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CONSISTENCY FOR QUERIES IN PRIVACY-PRESERVING DATA ANALYTIC SYSTEMS

发明专利审中
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20权利要求 · 3 独立
§ Ⅰ

卷宗概要

发明人

Mark B. Cesar; Praveen K. Chaganlal; Yu Chen; Tin Yam Ho; Ryan M. Rogers; Adrian Rivera Cardoso; Subbu Subramaniam; Siyao Sun; Rahul Tandra; Xinlin Zhou

IPC 分类

G6F 21/62G6F 16/22

CPC 分类

G6F21/6245G6F16/2282G6F21/6227

Techniques for executing privacy-preserving aggregation queries on a database table include receiving a query, obtaining the true output, and generating deterministic pseudorandom noise based on the query and current database state. This noise is added to the true output to create a privacy-protected result. For subsequent queries, this process is repeated, potentially with updated state data, ensuring that privacy protection adapts to changes in the underlying data. The approach maintains consistency for repeated queries on unchanged data while providing fresh noise when data changes. It balances privacy protection with data utility, allowing for various query types while guarding against privacy attacks. The techniques include displaying the noisy output to a user interface. The techniques offer adaptive privacy protection, query flexibility, and efficient resource utilization, making them suitable for dynamic data environments requiring both privacy and analytical capabilities.

原文(中文)

Techniques for executing privacy-preserving aggregation queries on a database table include receiving a query, obtaining the true output, and generating deterministic pseudorandom noise based on the query and current database state. This noise is added to the true output to create a privacy-protected result. For subsequent queries, this process is repeated, potentially with updated state data, ensuring that privacy protection adapts to changes in the underlying data. The approach maintains consistency for repeated queries on unchanged data while providing fresh noise when data changes. It balances privacy protection with data utility, allowing for various query types while guarding against privacy attacks. The techniques include displaying the noisy output to a user interface. The techniques offer adaptive privacy protection, query flexibility, and efficient resource utilization, making them suitable for dynamic data environments requiring both privacy and analytical capabilities.