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기록

CONSISTENCY FOR QUERIES IN PRIVACY-PRESERVING DATA ANALYTIC SYSTEMS

발명심사 중
1조회수
20청구항 · 3 독립항
§ Ⅰ

개요

발명자

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 분류

G6F 21/62G6F 16/22

CPC 분류

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.

원문 (중국어)

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.