Privacy-Preserving Computation_Unlocking Data Element Value While Ensuring Privacy Security

Privacy-Preserving Computation

It integrates secure multi-party computation, federated learning, homomorphic encryption, and differential privacy to provide one-stop data fusion computing capabilities like secure ID matching, joint analysis, federated machine learning, and private information retrieval, all without moving raw data. Pre-built operators for private set intersection, statistical analysis, and model training enhance usability, enabling secure data circulation and value realization.

It integrates secure multi-party computation, federated learning, homomorphic encryption, and differential privacy to provide one-stop data fusion computing capabilities. Pre-built operators enhance efficiency for secure data exchange and value release.

Industry Pain Points

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Difficulty in Ensuring Data Privacy

The platform enables secure data sharing without exposing plaintext data, using end-to-end encryption and opaque processing to effectively prevent data privacy leaks.

Lack of Trust in Data Collaboration

The platform separates data ownership, management rights, and usage rights, supporting cross-domain, multi-party data collaboration and joint modeling while protecting all parties' data IP and commercial interests.

Risks in Data Circulation

In sensitive sectors like finance and healthcare, the platform ensures data privacy and security while meeting compliance requirements for data application scenarios.

Value Proposition

Multi-layered Data Authentication & Protection

The platform supports mainstream privacy-preserving computation technologies, employs multi-factor authentication and channel protection for communications, and its data encryption fully complies with national confidentiality standards.

Codeless, Efficient Modeling

This visual fusion computing platform supports large-scale, codeless rule-based tagging and offers pre-built operators for visual modeling, significantly improving modeling efficiency.

Millisecond-level Big Data Performance

The platform is highly scalable, supports rapid deployment in hybrid or private cloud environments, and meets enterprise production requirements for high performance and stability with large datasets. It also delivers millisecond-level response times for high-concurrency private queries.

Multi-layered Data Authentication & Protection

The platform supports mainstream privacy-preserving computation technologies, employs multi-factor authentication and channel protection for communications, and its data encryption fully complies with national confidentiality standards.

Codeless, Efficient Modeling

This visual fusion computing platform supports large-scale, codeless rule-based tagging and offers pre-built operators for visual modeling, significantly improving modeling efficiency.

Millisecond-level Big Data Performance

The platform is highly scalable, supports rapid deployment in hybrid or private cloud environments, and meets enterprise production requirements for high performance and stability with large datasets. It also delivers millisecond-level response times for high-concurrency private queries.

Solution Architecture

Customer Cases

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Inclusive Finance - Enterprise Credit Rating

在服务某市数字金融信贷平台过程中，通过隐私计算平台服务实现社保、税务、市场监管、司法等数据汇集管理，对政务数据进行挖掘和应用，创新性设计出企业科技金融评分评级产品；金融机构通过隐匿查询服务，在不暴露自己查询的具体企业ID的前提下，获取企业ID对应的政务数据评分评级标签，有效提升了银行对中小微企业准入和风险的评估，为该市开展普惠金融服务实体经济的生态建设打开良好局面。

在服务某市数字金融信贷平台过程中，通过隐私计算平台服务实现社保、税务、市场监管、司法等数据汇集管理，对政务数据进行挖掘和应用，创新性设计出企业科技金融评分评级产品；金融机构通过隐匿查询服务，在不暴露自己查询的具体企业ID的前提下，获取企业ID对应的政务数据评分评级标签，有效提升了银行对中小微企业准入和风险的评估，为该市开展普惠金融服务实体经济的生态建设打开良好局面。