Replace sensitive datasets with high-utility synthetic data—designed to preserve statistical characteristics while reducing re-identification risk and accelerating delivery.
珠江数据是上海珠水江科数据科技有限公司旗下的合成数据及其解决方案供应商。我们的团队致力于使用合成数据技术，为各个专业领域提供稳定、精确、一致的合成数据和专业全面的解决方案，降低数据的收集成本和使用门槛。
We collaborate with leading organizations to deliver synthetic data solutions that drive innovation and privacy.
Produce synthetic datasets that mirror structure and statistics of your source data while avoiding direct record copying.
Measure re-identification risk using practical attack simulations and distance-based leakage checks.
Quantify whether synthetic data supports downstream tasks (reports, forecasting, classification, etc.).
Build repeatable workflows for your domain—data schema, constraints, formats, and governance.
CSV/Parquet + schema + constraints (optional) + versioned outputs.
Utility metrics, distribution checks, correlation analysis, and task-based benchmarks.
Privacy risk summary + recommended usage policy and sharing rules.
构建可扩展的数据生产能力，而不是简单的数据增强工具。
通过对交易结构、风险标签与时间分布的联合建模，生成可控的替代数据环境，用于压力测试、欺诈识别与极端场景推演，而不触碰真实客户数据。
Keep your answers short and confident. Offer a demo or a sample report for deeper details.
Not exactly. Anonymization typically modifies real records, while synthetic data generates new records from learned patterns. In practice, you still need evaluation to quantify privacy risk and utility.
Often yes—especially for augmentation, class imbalance mitigation, and sharing data for validation. We recommend task-based utility tests to confirm performance for your specific model and target.
We use practical risk checks such as nearest-neighbor similarity, leakage signals, and scenario-based tests (aligned to your threat model). The output is summarized in a decision-friendly report.
We typically start with tabular data (most common in enterprises). Time-series and text can be supported depending on constraints, data size, and intended usage.
Explore our interactive demo to see how our synthetic data solution works.
Tell us your data type and intended usage. We’ll reply with a recommended approach and what artifacts you’ll receive.