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AntTech

Jun 13, 2024 · Information Security

Privacy Computing Technology Theory and Practice – Highlights from the National Data Bureau’s 2024 Data Lecture Series

The National Data Bureau’s June 12, 2024 Data Lecture Hall focused on privacy computing technology theory and practice, featuring expert talks, emphasizing secure yet usable data sharing to boost the digital economy, and gathering over 110 officials and specialists to discuss challenges and solutions.

Data SecurityData SharingDigital Economy

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Privacy Computing Technology Theory and Practice – Highlights from the National Data Bureau’s 2024 Data Lecture Series

DataFunSummit

Jul 7, 2023 · Information Security

Exploration and Reflections on Interoperability of Privacy Computing

This article presents a comprehensive overview of privacy‑computing interoperability in the financial sector, covering background challenges, the Beijing FinTech Alliance’s collaborative project structure, an industry‑level framework with technical and requirement layers, sub‑topic research results, achieved milestones, and future outlook for standardisation and ecosystem building.

Data SharingSecure Computationfinancial data security

0 likes · 11 min read

Exploration and Reflections on Interoperability of Privacy Computing

DataFunTalk

Jan 18, 2023 · Big Data

Five Major Trends Shaping Big Data, AI, and Cloud Industries in 2023

The article forecasts five key trends for 2023—including cloud cost optimization, multi‑cloud freedom, rapid AI model adoption, expanding data‑sharing ecosystems, and the convergence of data warehouses and lakes—highlighting how they will reshape the big data, artificial intelligence, and cloud landscapes.

AlluxioArtificial IntelligenceBig Data

0 likes · 6 min read

Five Major Trends Shaping Big Data, AI, and Cloud Industries in 2023

Architect

Mar 7, 2022 · Backend Development

Differences Between Microservices and Monolithic Architecture and Managing Data Sharing and Service Dependencies

The article explains how microservices differ from monolithic applications, discusses the challenges of data sharing and service dependencies, and offers practical approaches such as one‑way dependencies, aggregation services, HTTP API design, and using a shared Redis cache to improve performance and reliability.

Data SharingMicroservicesbackend architecture

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Differences Between Microservices and Monolithic Architecture and Managing Data Sharing and Service Dependencies

DataFunSummit

Jan 2, 2022 · Big Data

Data Governance Practices and Product Perspective at Beike Zhaofang

This article shares Beike Zhaofang’s two‑year experience building a data governance center, covering the purpose and scope of governance, how the company tailors the focus to its business and system characteristics, the middle‑platform construction approach, project goal management, product and operation rollout, and the challenges and solutions encountered.

Big DataData Sharingdata governance

0 likes · 14 min read

Data Governance Practices and Product Perspective at Beike Zhaofang

Spring Full-Stack Practical Cases

Jul 9, 2021 · Backend Development

How to Share Data Across Multiple JVMs in a Distributed Environment Using Zookeeper

This guide demonstrates how to use Zookeeper with Spring Boot to enable data sharing among multiple JVM processes in a distributed system, covering Maven dependencies, configuration files, and Java code for connection, node watching, and data change handling.

Data SharingDistributed SystemsJVM

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How to Share Data Across Multiple JVMs in a Distributed Environment Using Zookeeper

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 SharingFinancial AIdifferential privacy

0 likes · 19 min read

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

Selected Java Interview Questions

Jul 7, 2020 · Databases

Database Design Principles for Microservices: Independent Databases, Data Sharing Strategies, Backward‑Compatible Updates, and Saga Transactions

This article explains why each microservice should own its own database, outlines four data‑sharing approaches, describes how to perform backward‑compatible schema changes, and introduces the Saga pattern for cross‑service transactions, providing practical guidance for building robust microservice architectures.

Backward CompatibilityData SharingDatabase Design

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Database Design Principles for Microservices: Independent Databases, Data Sharing Strategies, Backward‑Compatible Updates, and Saga Transactions

AntTech

Jun 17, 2020 · Artificial Intelligence

Shared Intelligence: Combining Trusted Execution Environments and Multi‑Party Computation for Privacy‑Preserving Machine Learning at Ant Group

This article presents Ant Group's shared‑intelligence solution that integrates Trusted Execution Environments (TEE) and Multi‑Party Computation (MPC) to enable privacy‑preserving data sharing and large‑scale machine‑learning across untrusted parties, discusses industry progress, technical evolution, practical deployments, and future challenges.

Ant GroupData Sharingmulti-party computation

0 likes · 17 min read

Shared Intelligence: Combining Trusted Execution Environments and Multi‑Party Computation for Privacy‑Preserving Machine Learning at Ant Group

AntTech

Apr 24, 2020 · Artificial Intelligence

Interpretation of the Shared Intelligence Standard and Its Practical Applications

This article explains the classification, value, and detailed interpretation of the first national shared intelligence standard, describes its technical background and solutions such as Trusted Execution Environments and Secure Multi‑Party Computation, and showcases real‑world applications and industry recognitions.

AIAnt GroupData Sharing

0 likes · 13 min read

Interpretation of the Shared Intelligence Standard and Its Practical Applications

AntTech

Mar 4, 2020 · Artificial Intelligence

Shared Intelligence vs. Federated Learning: Ant Group’s Privacy‑Preserving Machine Learning Solutions for Finance

The article explains how Ant Group tackles the privacy‑usability trade‑off in AI by combining Trusted Execution Environments and Multi‑Party Computation into a “shared intelligence” framework, contrasting it with federated learning, detailing technical architectures, training workflows, and its impact on financial data sharing.

Data SharingFederated Learningfinancial technology

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Shared Intelligence vs. Federated Learning: Ant Group’s Privacy‑Preserving Machine Learning Solutions for Finance

AntTech

Aug 17, 2019 · Artificial Intelligence

Shared Machine Learning: Tackling Data Islands with Trusted Execution Environments and Multi‑Party Computation

The article explains how data islands and privacy concerns hinder AI development and describes Ant Financial's shared machine learning approach, which combines Trusted Execution Environments (TEE) and Multi‑Party Computation (MPC) to enable secure, privacy‑preserving data sharing and collaborative model training across organizations.

AIData Sharingant financial

0 likes · 15 min read

Shared Machine Learning: Tackling Data Islands with Trusted Execution Environments and Multi‑Party Computation

JD Tech

Oct 15, 2018 · Blockchain

Blockchain-Based Financial Risk Data Sharing and Token Settlement Framework

This article proposes a consortium blockchain solution for financial risk data sharing that records query transactions as token‑based entries, enables post‑audit and reversal mechanisms, defines a multi‑type token settlement system, and discusses institution onboarding and future evaluation capabilities.

BlockchainData Sharingconsortium

0 likes · 9 min read

Blockchain-Based Financial Risk Data Sharing and Token Settlement Framework

Tencent Cloud Developer

Oct 8, 2018 · Artificial Intelligence

How Blockchain Can Transform Artificial Intelligence

Blockchain’s decentralized, secure ledger can empower AI by enabling open data sharing, trustworthy model licensing, and auditable provenance, while AI contributes decision‑making and pattern recognition, together creating a decentralized operating system that enhances security, transparency, and scalability across sectors such as healthcare, finance, and beyond.

Artificial IntelligenceBlockchainData Sharing

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How Blockchain Can Transform Artificial Intelligence

JD Tech Talk

Aug 30, 2018 · Blockchain

Design of a Blockchain‑Based Financial Risk Data Sharing Alliance (Version 2.0)

The article analyzes the shortcomings of current seller‑driven financial risk data sharing, proposes a blockchain consortium architecture using Hyperledger Fabric, compares a 1.0 token‑based design with an improved 2.0 version that adds a service layer, secure multi‑party computation, and regulatory oversight to achieve fair pricing, data quality, security, and efficient transaction processing.

BlockchainData SharingHyperledger Fabric

0 likes · 13 min read

Design of a Blockchain‑Based Financial Risk Data Sharing Alliance (Version 2.0)

JD Tech Talk

Aug 30, 2018 · Blockchain

Design and Implementation of a Blockchain-Based Financial Risk Data Sharing and Token Settlement System

This article proposes a blockchain-based alliance for financial risk data sharing, detailing the post‑transaction accounting and audit mechanisms, token‑based settlement processes, various transaction types, and the scalability considerations for onboarding new institutions, aiming to improve data quality, pricing fairness, and ecosystem governance.

BlockchainData Sharingconsortium

0 likes · 10 min read

Design and Implementation of a Blockchain-Based Financial Risk Data Sharing and Token Settlement System

JD Tech

Aug 28, 2018 · Blockchain

Design of a Blockchain‑Based Financial Risk Data Sharing Alliance (Version 1.0 & 2.0)

The article describes the business scenario of financial risk data sharing, identifies pain points of current query‑based pricing models, and proposes two blockchain consortium designs—Version 1.0 and an improved Version 2.0—detailing token‑based pricing, smart‑contract accounting, service‑layer architecture, and deployment on Hyperledger Fabric.

BlockchainData SharingHyperledger Fabric

0 likes · 13 min read

Design of a Blockchain‑Based Financial Risk Data Sharing Alliance (Version 1.0 & 2.0)

Tencent Music Tech Team

Aug 11, 2016 · Mobile Development

Android Content Provider: Fundamentals, Implementation, and Usage

Android Content Providers are a core component that expose a uniform, permission‑controlled CRUD interface via content:// URIs, require manifest declaration, load lazily, support custom implementations, multi‑process instances, shared‑memory cursor transfers, observer notifications, and handle large‑data limits through batching.

AndroidContent ProviderData Sharing

0 likes · 26 min read

Android Content Provider: Fundamentals, Implementation, and Usage