—machine learning, secure operators, cryptographic protocol, and network transmission—enabling interoperable, privacy‑preserving data flow and secure multi‑party model training across institutions.

Privacy ComputingSS-LRdata security
0 likes · 7 min read
Open Privacy Computing Protocol SS‑LR: A Secret‑Sharing Based Logistic Regression Framework
AntTech
AntTech
Mar 29, 2023 · Information Security

Introducing SCQL: Secure Collaborative Query Language for Privacy-Preserving Data Analysis

SCQL, an open‑source Secure Collaborative Query Language built on multi‑party computation, enables SQL‑style privacy‑preserving data analysis for small‑to‑medium organizations by offering easy integration, fine‑grained column‑level access control, broad data‑source support, and optimized performance for collaborative queries.

Privacy ComputingSCQLSQL
0 likes · 6 min read
Introducing SCQL: Secure Collaborative Query Language for Privacy-Preserving Data Analysis
DataFunTalk
DataFunTalk
Feb 11, 2023 · Information Security

Challenges and Trends in Privacy Computing: Insights from Alibaba Cloud Datatrust Architect Liang Aiping

The interview with Alibaba Cloud Datatrust architect Liang Aiping reveals that privacy computing is still in its early stage, facing technical challenges in data sources, algorithm theory‑engineering gaps, system management interoperability, and product trade‑offs, while outlining future trends toward cross‑platform interoperability and distributed computing.

InteroperabilityPrivacy ComputingSystem Management
0 likes · 13 min read
Challenges and Trends in Privacy Computing: Insights from Alibaba Cloud Datatrust Architect Liang Aiping
DataFunSummit
DataFunSummit
Dec 18, 2022 · Information Security

Privacy Computing: Concepts, Product Architecture, and Medical Industry Applications by Ant Group

This article explains Ant Group's privacy computing framework, covering its fundamental concepts, layered product architecture, and four concrete use‑cases in the medical sector—including insurance, hospitals, health commissions, and medical device manufacturers—demonstrating how secure multi‑party computation and federated learning enable data collaboration while preserving privacy.

Ant GroupFederated LearningPrivacy Computing
0 likes · 12 min read
Privacy Computing: Concepts, Product Architecture, and Medical Industry Applications by Ant Group
Tencent Tech
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.

Homomorphic EncryptionPrivacy ComputingiDASH
0 likes · 5 min read
How Tencent’s Angel PowerFL Team Dominated iDASH with Homomorphic Encryption
DataFunSummit
DataFunSummit
Nov 5, 2022 · Information Security

TECC: A New Approach to Trusted Enclave Confidential Computing – Architecture, Security, and Performance

The article introduces TECC, a privacy‑computing framework that balances security and performance by using trusted execution environments, data secret‑sharing, lightweight cryptographic protocols, and Rust‑based implementation to enable near‑plaintext speed for secure multi‑party machine learning and data analysis.

Privacy ComputingRustTECC
0 likes · 10 min read
TECC: A New Approach to Trusted Enclave Confidential Computing – Architecture, Security, and Performance
DataFunSummit
DataFunSummit
Oct 8, 2022 · Information Security

Exploring Privacy Computing Technologies in the Open Financial Ecosystem

This article provides a comprehensive overview of privacy computing—covering its background, key techniques such as MPC, TEE, federated learning, homomorphic encryption, and differential privacy—and examines how these technologies are applied in open financial ecosystems, including use cases, challenges, and future directions.

BlockchainFederated LearningPrivacy Computing
0 likes · 25 min read
Exploring Privacy Computing Technologies in the Open Financial Ecosystem
DataFunSummit
DataFunSummit
Sep 19, 2022 · Artificial Intelligence

Privacy-Preserving Graph Learning and Recommendation: Techniques, Challenges, and Platform Overview

This article reviews the rapid development of privacy-preserving computation, explains its classification, discusses differential privacy, secure multi‑party computation, federated and split learning, and demonstrates how these techniques can be combined for graph learning and recommendation systems, culminating in a description of the JinZhiTa privacy‑computing platform.

Federated LearningPrivacy Computinggraph learning
0 likes · 20 min read
Privacy-Preserving Graph Learning and Recommendation: Techniques, Challenges, and Platform Overview
AntTech
AntTech
Jun 10, 2022 · Information Security

Trusted-Environment-based Cryptographic Computing (TECC): Patent Authorization and Performance Advances

Trusted-Environment-based Cryptographic Computing (TECC), an Ant Group innovation that combines cryptographic MPC/FL with full‑stack trusted execution, has secured a new patent and demonstrates 10‑ to 100‑fold speed improvements, enabling large‑scale encrypted data processing for privacy‑critical applications.

Privacy ComputingTECCcryptography
0 likes · 5 min read
Trusted-Environment-based Cryptographic Computing (TECC): Patent Authorization and Performance Advances
DataFunSummit
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.

Homomorphic Encryptionfinancial technologysecure multi-party computation
0 likes · 8 min read
Privacy-Preserving Computation: Innovations and Practices at Jiànxìn Jīnke
DataFunTalk
DataFunTalk
Mar 30, 2022 · Information Security

A Brief History of Cryptography and the Rise of Privacy Computing

This article surveys the evolution of cryptography from ancient Mesopotamian cipher sticks through classical ciphers, the Enigma machine, modern public‑key systems, and multi‑party computation, then explains the concept, current challenges, and future directions of privacy‑preserving computation technologies.

MPCcryptographyinformation security
0 likes · 19 min read
A Brief History of Cryptography and the Rise of Privacy Computing
DataFunTalk
DataFunTalk
Nov 30, 2021 · Information Security

Privacy-Preserving Computation for Multi‑Center Medical Research: Challenges, Techniques, and Yidu Cloud Solutions

This article explains the background and challenges of medical multi‑center research, introduces privacy‑preserving computation concepts such as data‑usable‑invisible techniques, multi‑party secure computation and federated learning, and details Yidu Cloud's architecture, solutions, and real‑world case studies.

Privacy ComputingYidu Cloudmedical data security
0 likes · 18 min read
Privacy-Preserving Computation for Multi‑Center Medical Research: Challenges, Techniques, and Yidu Cloud Solutions
AntTech
AntTech
Jun 25, 2021 · Information Security

2021 WAIC Privacy Computing Academic Exchange – Overview and Schedule

The 2021 World Artificial Intelligence Conference (WAIC) in Shanghai hosts a Privacy Computing Academic Exchange on July 8, featuring keynote speeches, panel discussions, paper and poster sessions on secure multi‑party computation, federated learning, and differential privacy, with calls for submissions and a detailed agenda.

AI ConferenceFederated LearningPrivacy Computing
0 likes · 6 min read
2021 WAIC Privacy Computing Academic Exchange – Overview and Schedule
Baidu Geek Talk
Baidu Geek Talk
Jun 2, 2021 · Industry Insights

How Federated Computing Secures Data While Powering AI: Core Techniques Explained

This article provides a concise technical overview of federated computing, covering its origins, core cryptographic methods such as MPC, garbled circuits, secret sharing, homomorphic encryption, and TEE, and explains how Baidu applies these technologies to enable privacy‑preserving AI in advertising and other industries.

AIFederated Learningdata privacy
0 likes · 12 min read
How Federated Computing Secures Data While Powering AI: Core Techniques Explained
DataFunTalk
DataFunTalk
Dec 18, 2020 · Artificial Intelligence

Federated Learning and Secure Multi‑Party Computation: Concepts, Security Challenges, and Practical Solutions

This article explains the evolution of federated learning, contrasts Google’s cross‑device horizontal approach with China’s cross‑silo vertical implementations, analyzes their security vulnerabilities, and demonstrates how secure multi‑party computation—including differential privacy, secure aggregation, and secret‑sharing techniques—can address these challenges while highlighting performance trade‑offs.

Federated LearningSecure Aggregationcross-silo
0 likes · 18 min read
Federated Learning and Secure Multi‑Party Computation: Concepts, Security Challenges, and Practical Solutions
DataFunSummit
DataFunSummit
Dec 16, 2020 · Artificial Intelligence

Federated Learning vs Secure Multi‑Party Computation: Concepts, Challenges, and Alibaba’s Solutions

This article explains the fundamentals of federated learning and secure multi‑party computation, compares their security and performance trade‑offs, discusses the differences between Google’s cross‑device FL and China’s cross‑silo FL, and presents Alibaba’s recent advances and practical solutions for privacy‑preserving collaborative modeling.

cross-silodifferential privacyinformation security
0 likes · 18 min read
Federated Learning vs Secure Multi‑Party Computation: Concepts, Challenges, and Alibaba’s Solutions
AntTech
AntTech
Jul 17, 2020 · Artificial Intelligence

Privacy-Preserving Shared Intelligence: Secure AI Techniques for Financial Services

This article outlines how Ant Group’s shared‑intelligence platform combines differential privacy, trusted execution environments, and secure multi‑party computation to enable privacy‑preserving AI and data collaboration across financial scenarios, addressing regulatory demands, technical challenges, and real‑world deployment cases.

data sharingdifferential privacyprivacy
0 likes · 19 min read
Privacy-Preserving Shared Intelligence: Secure AI Techniques for Financial Services
AntTech
AntTech
Jun 2, 2020 · Artificial Intelligence

Privacy-Preserving Machine Learning Workshop at CCS 2020 (Ant Shared Intelligence)

The Ant Shared Intelligence workshop at ACM CCS 2020 invites researchers and practitioners to submit short papers on privacy‑preserving machine learning techniques such as secure multi‑party computation, homomorphic encryption, differential privacy, federated learning, and related applications, with a submission deadline of June 21, 2020.

AI securityCCS2020Federated Learning
0 likes · 5 min read
Privacy-Preserving Machine Learning Workshop at CCS 2020 (Ant Shared Intelligence)
DataFunTalk
DataFunTalk
Sep 9, 2019 · Artificial Intelligence

Federated Learning: Background, Techniques, Applications, and the FATE Open‑Source Platform

This article presents a comprehensive overview of federated learning, covering its motivation, vertical and horizontal variants, privacy‑preserving technologies, real‑world use cases, and the industrial‑grade open‑source platform FATE that enables secure cross‑organization machine learning.

Data CollaborationFATEFederated Learning
0 likes · 16 min read
Federated Learning: Background, Techniques, Applications, and the FATE Open‑Source Platform
Alibaba Cloud Developer
Alibaba Cloud Developer
Aug 1, 2019 · Information Security

How MPC‑Based Key Management Eliminates Key Leakage Risks

This article explains the challenges of traditional key management, compares local and server‑side encryption approaches, and introduces a secure multi‑party computation (MPC) key management system that distributes key fragments across multiple servers to prevent key exposure even if some nodes are compromised.

MPCThreshold Cryptographyinformation security
0 likes · 9 min read
How MPC‑Based Key Management Eliminates Key Leakage Risks
Alibaba Cloud Developer
Alibaba Cloud Developer
Mar 12, 2019 · Information Security

Why Publicly Verifiable Covert MPC Is a Game‑Changer for Secure Computation

This article explains the fundamentals of secure multi‑party computation, walks through oblivious transfer and garbled circuits, and introduces a novel publicly verifiable covert (PVC) model that offers near‑half‑honest performance with strong cheating deterrence, highlighting its practical impact on data privacy.

Garbled CircuitsOblivious TransferPublic Verifiable Covert
0 likes · 12 min read
Why Publicly Verifiable Covert MPC Is a Game‑Changer for Secure Computation