A privacy budget allocation and data publishing method and system for data query privacy protection

Abstract

The invention discloses a privacy budget allocation and data release method for data query privacy protection, which includes the following steps: Step 1: Setting privacy budget parameters: the data manager assigns the privacy budget of the given data according to the importance of the data, denoted as ε ; and set the number of basic queries on the data, denoted as k; Step 2: Calculate the privacy budget for each query; Step 3: According to the query f submitted by the user, get the sensitivity Δf of the query; combined with the privacy budget allocated to the query ε, apply the differential privacy protection algorithm to the query results, calculate the noise that needs to be added, and get the query results containing noise; Step 4: According to the query submitted by the user, return the query results containing noise, so that the privacy of the data is protected. The present invention not only provides privacy protection in the data publishing process, resists collusion attacks, but also ensures the accuracy of the first k queries, and will not cause too low data availability due to infinite allocation of privacy budgets.

Description

technical field [0001] The invention relates to a privacy budget allocation and data publishing method and system for data query privacy protection, and belongs to the technical field of information security. Background technique [0002] The deepening and popularization of information technology makes the collection, storage, release and analysis of data fast and convenient. Data mining technology can obtain valuable information from various published data, but at the same time it will also cause the leakage of personal information. As an effective privacy protection technology, differential privacy can ensure that personal information is not leaked while publishing valid data. . [0003] Differential privacy protection data publishing can be divided into two types according to different implementation scenarios, namely interactive data publishing and non-interactive data publishing. In a non-interactive scenario, the system applies a differential privacy algorithm to the...

AI Technical Summary

Problems solved by technology

The privacy budget ε represents the level of privacy protection. The smaller ε is, the higher the level of privacy protection is, but at the same time, more noise will be introduced, resulting in reduced data availability. Therefore, how to effectively allocate the privacy budget is a major challenge in differential privacy interactive scenarios.

[0004] The existing data publishing algorithms in interactive scenarios mainly study how to answer more queries with a given privacy budget under the condition of certain accuracy. Although these algorithms guarantee the availability of data to a certain extent, However, the number of queries for users is limited, and infinite queries for data sets cannot be realized

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