Privacy Secure Email Search System Building Solution
Vespa's Streaming Search (Streaming Search) model is ideal for addressing privacy-sensitive scenarios, with key benefits:
- data isolation: No global indexes are constructed, and each user's data is handled independently
- Cost optimization: Reduce resource consumption by 20 times compared to traditional search
- real-time assurance: New data is available as soon as it arrives, no need to wait for batch indexing
Implementation Steps:
- Enable streaming search mode (key configuration example):
{
"schema": {
"document": { "mode": "streaming" },
"documenttype": "email"
}
} - Store data in slices by user ID to ensure physical isolation
- Strictly bound user authentication when developing search front-end
- For generic semantic understanding requirements (e.g. spam recognition), lightweight machine learning models can be deployed
Caveats:
- Streaming mode does not support cross-user data aggregation analysis
- It is recommended that the last 6 months of data be retained in streaming storage, and historical data be archived to object storage
- Monitor APIs to set access frequency limits to prevent brute-force cracking
The solution has been validated in real-world applications, with a search latency of <200ms for single-user 10 million email data, while meeting GDPR compliance requirements.
This answer comes from the articleVespa.ai: an open source platform for building efficient AI search and recommendation systemsThe
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