Data Sharing

FAQ

What is the difference between data sharing and data openness?

Data sharing typically refers to the exchange of data between specific entities (such as internal departments, partners, or alliance members) according to agreed-upon rules, with controlled access permissions and a relatively limited scope. Data openness, on the other hand, is oriented toward the broader public or specific groups, often led by governments or public institutions, involving non-sensitive, anonymized public data that emphasizes transparency and reuse. In simple terms, sharing is 'targeted authorization,' while openness is 'broad dissemination.'

What are the main challenges enterprises face in implementing data sharing?

Key challenges include: 1) Data silos: Inconsistent data formats and standards across different systems make integration difficult; 2) Security and privacy risks: Data leakage or misuse may occur during the sharing process; 3) Unclear rights and responsibilities: Data ownership, usage rights, and benefit rights are hard to define; 4) Technical barriers: Lack of a unified data sharing platform and API management capabilities; 5) Compliance pressure: Must meet regulatory requirements such as the Data Security Law and the Personal Information Protection Law, imposing high demands on data classification, grading, and anonymization.

What core features should a data sharing platform have?

A mature data sharing platform should typically include: 1) Data catalog and discovery: Supports metadata management, allowing users to quickly find and understand available data resources; 2) Data governance and quality: Provides tools for data cleaning, standardization, and quality monitoring; 3) Security and permission control: Supports fine-grained access control, data anonymization, encrypted transmission, and audit logs; 4) Data exchange and APIs: Offers standardized data interfaces (e.g., RESTful APIs) supporting real-time or batch data exchange; 5) Compliance and auditing: Built-in data classification and grading, privacy impact assessment, and compliance reporting functions.

How can data security be ensured in data sharing?

Ensuring data sharing security requires a multi-pronged approach: 1) Technical level: Use techniques such as data anonymization (dynamic/static), differential privacy, and federated learning to extract value from data without exposing raw data; 2) Management level: Establish strict data access approval processes, implement the principle of least privilege, and conduct regular security audits; 3) Legal level: Clarify responsibilities, data usage scope, and consequences of breaches through Data Sharing Agreements (DSAs); 4) Platform level: Deploy data security gateways to monitor abnormal access behavior and achieve end-to-end data lineage tracking.