Privacy protection query language PQL and system thereof

Abstract

The invention discloses a privacy protection query language PQL and a system thereof; the system comprises a PQL statement and a system, and the system comprises an analysis module, a query module anda noise adding module; the analysis module comprises a lexical analyzer and a syntactic analyzer; a user inputs a PQL statement according to a preset semantic grammar rule, and after receiving the PQL statement, an analysis module performs error check on the PQL statement through a lexical analyzer and sends a correct result to a grammar analyzer; performing grammar and semantic check on the PQLstatement by a grammar analyzer, and generating a mapping table and a parameter table according to a correct result; the query module encapsulates the mapping table into an SQL statement after receiving the mapping table, and verifies the encapsulated SQL statement; the noise adding module acquires a final query result of the query module and calculates noise adding sensitivity according to the parameter table; according to the invention, the security of data privacy is ensured, and the availability of the data is not influenced by the added noise.

Description

technical field [0001] The invention relates to the field of data security, in particular to a privacy protection query language PQL and a system thereof. Background technique [0002] In today's network environment, sharing and summarizing data is an important way of data reuse, but due to the high correlation between data, summarizing data has the risk of leaking privacy. Therefore, how to protect the privacy and security of aggregated data has become a major challenge for current data sharing. [0003] The differential privacy protection algorithm interferes with the calculation results with noise while ensuring certain data availability, so it has become the main privacy protection method at present. [0004] However, because the differential privacy algorithm is too complex to use, and the balance between noise interference and data availability is too professional, it cannot be directly applied to the database query results, which increases the learning cost of staff ...

AI Technical Summary

Problems solved by technology

[0006] The technical problem to be solved by the present invention is that there is a risk of leaking privacy in the aggregated data in the prior art, and the differential privacy algorithm is too complicated to be directly applied to the database query results, and it also increases the learning cost of the staff and the preparation of differential privacy programs costs and other issues

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