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

A privacy protection and data query technology, applied in the field of information security, can solve problems such as the inability to realize infinite queries of data sets, limit the number of user queries, and reduce data availability, so as to achieve the effects of improving usability, resisting collusion attacks, and ensuring accuracy

Active Publication Date: 2022-04-05
NANJING UNIV OF POSTS & TELECOMM
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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

Method used

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

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Embodiment approach

[0058] Waitakere is a semi-synthetic dataset generated from New Zealand's 2006 census grid dataset, with a total population of 186,471 distributed over 1,340 grid areas. We randomly place residents into each grid block and then divide the entire area Divide into 7,725 non-overlapping rectangles (154×113m2 in size) and count the population in each rectangle.

[0059] Step 1. Take the privacy budget ε=1, and take the basic query times k=10;

[0060] Step 2. Calculate the privacy budget based on the Poisson mechanism:

[0061]

[0062] Step 3. According to the query submitted by the user, Laplace adds random noise to the query result. In order to simplify the operation, we set the query set F={f|f to find the total number of people in the interval [456,459]}, that is, f 1 =f 2 =…=f n =...=f, Δf 1 =Δf 2 =…=Δf n =…=Δf=1, f 1 (D)=f 2 (D)=…=f n (D)=...=f(D)=131.

[0063] When the user submits the first query f 1 When the query result f 1 Add a random noise to (D) S...

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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...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F21/62G06F16/248
CPCG06F21/6227G06F21/6245
Inventor 杨庚唐海霞白云璐王璇
Owner NANJING UNIV OF POSTS & TELECOMM
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